Template-Type: ReDIF-Article 1.0 Author-Name: Hakeem-Ur Rehman Author-X-Name-First: Hakeem-Ur Author-X-Name-Last: Rehman Author-Name: Guohua Wan Author-X-Name-First: Guohua Author-X-Name-Last: Wan Author-Name: Azmat Ullah Author-X-Name-First: Azmat Author-X-Name-Last: Ullah Author-Name: Badiea Shaukat Author-X-Name-First: Badiea Author-X-Name-Last: Shaukat Title: Individual and combination approaches to forecasting hierarchical time series with correlated data: an empirical study Abstract: Hierarchical time series arise in manufacturing and service industries when the products or services have the hierarchical structure, and top-down and bottom-up methods are commonly used to forecast the hierarchical time series. One of the critical factors that affect the performance of the two methods is the correlation between the data series. This study attempts to resolve the problem and shows that the top-down method performs better when data have high positive correlation compared to high negative correlation and combination of forecasting methods may be the best solution when there is no evidence of the correlationship. We conduct the computational experiments using 240 monthly data series from the ‘Industrial’ category of the M3-Competition and test twelve combination methods for the hierarchical data series. The results show that the regression-based, VAR-COV and the Rank-based methods perform better compared to the other methods. Journal: Journal of Management Analytics Pages: 231-249 Issue: 3 Volume: 6 Year: 2019 Month: 7 X-DOI: 10.1080/23270012.2019.1629342 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1629342 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:3:p:231-249 Template-Type: ReDIF-Article 1.0 Author-Name: Jianrong Hou Author-X-Name-First: Jianrong Author-X-Name-Last: Hou Author-Name: Xiaofeng Zhao Author-X-Name-First: Xiaofeng Author-X-Name-Last: Zhao Author-Name: Jiahao Zheng Author-X-Name-First: Jiahao Author-X-Name-Last: Zheng Title: The impact of consistency between the emotional feature of advertising music and brand personality on brand experience Abstract: Music in advertising plays a crucial role in making the audience feel beyond the multi-level visual experience. The intrinsic link between brand publicity and advertising music has long been a puzzle. This paper discusses the impact of the consistency between the emotional characteristics of music and brand personality on brand experience and expands the discussion to brand experience under market competition. We use the examples of Canon and Apple for our study. The results shows that: (1) the higher the degree of consistency between the emotional experience from music and brand personality, the greater the positive effect on brand experience; (2) this positive effect is not as significant for functional brands as it is for representative brands; (3) the consistency between the emotional experience from music and brand personality has a greater impact on brand experience for representative brands than functional brands. The results provide practical guidance for branding campaigns. Journal: Journal of Management Analytics Pages: 250-268 Issue: 3 Volume: 6 Year: 2019 Month: 7 X-DOI: 10.1080/23270012.2019.1613684 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1613684 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:3:p:250-268 Template-Type: ReDIF-Article 1.0 Author-Name: Udayan Chanda Author-X-Name-First: Udayan Author-X-Name-Last: Chanda Author-Name: Alok Kumar Author-X-Name-First: Alok Author-X-Name-Last: Kumar Title: Optimal ordering policy for short life-cycle products under credit financing with dynamic adoption in supply chain Abstract: Traditional inventory models are mostly ignorant of the life cycle dynamics of a technology product; hence, they often fail to identify different dimensions of inventory research. This paper attempts to investigate the relationship between adoption behavior of customers using life cycle dynamics and associated trade credit policies in order to optimize the total inventory cost. The demand model used in this paper treats sales as a function of awareness diffusion and adoption. Awareness is considered as a function of feedback effects from users/customers. Retailer’s optimal strategies for short life cycle product under credit financing were determined analytically. Finally, numerical examples have been used to support the theoretical results. Theoretical results have further been used to gain some managerial insights. Journal: Journal of Management Analytics Pages: 269-301 Issue: 3 Volume: 6 Year: 2019 Month: 7 X-DOI: 10.1080/23270012.2019.1614488 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1614488 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:3:p:269-301 Template-Type: ReDIF-Article 1.0 Author-Name: R. Sundararajan Author-X-Name-First: R. Author-X-Name-Last: Sundararajan Author-Name: M. Prabha Author-X-Name-First: M. Author-X-Name-Last: Prabha Author-Name: R. Jaya Author-X-Name-First: R. Author-X-Name-Last: Jaya Title: An inventory model for non-instantaneous deteriorating items with multivariate demand and backlogging under inflation Abstract: In this paper, a deterministic inventory model for non-instantaneous deteriorating items with price- and time-dependent demand with inflation is developed. The demand is continuous and differentiable function of price and time. Shortages are allowed and the unsatisfied demand is partially backlogged at a negative exponential rate with the waiting time. The objective is to find the optimal replenishment cycle such that present value of total profit is maximized, for any given selling price. We then provide a simple algorithm to find the optimal selling price and replenishment schedule for the proposed model that can be easily implemented by practitioners. Comparisons of the present model with various cases are presented as the special case. Numerical examples are used to illustrate the theoretical results and the sensitivity analysis with respect to major parameters on the optimal solutions is also performed. Journal: Journal of Management Analytics Pages: 302-322 Issue: 3 Volume: 6 Year: 2019 Month: 7 X-DOI: 10.1080/23270012.2019.1650671 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1650671 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:3:p:302-322 Template-Type: ReDIF-Article 1.0 Author-Name: Neha Bansal Author-X-Name-First: Neha Author-X-Name-Last: Bansal Author-Name: Arun Sharma Author-X-Name-First: Arun Author-X-Name-Last: Sharma Author-Name: R. K. Singh Author-X-Name-First: R. K. Author-X-Name-Last: Singh Title: Fuzzy AHP approach for legal judgement summarization Abstract: Legal documents are generally big and complex documents because of specific vocabulary, semantics and structure. One of the major challenges in legal processing systems is to generate summary of legal judgements. Till date, in most of the legal systems, the summary of judgements is produced manually by legal experts which are then used by Lawyers, Judges and other legal professionals. The manual process of summarization is very inefficient and time-consuming. Automatic text summarization (ATS) is the process of reducing the content of a textual document, while retaining the core description of text through the use of appropriate tool. The present work proposes a novel Fuzzy Analytical Hierarchical process (FAHP) based feature weighting scheme which helps in producing an efficient and effective summary of legal judgement. Model is applied on a number of legal judgements taken from Indian IT Act. Validation of the model is done using ROUGE (Recall-Oriented Understudy for Gisting Evaluation) tool with recall, precision, and f-measure as performance measures. The generated summaries are further assessed by legal experts and are found to be more promising than the summaries generated by traditional approaches. Journal: Journal of Management Analytics Pages: 323-340 Issue: 3 Volume: 6 Year: 2019 Month: 7 X-DOI: 10.1080/23270012.2019.1655672 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1655672 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:3:p:323-340 Template-Type: ReDIF-Article 1.0 Author-Name: Huiquan Li Author-X-Name-First: Huiquan Author-X-Name-Last: Li Author-Name: Shiping Mao Author-X-Name-First: Shiping Author-X-Name-Last: Mao Title: Incentive equilibrium strategies of transboundary industrial pollution control under emission permit trading Abstract: In this paper, we investigate the incentive equilibrium strategies of two neighboring regions facing transboundary industrial pollution under abatement investment and emission permits trading in a differential game setting. Our paper can be viewed as an extension of the work of Yeung [2007. Dynamically consistent cooperative solution in a differential game of transboundary industrial pollution. Journal of Optimization Theory and Applications, 134, 143–160] in the context of the transboundary industrial pollution. Compared with the work of Yeung [2007. Dynamically consistent cooperative solution in a differential game of transboundary industrial pollution. Journal of Optimization Theory and Applications, 134, 143–160], our research significant features (i) introduce the emission permits trading into the transboundary industrial pollution control;(ii) take into account the pollution abatement investment; (iii) examine the incentive equilibrium strategies of transboundary industrial pollution control; and (iv) design an allocation mechanism for regions’ cooperative profits. Furthermore, we illustrate the results of the paper with a numerical example. The utility of this paper is how to make incentive equilibrium strategies in a situation where the neighboring regions facing transboundary industrial pollution under abatement investment and emission permits trading in a differential game setting. Journal: Journal of Management Analytics Pages: 107-134 Issue: 2 Volume: 6 Year: 2019 Month: 4 X-DOI: 10.1080/23270012.2019.1595187 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1595187 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:2:p:107-134 Template-Type: ReDIF-Article 1.0 Author-Name: Azmat Ullah Author-X-Name-First: Azmat Author-X-Name-Last: Ullah Author-Name: Wei Jiang Author-X-Name-First: Wei Author-X-Name-Last: Jiang Title: Optimal periodic replacement policy for a warranted product subject to multi modes failure process Abstract: A complex product subjects to multiple failure modes such as minor and catastrophic failure with some probability. This paper investigates the effects of minor failure and catastrophic failure on the periodic replacement policy for a complex product supported by a warranty period. Cost models are developed and the expected optimal replacement policies are developed analytically such that long run expected life-cycle cost rate is minimized. Structural properties of the optimal replacement policies are derived for a product which fails with multiple failure modes and the failure rate is an increasing function of time. Finally, a numerical experiment is performed to show the important features of our study. Journal: Journal of Management Analytics Pages: 154-172 Issue: 2 Volume: 6 Year: 2019 Month: 4 X-DOI: 10.1080/23270012.2019.1596846 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1596846 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:2:p:154-172 Template-Type: ReDIF-Article 1.0 Author-Name: Rafael Voltolini Author-X-Name-First: Rafael Author-X-Name-Last: Voltolini Author-Name: Kaio Vasconcelos Author-X-Name-First: Kaio Author-X-Name-Last: Vasconcelos Author-Name: Milton Borsato Author-X-Name-First: Milton Author-X-Name-Last: Borsato Author-Name: Margherita Peruzzini Author-X-Name-First: Margherita Author-X-Name-Last: Peruzzini Title: Product development cost estimation through ontological models – a literature review Abstract: The early stages of product development are characterized by uncertainties. Designers must deal with challenges that arise unexpectedly in an agile and responsive manner. Expert information systems based on ontological models are a promising approach to capture knowledge and rationale of domain specialists, either for decision making or knowledge reuse. The present study presents a bibliometric analysis on the use of ontologies in product development for cost estimation. It identifies trends and research opportunities that can orient future works. From a general search in scientific databases, 31 articles were found and selected based on criteria established using the Proknow-C method. Results indicate that there are several possibilities for solutions using ontological and hybrid, transdisciplinary approaches. Using intelligent systems is not only promising but is also challenging as a new and real transdisciplinary research area of interest. Journal: Journal of Management Analytics Pages: 209-229 Issue: 2 Volume: 6 Year: 2019 Month: 4 X-DOI: 10.1080/23270012.2019.1598899 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1598899 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:2:p:209-229 Template-Type: ReDIF-Article 1.0 Author-Name: Mahamaya Mohanty Author-X-Name-First: Mahamaya Author-X-Name-Last: Mohanty Author-Name: Ravi Shankar Author-X-Name-First: Ravi Author-X-Name-Last: Shankar Title: A hierarchical analytical model for performance management of integrated logistics Abstract: In this paper, a holistic hierarchical analytical model is proposed to assess the performance of enablers in an integrated logistics system. Due to the ambiguous and complex environment, various refinements are needed to assess enablers and prioritize for the criteria such as economic, operational, and environment. The proposed hierarchical model is developed by a systematic approach that includes fuzzy analytical hierarchy process (FAHP), triangular fuzzy numbers (TFN), an evidential reasoning algorithm (ERA), and expected utility theory (EUT). The FAHP is used to analyze and obtain the weights of the criteria by considering the expert’s opinions. ERA is used to synthesize the enablers based on the selected criteria. These enablers are represented using subjective assessment along with a set of evaluation grades for a qualitative attribute. EUT helps in obtaining crisp values of enablers for their performance estimation. With these set of methodologies, a hierarchical model is proposed that prevent low flexibility and inadequate appropriateness of the proposed model. Further, the model helps in scenario generation for the logistics professionals who are facing various problems in integrating logistics and incorporating sustainability due to lack of appropriate methodologies and evaluation techniques. Finally, sensitivity analysis is used for overall model validation. Journal: Journal of Management Analytics Pages: 173-208 Issue: 2 Volume: 6 Year: 2019 Month: 4 X-DOI: 10.1080/23270012.2019.1608326 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1608326 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:2:p:173-208 Template-Type: ReDIF-Article 1.0 Author-Name: Guanmei Liu Author-X-Name-First: Guanmei Author-X-Name-Last: Liu Author-Name: Ruisi Jiang Author-X-Name-First: Ruisi Author-X-Name-Last: Jiang Author-Name: Xiaofeng Shao Author-X-Name-First: Xiaofeng Author-X-Name-Last: Shao Title: Coordinating contingent assistance of lateral suppliers under disruption Abstract: This paper focuses on coordinating contingent assistance between two lateral suppliers controlled by a central firm, when one supplier is exposed to supply disruption. By comparing two scenarios where the central firm can/cannot coordinate contingent assistance, we find the coordination of contingent assistance is more efficient, thus the central firm should do that. In the scenario without coordination, if the holding cost of the disrupted supplier is low, while the opportunity cost of the reliable supplier is high relatively, allowing the reliable supplier to hold decision power of assistance price can generate more assistance quantity for the disrupted supplier and bring more profits for the central firm. However, if the holding cost is high, and the opportunity cost is low relatively, the disrupted supplier can receive more assistance quantity, and the central firm can get more profits, by letting the disrupted supplier have the power to decide assistance price. Journal: Journal of Management Analytics Pages: 135-153 Issue: 2 Volume: 6 Year: 2019 Month: 4 X-DOI: 10.1080/23270012.2019.1608327 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1608327 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:2:p:135-153 Template-Type: ReDIF-Article 1.0 Author-Name: Marina V. Sokolova Author-X-Name-First: Marina V. Author-X-Name-Last: Sokolova Author-Name: Francisco J. Gómez Author-X-Name-First: Francisco J. Author-X-Name-Last: Gómez Author-Name: Larisa N. Borisoglebskaya Author-X-Name-First: Larisa N. Author-X-Name-Last: Borisoglebskaya Title: Migration from an SQL to a hybrid SQL/NoSQL data model Abstract: The paper presents a redesign of a database management system for a retail business company. Initially, based on a traditional data model, it is migrated to a hybrid model which combines both SQL and NoSQL databases. This approach adds flexibility, mobility, and efficiency to the data management system. The NoSQL database uses an ontology as a data schema, which we describe in this study. The NoSQL database is consulted using the SPARQL query language, and some examples of the queries are detailed in the paper. The architecture of the system and its functionality are discussed. Journal: Journal of Management Analytics Pages: 1-11 Issue: 1 Volume: 7 Year: 2020 Month: 1 X-DOI: 10.1080/23270012.2019.1700401 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1700401 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:1:p:1-11 Template-Type: ReDIF-Article 1.0 Author-Name: Stan Lipovetsky Author-X-Name-First: Stan Author-X-Name-Last: Lipovetsky Title: Express analysis for prioritization: Best–Worst Scaling alteration to System 1 Abstract: The work considers modification of the Best–Worst Scaling (BWS) to the so-called System 1 (S1) approach. S1 was described by D. Kahneman as a spontaneous and automatic reaction by an unconscious way in which human decision-makers choose among multiple alternatives. Application of S1 can be seen as a simplified BWS for data eliciting and express analysis of prioritization between many compared items. In S1, a respondent picks the items with which she feels “happy”, and those can be one, several, all, or none items in a task. Estimation of utilities is performed by multinomial-logit modeling with different optimization criteria which produce parameters of the models and choice probabilities of the items. Numerical examples by marketing research data are encouraging and demonstrating that spontaneous choice decisions can make S1 approach very fast, efficient, and convenient for express analysis of items prioritization, especially for big data. Journal: Journal of Management Analytics Pages: 12-27 Issue: 1 Volume: 7 Year: 2020 Month: 1 X-DOI: 10.1080/23270012.2019.1702112 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1702112 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:1:p:12-27 Template-Type: ReDIF-Article 1.0 Author-Name: Jianrong Hou Author-X-Name-First: Jianrong Author-X-Name-Last: Hou Author-Name: Xiaofeng Zhao Author-X-Name-First: Xiaofeng Author-X-Name-Last: Zhao Title: Using a priority queuing approach to improve emergency department performance Abstract: Emergency department over-crowding has been a growing problem throughout the world. This paper presents a practical approach to estimate the waiting times for multi-class patients and apply the approach to reduce the waiting time for high priority patients. Patient flows with different levels of acuity are formulated based on the priority queue models. It derives explicit expressions of the wait time for the Markov queue and uses the concept of isomorphism to approximate the wait time in the general queue. Numerical results with simulation experiments are reported to display the accuracy of the approach. A case study from an emergency department indicates that the proposed approach can efficiently prioritize patient flows in decreasing waiting times. The queuing models have two features. First, the approximation applies to the general priority queues and reduces to the exact results of the Markov priority queue. Second, the models requires no iterative algorithm. Journal: Journal of Management Analytics Pages: 28-43 Issue: 1 Volume: 7 Year: 2020 Month: 1 X-DOI: 10.1080/23270012.2019.1691945 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1691945 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:1:p:28-43 Template-Type: ReDIF-Article 1.0 Author-Name: Ajoy Kumar Maiti Author-X-Name-First: Ajoy Kumar Author-X-Name-Last: Maiti Title: Multi-item fuzzy inventory model for deteriorating items in multi-outlet under single management Abstract: Multi-item inventory model with stock-dependent demand is developed in fuzzy environment. Items are deteriorated in constant rate and are sold from different outlets in the city under single management. Due to the impreciseness of different parameters, objectives as well as constraints are imprecise in nature. As optimization of fuzzy objectives as well as fuzzy constraints are not well defined, the model is formulated as a multi-objective chance constrained programming problem where optimistic/pessimistic return of the objectives with some degree of possibility/necessity are optimized and constraints are satisfied with some degree of necessity. The model is solved via Multi-Objective Genetic Algorithm (MOGA) when crisp equivalent of the problem is available. In other cases, fuzzy simulation process is proposed to check the constraints as well as to determine the optimistic/pessimistic return of the objectives. The model is illustrated with some numerical examples. Journal: Journal of Management Analytics Pages: 44-68 Issue: 1 Volume: 7 Year: 2020 Month: 1 X-DOI: 10.1080/23270012.2019.1699873 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1699873 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:1:p:44-68 Template-Type: ReDIF-Article 1.0 Author-Name: Mohammad Mahdi Movahedisaveji Author-X-Name-First: Mohammad Mahdi Author-X-Name-Last: Movahedisaveji Author-Name: Badiea Shaukat Author-X-Name-First: Badiea Author-X-Name-Last: Shaukat Title: Mediating role of brand app trust in the relationship between antecedents and purchase intentions-Iranian B2C mobile apps Abstract: This study investigates the mediating role of brand app trust in the relationship between brand app antecedents and purchase intentions via a brand mobile app. It explores the mediating potential of brand and platform trust in this relationship between antecedents and purchase intentions and also highlights which brand antecedents are useful for directly or indirectly establishing trust towards purchasing via a mobile app in the Iranian mobile commerce market. This study uses survey data collected through an online questionnaire form and applies the partial least squares method to extract its findings. All of the estimation results are found to be unbiased and robust. The results show that brand app trust plays a mediating role in the relationship between the brand app antecedents of word-of-mouth recommendation, subjective norms, perceived image (social antecedents), mobile computing self-efficacy (consumer-based antecedent), and perceived ease of use (system antecedent) and purchase intentions via a brand mobile app in Iran. Journal: Journal of Management Analytics Pages: 69-104 Issue: 1 Volume: 7 Year: 2020 Month: 1 X-DOI: 10.1080/23270012.2019.1699874 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1699874 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:1:p:69-104 Template-Type: ReDIF-Article 1.0 Author-Name: Udayan Chanda Author-X-Name-First: Udayan Author-X-Name-Last: Chanda Author-Name: Praveen Goyal Author-X-Name-First: Praveen Author-X-Name-Last: Goyal Title: A Bayesian network model on the interlinkage between Socially Responsible HRM, employee satisfaction, employee commitment and organizational performance Abstract: In recent years several studies have been made to understand the impact of Socially Responsible HRM practices on Organizational Performance. Employee progress, community and environment play an important role in the sustainable growth of an organization. Thus, organizations are always looking for the ways to improve the employee satisfaction vis-a-vis commitment to improve the performance. Recent studies have shown that as employees are important stakeholder, hence formulating proper Socially Responsible HRM practices may help organization to better the returns on assets. The main objective of the study is to identify the relationship among various dimensions of Socially Responsible HRM practices with dimensions of employee satisfaction, employee commitment and organizational performance for Indian manufacturing sector by using Bayesian Network approach. Results of the study establish the relationship between dimensions of Socially Responsible HRM and Organizational Performance. Journal: Journal of Management Analytics Pages: 105-138 Issue: 1 Volume: 7 Year: 2020 Month: 1 X-DOI: 10.1080/23270012.2019.1650670 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1650670 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:1:p:105-138 Template-Type: ReDIF-Article 1.0 Author-Name: Michael Haenlein Author-X-Name-First: Michael Author-X-Name-Last: Haenlein Author-Name: Andreas Kaplan Author-X-Name-First: Andreas Author-X-Name-Last: Kaplan Author-Name: Chee-Wee Tan Author-X-Name-First: Chee-Wee Author-X-Name-Last: Tan Author-Name: Pengzhu Zhang Author-X-Name-First: Pengzhu Author-X-Name-Last: Zhang Title: Artificial intelligence (AI) and management analytics Journal: Journal of Management Analytics Pages: 341-343 Issue: 4 Volume: 6 Year: 2019 Month: 10 X-DOI: 10.1080/23270012.2019.1699876 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1699876 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:4:p:341-343 Template-Type: ReDIF-Article 1.0 Author-Name: Grzegorz Mazurek Author-X-Name-First: Grzegorz Author-X-Name-Last: Mazurek Author-Name: Karolina Małagocka Author-X-Name-First: Karolina Author-X-Name-Last: Małagocka Title: Perception of privacy and data protection in the context of the development of artificial intelligence Abstract: Customer privacy perception and the principles of its regulatory protection determine how the tech sector is operating, striking a new balance between economic winners and losers. Nevertheless, not all countries that are leaders in the latest technologies are strongly in favor of flexible and pro-business regulations. This can be clearly seen in the field of artificial intelligence (AI). Self-regulation as a key strategic approach to AI may be seen as an essential factor of broader implementation of AI solutions. The purpose of this paper is to present approaches to AI while indicating the differences that result from the understanding of privacy, increasing customers privacy concerns and regulations related to data privacy which come together with official administrative strategies. The impact of AI implementation on relationships between customers and companies has been emphasized and analyzed in the context of regulations and customer perception of privacy. Journal: Journal of Management Analytics Pages: 344-364 Issue: 4 Volume: 6 Year: 2019 Month: 10 X-DOI: 10.1080/23270012.2019.1671243 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1671243 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:4:p:344-364 Template-Type: ReDIF-Article 1.0 Author-Name: Dheeraj Malhotra Author-X-Name-First: Dheeraj Author-X-Name-Last: Malhotra Author-Name: O. P. Rishi Author-X-Name-First: O. P. Author-X-Name-Last: Rishi Title: A comprehensive review from hyperlink to intelligent technologies based personalized search systems Abstract: In the present era of big data, web page searching and ranking in an efficient manner on the World Wide Web to satisfy the specific search needs of the modern user is undoubtedly a major challenge for search engines. Even though a large number of web search techniques have been developed, some problems still exist while searching with generic search engines as none of the search engines can index the entire web. The issue is not just the volume but also the relevance concerning the user’s requirements. Moreover, if the search query is partially incomplete or is ambiguous, then most of the modern search engines tend to return the result by interpreting all possible meanings of the query. Concerning search quality, more than half of the retrieved web pages have been reported to be irrelevant. Hence web search personalization is required to retrieve search results while incorporating the user’s interests. In the proposed research work we have highlighted the strengths and weakness of various studies as proposed in the literature for web search personalization by carrying out a detailed comparison among them. The in-depth comparative study with baselines leads to the recommendation of Intelligent Meta Search System (IMSS) and Advanced Cluster Vector Page Ranking (ACVPR) algorithm as one of the best approaches as proposed in the literature for web search personalization. Furthermore, the detailed discussion about the comparative analysis of all categories gives new opportunities to think in different research areas. Journal: Journal of Management Analytics Pages: 365-389 Issue: 4 Volume: 6 Year: 2019 Month: 10 X-DOI: 10.1080/23270012.2019.1671241 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1671241 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:4:p:365-389 Template-Type: ReDIF-Article 1.0 Author-Name: Kenneth Tung Author-X-Name-First: Kenneth Author-X-Name-Last: Tung Title: AI, the internet of legal things, and lawyers Abstract: A great deal of has been written about the challenges of a legal profession that resists change and sub-optimizes clients’ benefits, and even more ink has been spilled on the opportunities and threats of AI. As laws are falling further behind an accelerating, dynamic world, the gap between businesses and opaque legal functions widens with the latter being perceived often as fire-fighting cost centers. This article calls out the opportunity of AI, specifically machine learning, and its impact on decision making as an opportunity for business leaders to elevate lawyers to contribute further to corporate strategies and operations. Surveying the implications of machine learning through the lens of each element of decision making, the article aims to bring the relevance of today's ubiquitous transformations to the legal function. It also reminds business leaders that the legal function will need some help, such as corporate legal strategists, to drive and sustain change that resides in the intersection of law, business and technology. Journal: Journal of Management Analytics Pages: 390-403 Issue: 4 Volume: 6 Year: 2019 Month: 10 X-DOI: 10.1080/23270012.2019.1671242 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1671242 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:4:p:390-403 Template-Type: ReDIF-Article 1.0 Author-Name: Kuo Chi-Hsien Author-X-Name-First: Kuo Author-X-Name-Last: Chi-Hsien Author-Name: Shinya Nagasawa Author-X-Name-First: Shinya Author-X-Name-Last: Nagasawa Title: Applying machine learning to market analysis: Knowing your luxury consumer Abstract: Chinese consumer research in the luxury sector is the emphasis in the business research field. However, it can be cost-intensive or time-consuming to interpret big data from any research conducted in the field. In this paper, the researchers created a machine-learning model to help minimize those research barriers.This study analyzed Chinese luxury consumption behavior, while the Chinese contributed 33% of the global luxury market in 2018 and play as a growth engine in the luxury market (Bain & Company. 2019. https://www.bain.com/insights/whats-powering-chinas-market-for-luxury-goods/). The researchers interpreted this analysis using machine-learning algorithms through different sets of conditions and then proposed an understandable and highly accurate machine-learning model.Unlike traditional statistical methods, which rely on domain experts to create hand-crafted features, this paper proposes an unsupervised end-to-end model that can directly and accurately process questionnaire data without human intervention. This paper also demonstrates how to practically apply an automatic unsupervised analysis method (PCA) to find inferences in the big data, and helps interpret the implied meaning to the questions. Journal: Journal of Management Analytics Pages: 404-419 Issue: 4 Volume: 6 Year: 2019 Month: 10 X-DOI: 10.1080/23270012.2019.1692254 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1692254 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:4:p:404-419 Template-Type: ReDIF-Article 1.0 Author-Name: Weiqiang Zhang Author-X-Name-First: Weiqiang Author-X-Name-Last: Zhang Author-Name: Yidan Xiang Author-X-Name-First: Yidan Author-X-Name-Last: Xiang Author-Name: Xiaohui Liu Author-X-Name-First: Xiaohui Author-X-Name-Last: Liu Author-Name: Pengzhu Zhang Author-X-Name-First: Pengzhu Author-X-Name-Last: Zhang Title: Domain ontology development of knowledge base in cardiovascular personalized health management Abstract: In China, cardiovascular disease has become the leading killer in recent years, and mortality from cardiovascular disease is continuing to rapidly increase. Extant medical research has proven that personal health management (prevention, intervention, and recuperation) of chronic diseases, such as cardiovascular diseases, is the best strategy for their prevention and treatment. Currently, the public can obtain health management knowledge through the Internet, newspapers, books, and other channels. However, with the explosive growth of available information, the public is limited to obtain effective health management guidance due to the characteristics of multiple sources, uneven accuracy (even some contradictory knowledge) and a major paucity of personalization, especially for the general public who lack professional medical knowledge. To address these problems, this paper proposes a knowledge base framework (i.e. domain ontology library) of health management programs based on the cardiovascular disease domain, which can standardize knowledge of health management programs both logically and structurally. In order to satisfy the needs of personalized health management, the core ontology of the domain ontology library is health-management-program ontology. In addition to common ontologies (e.g. disease ontology, drug ontology, etc.), basic ontologies include the ontology of individual health characteristics (e.g. individual-health-characteristics and environmental-characteristics ontology), and ontologies comprising diet and sport (e.g. ingredients, recipes, physical exercise, etc.). We then construct the ontology library through the professional ontology tool, Protégé. With a case study, we translate a piece of text health management knowledge into instances of an ontology library. At the same time, we present a personalized health management program recommendation algorithm based on the ontology library, and a recommendation case is realized according to this algorithm. As a basic research, the results of this paper can also support other health management applications in the future. Journal: Journal of Management Analytics Pages: 420-455 Issue: 4 Volume: 6 Year: 2019 Month: 10 X-DOI: 10.1080/23270012.2019.1694454 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1694454 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:4:p:420-455 Template-Type: ReDIF-Article 1.0 Author-Name: Yue Kang Author-X-Name-First: Yue Author-X-Name-Last: Kang Author-Name: Zhao Cai Author-X-Name-First: Zhao Author-X-Name-Last: Cai Author-Name: Chee-Wee Tan Author-X-Name-First: Chee-Wee Author-X-Name-Last: Tan Author-Name: Qian Huang Author-X-Name-First: Qian Author-X-Name-Last: Huang Author-Name: Hefu Liu Author-X-Name-First: Hefu Author-X-Name-Last: Liu Title: Natural language processing (NLP) in management research: A literature review Abstract: Natural language processing (NLP) is gaining momentum in management research for its ability to automatically analyze and comprehend human language. Yet, despite its extensive application in management research, there is neither a comprehensive review of extant literature on such applications, nor is there a detailed walkthrough on how it can be employed as an analytical technique. To this end, we review articles in the UT Dallas List of 24 Leading Business Journals that employ NLP as their focal analytical technique to elucidate how textual data can be harnessed for advancing management theories across multiple disciplines. We describe the available toolkits and procedural steps for employing NLP as an analytical technique as well as its advantages and disadvantages. In so doing, we highlight the managerial and technological challenges associated with the application of NLP in management research in order to guide future inquires. Journal: Journal of Management Analytics Pages: 139-172 Issue: 2 Volume: 7 Year: 2020 Month: 4 X-DOI: 10.1080/23270012.2020.1756939 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1756939 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:2:p:139-172 Template-Type: ReDIF-Article 1.0 Author-Name: Ebony Carter Author-X-Name-First: Ebony Author-X-Name-Last: Carter Author-Name: Patrick Adam Author-X-Name-First: Patrick Author-X-Name-Last: Adam Author-Name: Deon Tsakis Author-X-Name-First: Deon Author-X-Name-Last: Tsakis Author-Name: Stephanie Shaw Author-X-Name-First: Stephanie Author-X-Name-Last: Shaw Author-Name: Richard Watson Author-X-Name-First: Richard Author-X-Name-Last: Watson Author-Name: Peter Ryan Author-X-Name-First: Peter Author-X-Name-Last: Ryan Title: Enhancing pedestrian mobility in Smart Cities using Big Data Abstract: Smart City is an emerging concept in global urban development. A Smart City applies ICT technologies to provide greater efficiencies for its urban areas and civilian population. One of the key requirements for a Smart City is to exploit data from its ICT infrastructure (such as Internet of Things connected sensors) to improve city services and features such as accessibility and sustainability. To address this requirement, the City of Melbourne (COM) Smart City office maintains several hundred data sets relating to urban activity and development. These datasets address parking, mobility, land use, 3D data, statistics, environment, and major city developments such as rail projects. One promising dataset relates to pedestrian traffic. Data are obtained from sensors and updated on the COM website (City of Melbourne Open Data Platform: https://data.melbourne.vic.gov.au/.) at regular intervals. These data include the number of pedestrians passing 53 specific locations in the central business district and also their times and directions of travel. In a 24 h period, over 650,000 pedestrians were counted passing all locations. Peak rates of several thousand pedestrians per minute are regularly recorded during city rush hours at hotspots making the data amenable to Big Data analysis techniques. Results are obtained in graphical format as heatmaps and charts of city pedestrian traffic using both Microsoft Excel® for static analysis and PowerBI® for more advanced interactive visualisation and analysis. These findings can identify pedestrian hotspots and inform future locations of traffic lights and street configurations to make the city more pedestrian friendly. Further, the experience gained can be used to examine other data sets such as bicycle traffic that can be analysed to inform city infrastructure projects. Future work is suggested that could link these pedestrian flow data with social media data from smartphones and potentially wearable devices such as fitness monitors to correlate pedestrian satisfaction with traffic flow. The ‘happiness’ effect of pedestrians passing through green areas such as city parks can also be quantified. This research was undertaken with the assistance of Swinburne University under its Capstone Project scheme. Journal: Journal of Management Analytics Pages: 173-188 Issue: 2 Volume: 7 Year: 2020 Month: 4 X-DOI: 10.1080/23270012.2020.1741039 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1741039 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:2:p:173-188 Template-Type: ReDIF-Article 1.0 Author-Name: Sebahattin Demirkan Author-X-Name-First: Sebahattin Author-X-Name-Last: Demirkan Author-Name: Irem Demirkan Author-X-Name-First: Irem Author-X-Name-Last: Demirkan Author-Name: Andrew McKee Author-X-Name-First: Andrew Author-X-Name-Last: McKee Title: Blockchain technology in the future of business cyber security and accounting Abstract: This study looks into the current, and potential uses of Blockchain technology in business, specifically in Accounting and in cybersecurity. We relate Blockchain uses to current concerns within cybersecurity and accounting. We review the literature that includes topics such as Big Data in Accounting, blockchain’s use in financial security and cybersecurity, and its use in financial accounting though the use of ledger technology and also as a system of tracking financial misconduct. We also review the Department of Homeland Security plan for cybersecurity over the next few years to understand what the US Government plans because of the importance of cybersecurity development. We show that Blockchain impacts auditing in different ways that will change the profession drastically. We also find that blockchain should be effectively implemented into different aspects of cybersecurity, and accounting, such as Auditing and general accounting procedures. Journal: Journal of Management Analytics Pages: 189-208 Issue: 2 Volume: 7 Year: 2020 Month: 4 X-DOI: 10.1080/23270012.2020.1731721 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1731721 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:2:p:189-208 Template-Type: ReDIF-Article 1.0 Author-Name: K. S. Law Author-X-Name-First: K. S. Author-X-Name-Last: Law Author-Name: Fu-Lai Chung Author-X-Name-First: Fu-Lai Author-X-Name-Last: Chung Title: Knowledge-driven decision analytics for commercial banking Abstract: Although the corporate relationship manager seems to be the key enabler in commercial banking, the personal relationship sales model is not a sustainable model for the paradigm shift in digital financial markets. In this research, we propose a knowledge-driven decision analytics approach to improve the decision process. However, most of the corporate client documents processed in banks are not well-structured and the traditional analysis approach does not consider the document structure, which carries rich semantic information. We propose a document structure-based text representation approach with incorporating auxiliary information in the predictive analytics of unstructured data to improve the performance in the document classification task. The proposed approach significantly outperforms the traditional whole document approach which does not take into considerations of the document structure. With the proposed approach, knowledge can be effectively and efficiently used for business decisions and planning to improve the competitive advantage and substantiality of banks. Journal: Journal of Management Analytics Pages: 209-230 Issue: 2 Volume: 7 Year: 2020 Month: 4 X-DOI: 10.1080/23270012.2020.1734879 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1734879 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:2:p:209-230 Template-Type: ReDIF-Article 1.0 Author-Name: Engin Akman Author-X-Name-First: Engin Author-X-Name-Last: Akman Author-Name: Abdullah S. Karaman Author-X-Name-First: Abdullah S. Author-X-Name-Last: Karaman Author-Name: Cemil Kuzey Author-X-Name-First: Cemil Author-X-Name-Last: Kuzey Title: Visa trial of international trade: evidence from support vector machines and neural networks Abstract: International trade depends on networking, interaction and in-person meetings which stimulate cross-border travels. The countries are seeking policies to encourage inbound mobility to support bilateral trade, tourism, and foreign direct investments. Some nations have been implementing liberal visa regimes as an important part of facilitating policies in view of security concerns. Turkey has been among the nations introducing liberal visa policies to support trade in the last decade and recorded significant increases in the volumes of exports. In this paper, we employed machine learning methodologies, Support vector machines (SVM) and Neural networks (NN), to investigate the facilitating impact of liberal visa policies on bilateral trade, using the export data from Turkey for the period of 2000–2014. The research disentangled the variables that have the strongest impact on trade utilizing SVM and NN models and exhibited that visa policies have significant impacts on the bilateral trade. More relaxed visa policies are recommended for the countries in the pursuit of increasing exports. Journal: Journal of Management Analytics Pages: 231-252 Issue: 2 Volume: 7 Year: 2020 Month: 4 X-DOI: 10.1080/23270012.2020.1731719 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1731719 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:2:p:231-252 Template-Type: ReDIF-Article 1.0 Author-Name: Warit Wipulanusat Author-X-Name-First: Warit Author-X-Name-Last: Wipulanusat Author-Name: Kriengsak Panuwatwanich Author-X-Name-First: Kriengsak Author-X-Name-Last: Panuwatwanich Author-Name: Rodney A. Stewart Author-X-Name-First: Rodney A. Author-X-Name-Last: Stewart Author-Name: Stewart L. Arnold Author-X-Name-First: Stewart L. Author-X-Name-Last: Arnold Author-Name: Jue Wang Author-X-Name-First: Jue Author-X-Name-Last: Wang Title: Bayesian network revealing pathways to workplace innovation and career satisfaction in the public service Abstract: This paper examined the innovation process in the Australian Public Service (APS) using a Bayesian network (BN) founded on an empirically derived structural equation model. The focus of the BN was to examine the impact of leadership style and organisational culture on workplace innovation and career satisfaction in the APS. Using scenario analysis, the best combination of managerial actions for enhancing APS career satisfaction was determined. The results emphasise the benefit of encouraging management to adopt a transformational leadership style and instilling innovative culture in their organisation. In addition, innovative culture was a key driver of workplace innovation, which served to improve the career satisfaction of APS employees. Implications are discussed to propose practical strategies for organisations wish to encourage innovation among employees. Journal: Journal of Management Analytics Pages: 253-280 Issue: 2 Volume: 7 Year: 2020 Month: 4 X-DOI: 10.1080/23270012.2020.1749900 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1749900 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:2:p:253-280 Template-Type: ReDIF-Article 1.0 Author-Name: Yakup Çelikbilek Author-X-Name-First: Yakup Author-X-Name-Last: Çelikbilek Author-Name: Fatih Tüysüz Author-X-Name-First: Fatih Author-X-Name-Last: Tüysüz Title: An in-depth review of theory of the TOPSIS method: An experimental analysis Abstract: Decision-making is an important part of daily and business life for both individuals and organizations. Although the multi-criteria decision-making methods provide decision makers the necessary tools, they have differences in terms of the assumptions and fundamental theory. Hence, selecting the right decision-making method is at least as important as making the decision. TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) method, which is one of the most widely used multi-criteria decision-making methods, has gained attention of researchers and thus various improved versions of the method have been proposed. This study considers the conventional TOPSIS method and experimentally displays the underlying reasons of the lacks of the conventional TOPSIS method by using a simulation technique. Detailed experimental analysis based on simulation with an application is used to reveal theoretical fundamentals of the TOPSIS method to better understand it and contribute to its improvement. Journal: Journal of Management Analytics Pages: 281-300 Issue: 2 Volume: 7 Year: 2020 Month: 4 X-DOI: 10.1080/23270012.2020.1748528 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1748528 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:2:p:281-300 Template-Type: ReDIF-Article 1.0 Author-Name: Zhongzhi Yang Author-X-Name-First: Zhongzhi Author-X-Name-Last: Yang Author-Name: Pengzhi Kong Author-X-Name-First: Pengzhi Author-X-Name-Last: Kong Author-Name: Boying Li Author-X-Name-First: Boying Author-X-Name-Last: Li Author-Name: Bo Chao Author-X-Name-First: Bo Author-X-Name-Last: Chao Title: A compartment model and numerical analysis of circulatory economy Abstract: This paper describes an industrial structure and its equation system of a circular economy for material circulation and builds a system dynamic model for resources recycling utilization based on Compartment Model Theory. A circulation multiplier and its computational formula are defined for measuring the efficiency of resources recycling utilization. The simulated results indicate that the resources recycling utilization can not only realize the amount accumulation of natural resources and improve the resources recycling efficiency but can minimize discharges into natural environment by means of adjustment to each compartment parameter in the circular economy. Journal: Journal of Management Analytics Pages: 88-105 Issue: 1 Volume: 6 Year: 2019 Month: 1 X-DOI: 10.1080/23270012.2019.1566032 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1566032 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:1:p:88-105 Template-Type: ReDIF-Article 1.0 Author-Name: Binaya Kumar Panigrahi Author-X-Name-First: Binaya Kumar Author-X-Name-Last: Panigrahi Author-Name: Tushar Kumar Nath Author-X-Name-First: Tushar Kumar Author-X-Name-Last: Nath Author-Name: Manas Ranjan Senapati Author-X-Name-First: Manas Ranjan Author-X-Name-Last: Senapati Title: An application of local linear radial basis function neural network for flood prediction Abstract: Heavy seasonal rain makes waterway flood and is one of the preeminent reason behind flooding. Flooding causes various perils with outcomes including danger to human life, harm to building, streets, misfortune to horticultural fields and bringing about human uprooting. Thus, prediction of flood is of prime importance so as to reduce exposure of people and destruction of property. This paper focuses on applying different neural networks approach, i.e. Multilayer Perceptron, Radial Basis functional neural network, Local Linear Radial Basis Functional Neural Network and Artificial Neural Network with Whale Optimization to predict flood in terms of rainfall, gauge, area, velocity, pressure, average temperature, average wind speed that are setup through field and lab investigation from the contextual analysis of river “Daya” and “Bhargavi”. It has always been a troublesome undertaking to predict flood as many factors have influence on it although with this neural network models the prediction accuracy can be optimized using back propagation method which is a widely applied over traditional learning method for neural system because of its preeminent learning ability. The flood prediction system is built with the four models and a comparison is made which provides us the answer to which model is effective for the prediction. Journal: Journal of Management Analytics Pages: 67-87 Issue: 1 Volume: 6 Year: 2019 Month: 1 X-DOI: 10.1080/23270012.2019.1566033 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1566033 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:1:p:67-87 Template-Type: ReDIF-Article 1.0 Author-Name: Jingcheng Yang Author-X-Name-First: Jingcheng Author-X-Name-Last: Yang Author-Name: Kai Yu Author-X-Name-First: Kai Author-X-Name-Last: Yu Title: The role of an integrated logistics and procurement service offered by a 3PL firm in supply chain Abstract: As an integrated part in supply chain, third-party logistics (3PL) has intrinsic connections with upstream manufacturer and downstream retailer. Using a Stackelberg game model consisting of a manufacturer, a retailer and a 3PL to explicitly capture the interaction of firms' operations decisions, this paper attempts to better understand the role of integrated logistics and procurement service (ILPS) provided by a 3PL firm in supply chain management. Compared with a supply chain without ILPS, a Pareto region, in which all the supply chain members benefit from working with a 3PL firm offering ILPS, is disclosed. We also show that the Pareto region is more likely to occur with higher demand uncertainty. Finally, we reveal that the manufacturer obtains the highest profit in the Pareto region, and that the retailer can improve his profit share as the standard deviation of demand increases. Journal: Journal of Management Analytics Pages: 49-66 Issue: 1 Volume: 6 Year: 2019 Month: 1 X-DOI: 10.1080/23270012.2019.1568921 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1568921 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:1:p:49-66 Template-Type: ReDIF-Article 1.0 Author-Name: Prasobh Narayanan Author-X-Name-First: Prasobh Author-X-Name-Last: Narayanan Author-Name: Wim J. C. Verhagen Author-X-Name-First: Wim J. C. Author-X-Name-Last: Verhagen Author-Name: V. S. Viswanath Dhanisetty Author-X-Name-First: V. S. Viswanath Author-X-Name-Last: Dhanisetty Title: Identifying strategic maintenance capacity for accidental damage occurrence in aircraft operations Abstract: Airline operators face accidental damages on their fleet of aircraft as part of operational practice. Individual occurrences are hard to predict; consequently, the approach towards repairing accidental damage is reactive in aircraft maintenance practice. However, by aggregating occurrence data and predicting future occurrence rates, it is possible to predict future long-term (strategic) demand for maintenance capacity. In this paper, a novel approach for integration of reliability modelling and inventory control is presented. Here, the concept of a base stock policy has been translated to the maintenance slot capacity problem to determine long-term cost-optimal capacity. Demand has been modelled using a superposed Non-homogeneous Poisson Process (NHPP). A case study has been performed on damage data from a fleet of Boeing 777 aircraft. The results prove the feasibility of adopting an integrated approach towards strategic capacity identification, using real-life data to predict future damage occurrence and associated maintenance slot requirements. Journal: Journal of Management Analytics Pages: 30-48 Issue: 1 Volume: 6 Year: 2019 Month: 1 X-DOI: 10.1080/23270012.2019.1570364 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1570364 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:1:p:30-48 Template-Type: ReDIF-Article 1.0 Author-Name: Yang Lu Author-X-Name-First: Yang Author-X-Name-Last: Lu Title: Artificial intelligence: a survey on evolution, models, applications and future trends Abstract: Artificial intelligence (AI) is one of the core drivers of industrial development and a critical factor in promoting the integration of emerging technologies, such as graphic processing unit, Internet of Things, cloud computing, and the blockchain, in the new generation of big data and Industry 4.0. In this paper, we construct an extensive survey over the period 1961–2018 of AI and deep learning. The research provides a valuable reference for researchers and practitioners through the multi-angle systematic analysis of AI, from underlying mechanisms to practical applications, from fundamental algorithms to industrial achievements, from current status to future trends. Although there exist many issues toward AI, it is undoubtful that AI has become an innovative and revolutionary assistant in a wide range of applications and fields. Journal: Journal of Management Analytics Pages: 1-29 Issue: 1 Volume: 6 Year: 2019 Month: 1 X-DOI: 10.1080/23270012.2019.1570365 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1570365 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:1:p:1-29 Template-Type: ReDIF-Article 1.0 Author-Name: Yang Lu Author-X-Name-First: Yang Author-X-Name-Last: Lu Author-Name: Xue Ning Author-X-Name-First: Xue Author-X-Name-Last: Ning Title: A vision of 6G – 5G's successor Abstract: 6G represents the next generation of mobile communication and mobile networking. Following the previous five generations of mobile communication systems (from 1G to 5G), the development of the 6G mobile communication will be a revolution. As a holographic and ubiquitous multidimensional network, 6G will present us with the ability to establish full coverage of the “air–space–sea–land” system and to integrate with AI, IoT, and blockchain to form a network ecosystem. Our study addresses and emphasizes the literature that focuses on the technological perspectives of 6G, most specifically, its technological characteristics, key enabling technologies, and potential applications. In addition, a thorough description of the development of the mobile communication system from 1G to 6G is illustrated. This paper plays the important role of introducing a technological view of 6G to practitioners and researchers. Journal: Journal of Management Analytics Pages: 301-320 Issue: 3 Volume: 7 Year: 2020 Month: 7 X-DOI: 10.1080/23270012.2020.1802622 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1802622 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:3:p:301-320 Template-Type: ReDIF-Article 1.0 Author-Name: Anjee Gorkhali Author-X-Name-First: Anjee Author-X-Name-Last: Gorkhali Author-Name: Ling Li Author-X-Name-First: Ling Author-X-Name-Last: Li Author-Name: Asim Shrestha Author-X-Name-First: Asim Author-X-Name-Last: Shrestha Title: Blockchain: a literature review Abstract: A blockchain consists of an ordered list with nodes and links where the nodes store information and are connected through links called chains. This technology supports the availability of a publicly maintained ledger of transactions, first gaining mainstream attraction with cryptocurrencies. A myriad of other applications have emerged ever since. There has been a steady growth in the number of research studies conducted in this field; as such, there is a need to review the research in this field. This paper conducts an extensive review on 76 journal publications in the field of blockchain from 2016 to 2018 available in Science Citation Index (SCI) and Social Science Citation Index (SSCI) database. The aim of this paper is to present scholars and practitioners with a detailed overview of the available research in the field of blockchain. The selected papers have been grouped into 14 categories. The contents of papers in each category are summarized and future research direction for each category is outlined. This overview indicates that the research in blockchain is becoming more prominent and requires more effort in developing new methodologies and framework to integrate blockchain. It is the need of today’s growing business that ventures into new technologies like cloud computing and Internet of Things (IoT). Journal: Journal of Management Analytics Pages: 321-343 Issue: 3 Volume: 7 Year: 2020 Month: 7 X-DOI: 10.1080/23270012.2020.1801529 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1801529 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:3:p:321-343 Template-Type: ReDIF-Article 1.0 Author-Name: Haiyue Yu Author-X-Name-First: Haiyue Author-X-Name-Last: Yu Author-Name: Panpan Wang Author-X-Name-First: Panpan Author-X-Name-Last: Wang Author-Name: Huan Zheng Author-X-Name-First: Huan Author-X-Name-Last: Zheng Author-Name: Jifeng Luo Author-X-Name-First: Jifeng Author-X-Name-Last: Luo Author-Name: Jun Liu Author-X-Name-First: Jun Author-X-Name-Last: Liu Title: Impacts of congestion on healthcare outcomes: an empirical observation in China Abstract: Many studies have shown that healthcare quality such as mortality rate exhibits U-shaped curves for congestion. These studies usually focus on how the congestion rate affects the overall hospitals’ mortality rate. Our study investigates the impact of congestion on two subcategories: surgical and non-surgical inpatients, using the records of 27,575 patients from a public hospital in China. We confirm that the overall mortality rate exhibits a U-shaped curve. We further show that the mortality rate of surgical patients first decreases and then almost stays unchanged as congestion level increases, while the curve for the mortality rate of non-surgical patients remains U-shaped. The initial decrease partly results from the combination of decreasing mortality and increasing peak occupancy with time. The increase is in accordance with the overwork, but the condition requirement and high standard protocols of care may cause the mortality of surgical patients to be less affected by congestion. Journal: Journal of Management Analytics Pages: 344-366 Issue: 3 Volume: 7 Year: 2020 Month: 7 X-DOI: 10.1080/23270012.2020.1731720 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1731720 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:3:p:344-366 Template-Type: ReDIF-Article 1.0 Author-Name: James R. Barth Author-X-Name-First: James R. Author-X-Name-Last: Barth Author-Name: Hemantha S. B. Herath Author-X-Name-First: Hemantha S. B. Author-X-Name-Last: Herath Author-Name: Tejaswini C. Herath Author-X-Name-First: Tejaswini C. Author-X-Name-Last: Herath Author-Name: Pei Xu Author-X-Name-First: Pei Author-X-Name-Last: Xu Title: Cryptocurrency valuation and ethics: a text analytic approach Abstract: A recent and potentially profound innovation is the creation of cryptocurrencies and the underlying technology that is essential for their use in various financial transactions. Given the anonymity of a user of a cryptocurrency, such digital currencies may be used for many different types of both lawful and illicit activities. The main purpose of this paper is to examine the extent to which ethical considerations associated with the use of cryptocurrencies affect the valuations attached to such currencies. The examination is based on a text analytic approach that involves measuring the extent to which ethical and unethical words are used in a discussion related to Bitcoin on Twitter to determine if there is a connection between ethics and cryptocurrency valuations. We find the frequency of an unethical discussion about Bitcoin is negatively associated with its price. In contrast, the frequency of an ethical discussion is positively associated with its price. Journal: Journal of Management Analytics Pages: 367-388 Issue: 3 Volume: 7 Year: 2020 Month: 7 X-DOI: 10.1080/23270012.2020.1790046 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1790046 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:3:p:367-388 Template-Type: ReDIF-Article 1.0 Author-Name: Dongwei He Author-X-Name-First: Dongwei Author-X-Name-Last: He Author-Name: Kai Yu Author-X-Name-First: Kai Author-X-Name-Last: Yu Author-Name: Jun Wu Author-X-Name-First: Jun Author-X-Name-Last: Wu Title: Industry characteristics, court location, and bankruptcy resolution Abstract: Insolvent firms usually file for formal bankruptcy protection under either liquidation or reorganization. Reorganization aims to save viable failing firms whereas liquidation focuses on filtering out unviable failing firms. This paper theoretically and empirically investigates the determinants of formal bankruptcy resolution. We present a concise theory to reveal the theoretical boundary between liquidation and reorganization, which reflects how industry characteristics, judicial bias, and firm characteristics affect the outcome of bankruptcy resolution. By using the commercial bankruptcy data on US courts (2000–2016), we validate the proposed theory. In empirical tests, we deploy discrete-choice models to address the main predictions derived from theory and conduct robustness checks (e.g. placebo test). We document that firms are more likely to be reorganized when their industry is experiencing prosperity. Firms in asset-heavy industries (e.g. hotels, mining, and oil) tend to be reorganized. Formal resolution of bankruptcy cases handled by courts in Alaska and Hawaii are more likely to be reorganization than is the case in other states; however, firms that file bankruptcy petitions in California courts are more likely to face liquidation. Finally, larger and more transparent firms are more likely to be reorganized. Journal: Journal of Management Analytics Pages: 389-423 Issue: 3 Volume: 7 Year: 2020 Month: 7 X-DOI: 10.1080/23270012.2020.1715272 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1715272 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:3:p:389-423 Template-Type: ReDIF-Article 1.0 Author-Name: Neha Verma Author-X-Name-First: Neha Author-X-Name-Last: Verma Author-Name: Dheeraj Malhotra Author-X-Name-First: Dheeraj Author-X-Name-Last: Malhotra Author-Name: Jatinder Singh Author-X-Name-First: Jatinder Author-X-Name-Last: Singh Title: Big data analytics for retail industry using MapReduce-Apriori framework Abstract: Presently, retailing has changed its face from unordered stacked traditional stores to beautifully decorated and appropriately managed merchandise stores or shopping malls with excellent ambiance and comfort. Therefore, these stores try to accommodate all needed items for daily use or rarely required items under the same roof. However, the primary challenge for today’s retailer is that the modern customer is quality and brands conscious as well as compare for services provided to them by different outlets at the comfort of home with a single click. Therefore, customers prefer to purchase from E-Commerce websites instead of physically visiting a retail store, which leads to the downfall in the sales of retailers which become a serious threat to them. Therefore, retailers are required to work sincerely towards their customer expectations by providing all their needed goods under the same roof. Therefore, the objective of this paper is to assist retail business owners to recognize the purchasing needs of their customers and hence to entice customers to physical retail stores away from competitor E-Commerce websites. This paper employs a systematic research methodology based on association rule mining deployed over Map-Reduce based Apriori association mining and Hadoop based intelligent cloud architecture to determine useful buying patterns from purchase history of previous customers, in order to assist retail business owners. The finding acknowledges that the traditional mining algorithms have not progressed to support big data analysis as required by current retail businesses owners. The job of finding unknown association rules from big data requires a lot of resources such as memory and processing engines. Moreover, traditional mining systems are inadequate to provide support for partial failure support, extensibility, scalability etc. Therefore, this study aims to implement and develop MapReduce based Apriori (MR-Apriori) algorithm in the form of Intelligent Retail Mining Tool i.e. IRM Tool to recognize all these concerns in an efficient manner. The proposed system adequately satisfy all significant requisites anticipated from modern Big Data processing systems such as scalability, fault tolerance, partial failure support etc. Finally, this study experimentally verifies the effectiveness of the proposed algorithm i.e. MR-Apriori by speed-up, size-up, and scale-up evaluation parameters. Journal: Journal of Management Analytics Pages: 424-442 Issue: 3 Volume: 7 Year: 2020 Month: 7 X-DOI: 10.1080/23270012.2020.1728403 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1728403 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:3:p:424-442 Template-Type: ReDIF-Article 1.0 Author-Name: Praveen Ranjan Srivastava Author-X-Name-First: Praveen Ranjan Author-X-Name-Last: Srivastava Author-Name: Satyendra Sharma Author-X-Name-First: Satyendra Author-X-Name-Last: Sharma Author-Name: Simran Kaur Author-X-Name-First: Simran Author-X-Name-Last: Kaur Title: Data mining-based algorithm for assortment planning Abstract: With increasing varieties and products, management of limited shelf space becomes quite difficult for retailers. Hence, an efficient product assortment, which in turn helps to plan the organization of various products across limited shelf space, is extremely important for retailers. Products can be distinguished based on quality, price, brand, and other attributes, and decision needs to be made about an assortment of the products based on these attributes. An efficient assortment planning improves the financial performance of the retailer by increasing profits and reducing operational costs. Clustering techniques can be very effective in grouping products, stores, etc. and help managers solve the problem of assortment planning. This paper proposes data mining approaches for assortment planning for profit maximization with space, and cost constraints by mapping it into well-known knapsack problem. Journal: Journal of Management Analytics Pages: 443-457 Issue: 3 Volume: 7 Year: 2020 Month: 7 X-DOI: 10.1080/23270012.2020.1725666 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1725666 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:3:p:443-457 Template-Type: ReDIF-Article 1.0 Author-Name: Adarsh Anand Author-X-Name-First: Adarsh Author-X-Name-Last: Anand Author-Name: Shakshi Singhal Author-X-Name-First: Shakshi Author-X-Name-Last: Singhal Author-Name: Ompal Singh Author-X-Name-First: Ompal Author-X-Name-Last: Singh Title: Optimal advertising duration for profit maximization Abstract: The current research elucidates the advertising scheme of automotive innovation by incorporating the various stages of the product life cycle. The study proposes an empirical model for the automotive industry to evaluate a time-point known as a switch-point or a take-off point at which firms should modify the advertising and sales promotion strategies to boost sales volume. The problem applies a time-series innovation diffusion model wherein adoption rate changes when a product enters a growth stage and then again when the company stops the advertising campaign in the maturity stage. The present paper develops a profit maximization problem, which optimizes the overall advertising duration and advertising take-off point. A numerical illustration is provided using the actual sales data of automobile industries, and sensitivity analysis is further performed to validate the effect of critical parameters on the optimization problem. Journal: Journal of Management Analytics Pages: 458-480 Issue: 3 Volume: 7 Year: 2020 Month: 7 X-DOI: 10.1080/23270012.2019.1702904 File-URL: http://hdl.handle.net/10.1080/23270012.2019.1702904 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:3:p:458-480 Template-Type: ReDIF-Article 1.0 Author-Name: Yaqin Lin Author-X-Name-First: Yaqin Author-X-Name-Last: Lin Author-Name: Weiqiang Zhang Author-X-Name-First: Weiqiang Author-X-Name-Last: Zhang Title: An incentive model between a contractor and multiple subcontractors in a green supply chain based on robust optimization Abstract: In this paper, we consider the incentive mechanism of a construction supply chain which includes a contractor and several subcontractors from both economic and environmental perspectives. Firstly, we describe the structure of the construction supply chain as well as the relationship between the contractor and subcontractors. Then, a bi-level nonlinear model with multiple followers comprising uncertain parameters is developed to balance the benefits of all supply chain members. In this model, the contractor is the leader while the subcontractors are followers. Next, we convert the primal model into a deterministic counterpart robust model, and a heuristic polynomial algorithm is designed to solve the transformed model. Finally, the validity of the model is verified by a numerical example. Our paper provides a method to quantitatively analyze construction projects from the perspective of supply chains while considering economic performance and environmental performance with the existence of uncertainty. Journal: Journal of Management Analytics Pages: 481-509 Issue: 4 Volume: 7 Year: 2020 Month: 10 X-DOI: 10.1080/23270012.2020.1747030 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1747030 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:4:p:481-509 Template-Type: ReDIF-Article 1.0 Author-Name: Niamat Ullah Ibne Hossain Author-X-Name-First: Niamat Author-X-Name-Last: Ullah Ibne Hossain Author-Name: Morteza Nagahi Author-X-Name-First: Morteza Author-X-Name-Last: Nagahi Author-Name: Raed Jaradat Author-X-Name-First: Raed Author-X-Name-Last: Jaradat Author-Name: Erin Stirgus Author-X-Name-First: Erin Author-X-Name-Last: Stirgus Author-Name: Charles B. Keating Author-X-Name-First: Charles B. Author-X-Name-Last: Keating Title: The effect of an individual's education level on their systems skills in the system of systems domain Abstract: Today's rapid proliferation of information and technological advancements has led to complex and uncertain modern systems environments. The problems resulting from this increased complexity may surpass engineers’ current capacity to perform effectively within the domain of complex systems. In response to this situation, the concept of Systems Thinking (ST) has been advanced as an aid to building a mental map that offers a robust conceptual understanding to offset the challenges of modern system of systems (SoS) problems. Although there has been some research regarding the effect of age and gender on ST preferences, there is still a lack of studies investigating how an individual's ST skills preferences in system of systems (SoS) domain vary across educational qualifications. In addition, most of the extant literature focuses on one or two measures to assess the individual ST; thus, there is a need to include the full spectrum of ST measures to assess the ST skills preferences of an individual in the domain of complex systems. To address these gaps, this research uses an established ST skills preferences instrument to gauge an individual’s ST skills preferences in the SoS domain based on the educational qualifications. Two hundred and fifty-eight participants with educational qualifications ranging from non-degree to graduate degree participated in the research. The analysis of the responses was performed by a post-hoc test to show which groups differ significantly. From the results obtained through aggregate individual responses, we conclude that each group (i.e bachelor, masters and phD), possesses a different ST skills preference profile on average, and the educational qualifications in the SoS environment has a moderation impact on individuals’ system skills preferences. Journal: Journal of Management Analytics Pages: 510-531 Issue: 4 Volume: 7 Year: 2020 Month: 10 X-DOI: 10.1080/23270012.2020.1811788 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1811788 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:4:p:510-531 Template-Type: ReDIF-Article 1.0 Author-Name: Dursun Delen Author-X-Name-First: Dursun Author-X-Name-Last: Delen Author-Name: Oleksandr Dorokhov Author-X-Name-First: Oleksandr Author-X-Name-Last: Dorokhov Author-Name: Liudmyla Dorokhova Author-X-Name-First: Liudmyla Author-X-Name-Last: Dorokhova Author-Name: Hasan Dinçer Author-X-Name-First: Hasan Author-X-Name-Last: Dinçer Author-Name: Serhat Yüksel Author-X-Name-First: Serhat Author-X-Name-Last: Yüksel Title: Balanced scorecard-based analysis of customer expectations for cosmetology services: a hybrid decision modeling approach Abstract: The goal of this study is to analyze and characterize customer expectations in the cosmetics sector. Within this framework, first, the extant literature is reviewed, and 12 most prominent performance measurement criteria are identified. Then, these criteria are organized along the four different balanced scorecard dimensions. By employing an Interval Type-2 Fuzzy DEMATEL methodology, the weighted importance of these dimensions and criteria are identified. Additionally, with the Interval Type-2 Fuzzy TOPSIS approach, 13 leading cosmetic service providers in Ukraine are ranked based on their relative scores. The findings of the study indicate that consumer is the most significant dimension while learning and growth seem to have the least importance. Similarly, it is also concluded that all consumer-focused criteria (i.e. diversification of services, feedback on the product and services, and customer loyalty) have the highest priorities in the complete criterion set. Journal: Journal of Management Analytics Pages: 532-563 Issue: 4 Volume: 7 Year: 2020 Month: 10 X-DOI: 10.1080/23270012.2020.1818319 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1818319 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:4:p:532-563 Template-Type: ReDIF-Article 1.0 Author-Name: Wen Ding Author-X-Name-First: Wen Author-X-Name-Last: Ding Author-Name: Huihui Song Author-X-Name-First: Huihui Author-X-Name-Last: Song Title: Financing the price-setting newsvendor with sales effort Abstract: In this paper, we consider the financing issues in a supply chain where the capital-constrained retailer should use limited funds to pay for both order and sales. The retailer decides the retail price, order quantity exante and sales effort expost, and he supports these operational activities by two financing sources: bank loan and trade credit. We illustrate that in this two-stage problem, the price-setting retailer's equilibrium sales effort and retail price are both related to the market realization value and the retailer's cash level. These two financing sources are both able to stimulate the retailer to promote sales. If the retailer's cash level is reasonably high, both parties (supplier and retailer) benefit from trade credit. By contrast, if the retailer's cash level is low, trade credit may discourage the retailer from sales promotion. Counterintuitively, our study shows that the retailer spends more on sales when the market is good than when it is bad. Journal: Journal of Management Analytics Pages: 564-590 Issue: 4 Volume: 7 Year: 2020 Month: 10 X-DOI: 10.1080/23270012.2020.1768910 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1768910 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:4:p:564-590 Template-Type: ReDIF-Article 1.0 Author-Name: Kanchan Pradhan Author-X-Name-First: Kanchan Author-X-Name-Last: Pradhan Author-Name: Priyanka Chawla Author-X-Name-First: Priyanka Author-X-Name-Last: Chawla Title: Medical Internet of things using machine learning algorithms for lung cancer detection Abstract: This paper empirically evaluates the several machine learning algorithms adaptable for lung cancer detection linked with IoT devices. In this work, a review of nearly 65 papers for predicting different diseases, using machine learning algorithms, has been done. The analysis mainly focuses on various machine learning algorithms used for detecting several diseases in order to search for a gap toward the future improvement for detecting lung cancer in medical IoT. Each technique was analyzed on each step, and the overall drawbacks are pointed out. In addition, it also analyzes the type of data used for predicting the concerned disease, whether it is the benchmark or manually collected data. Finally, research directions have been identified and depicted based on the various existing methodologies. This will be helpful for the upcoming researchers to detect the cancerous patients accurately in early stages without any flaws. Journal: Journal of Management Analytics Pages: 591-623 Issue: 4 Volume: 7 Year: 2020 Month: 10 X-DOI: 10.1080/23270012.2020.1811789 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1811789 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:4:p:591-623 Template-Type: ReDIF-Article 1.0 Author-Name: A. Kullaya Swamy Author-X-Name-First: A. Author-X-Name-Last: Kullaya Swamy Author-Name: B. Sarojamma Author-X-Name-First: B. Author-X-Name-Last: Sarojamma Title: Bank transaction data modeling by optimized hybrid machine learning merged with ARIMA Abstract: The bank transactions are needed to be modeled to predict the future transactions of the banks based on the previous transactions. In order to achieve efficient modeling of bank data transactions, Deep Belief Network (DBN) and Neural network (NN) classifiers are used in this paper. Initially, the bank transaction data such as transaction count and amount are subjected to feature extraction to extract the statistical features. Now, the extracted data are modeled using the combination of DBN and NN models, where the average modeled output from both the network is considered as the final result. The above procedure is utilized for the two prediction models such as transaction count and transaction amount. Moreover, the transaction count from prediction model 1 is subjected to the Auto-Regressive Integrated Moving Average (ARIMA) model to compute the relationship between the transition count and transition amount. Here, as the main contribution, the number of hidden neurons in both DBN and NN are optimized or tuned accurately using the hybridized optimization models with Lion Algorithm (LA), and Artificial Bee Colony (ABC) named L-ABC model. The average of entire transactional amounts, i.e. the modeled outputs are matched with the actual data to validate the performance of the implemented model. Journal: Journal of Management Analytics Pages: 624-648 Issue: 4 Volume: 7 Year: 2020 Month: 10 X-DOI: 10.1080/23270012.2020.1726217 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1726217 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:4:p:624-648 Template-Type: ReDIF-Article 1.0 Author-Name: Anubhav Namdeo Author-X-Name-First: Anubhav Author-X-Name-Last: Namdeo Author-Name: Uttam Kumar Khedlekar Author-X-Name-First: Uttam Kumar Author-X-Name-Last: Khedlekar Author-Name: Priyanka Singh Author-X-Name-First: Priyanka Author-X-Name-Last: Singh Title: Discount pricing policy for deteriorating items under preservation technology cost and shortages Abstract: This paper presents an inventory model with a constant rate of deterioration of the product, and shortages are allowed at the end of the cycle. The stock-dependent and price-sensitive demand have been considered with a policy of providing price discount to uplift the market demand. Preservation technology is applied to preserve the items from deterioration. The objective of this model is to maximize the total profit function by finding the optimal replenishment time, the optimal preservation technology investment and the optimal quantity. Next, we have shown that the total profit is a concave function of the replenishment time and preservation technology cost. Discount in pricing may be offered at a time of festival, season breaks down, lockdown and clearance of stock, before introducing a new product with up-gradations, etc. Numerical examples and graphical analysis are provided to explain the model. The discount policy could help any business organizations for smooth running of the business and obtain respective optimal profit. Journal: Journal of Management Analytics Pages: 649-671 Issue: 4 Volume: 7 Year: 2020 Month: 10 X-DOI: 10.1080/23270012.2020.1811787 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1811787 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:7:y:2020:i:4:p:649-671 Template-Type: ReDIF-Article 1.0 Author-Name: Yuan Xu Author-X-Name-First: Yuan Author-X-Name-Last: Xu Author-Name: Yong Shin Park Author-X-Name-First: Yong Shin Author-X-Name-Last: Park Author-Name: Ju Dong Park Author-X-Name-First: Ju Dong Author-X-Name-Last: Park Author-Name: Wonjoo Cho Author-X-Name-First: Wonjoo Author-X-Name-Last: Cho Title: Evaluating the environmental efficiency of the U.S. airline industry using a directional distance function DEA approach Abstract: This study applies a directional distance function (DDF) data envelopment analysis (DEA) model to measure the environmental efficiency of 12 U.S. airlines 2013–2016 by considering flight delay and greenhouse gas (GHG) emissions as joint undesirable outputs. First, the environmental efficiency of airlines is compared using the CCR DEA (without flight delay) and DDF DEA (with flight delay). We find that several airlines experienced substantial changes in environmental efficiency scores when flight delay is considered. Secondly, a tobit regression is used to explore whether the environmental factors of fleet age, ownership type, freight traffic, market share, and carrier type affect airlines’ environmental efficiency. The results demonstrate that all of these factors significantly influence airline performance. Journal: Journal of Management Analytics Pages: 1-18 Issue: 1 Volume: 8 Year: 2021 Month: 1 X-DOI: 10.1080/23270012.2020.1832925 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1832925 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:1:p:1-18 Template-Type: ReDIF-Article 1.0 Author-Name: Oktay Karabağ Author-X-Name-First: Oktay Author-X-Name-Last: Karabağ Author-Name: M. Murat Fadıloğlu Author-X-Name-First: M. Murat Author-X-Name-Last: Fadıloğlu Title: Augmented Winter's method for forecasting under asynchronous seasonalities Abstract: The method of Winters (1960) is one of the most well-known forecasting methodologies in practice. The main reason behind its popularity is that it is easy to implement and can give quite effective and efficient results for practice purposes. However, this method is not capable of capturing a pattern being emerged due to the simultaneous effects of two different asynchronous calendars, such as Gregorian and Hijri. We adapt this method in a way that it can deal with such patterns, and study its performance using a real dataset collected from a brewery factory in Turkey. With the same data set, we also provide a comparative performance analysis between our model and several forecasting models such as Winter’s (Winters 1960), TBAT (De Livera et al. 2011), ETS (Hyndman et al. 2002), and ARIMA (Hyndman and Khandakar 2008). The results we obtained reveal that better forecasts can be achieved using the new method when two asynchronous calendars exert their effects on the time-series. Journal: Journal of Management Analytics Pages: 19-35 Issue: 1 Volume: 8 Year: 2021 Month: 01 X-DOI: 10.1080/23270012.2020.1839362 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1839362 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:1:p:19-35 Template-Type: ReDIF-Article 1.0 Author-Name: Hong Chen Author-X-Name-First: Hong Author-X-Name-Last: Chen Author-Name: Ling Li Author-X-Name-First: Ling Author-X-Name-Last: Li Author-Name: Yong Chen Author-X-Name-First: Yong Author-X-Name-Last: Chen Title: Explore success factors that impact artificial intelligence adoption on telecom industry in China Abstract: As the core driving force of the new round of informatization development and industrial revolution, the disruptive achievements of artificial intelligence (AI) are rapidly and comprehensively infiltrating into various fields of human activities. Although technologies and applications of AI have been widely studied and factors that affect AI adoption are identified in existing literature, the impact of success factors on AI adoption remains unknown. Accordingly, this paper proposes a framework to explore the impacts of success factors on AI adoption in telecom industry by integrating the technology, organization, and environment (TOE) framework and diffusion of innovation (DOI) theory. Particularly, this framework consists of factors regarding external environment, organizational capabilities, and innovation attributes of AI. The framework is empirically tested with data collected by surveying telecom companies in China. Structural equation modeling is applied to analyze the data. The study provides support for firms’ decision-making and resource allocation regarding AI adoption. Journal: Journal of Management Analytics Pages: 36-68 Issue: 1 Volume: 8 Year: 2021 Month: 01 X-DOI: 10.1080/23270012.2020.1852895 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1852895 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:1:p:36-68 Template-Type: ReDIF-Article 1.0 Author-Name: Maryam Abdirad Author-X-Name-First: Maryam Author-X-Name-Last: Abdirad Author-Name: Krishna Krishnan Author-X-Name-First: Krishna Author-X-Name-Last: Krishnan Author-Name: Deepak Gupta Author-X-Name-First: Deepak Author-X-Name-Last: Gupta Title: A two-stage metaheuristic algorithm for the dynamic vehicle routing problem in Industry 4.0 approach Abstract: Industry 4.0 is a concept that assists companies in developing a modern supply chain (MSC) system when they are faced with a dynamic process. Because Industry 4.0 focuses on mobility and real-time integration, it is a good framework for a dynamic vehicle routing problem (DVRP). This research works on DVRP. The aim of this research is to minimize transportation cost without exceeding the capacity constraint of each vehicle while serving customer demands from a common depot. Meanwhile, new orders arrive at a specific time into the system while the vehicles are executing the delivery of existing orders. This paper presents a two-stage hybrid algorithm for solving the DVRP. In the first stage, construction algorithms are applied to develop the initial route. In the second stage, improvement algorithms are applied. Experimental results were designed for different sizes of problems. Analysis results show the effectiveness of the proposed algorithm. Journal: Journal of Management Analytics Pages: 69-83 Issue: 1 Volume: 8 Year: 2021 Month: 1 X-DOI: 10.1080/23270012.2020.1811166 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1811166 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:1:p:69-83 Template-Type: ReDIF-Article 1.0 Author-Name: Shenghai Zhou Author-X-Name-First: Shenghai Author-X-Name-Last: Zhou Author-Name: Yang Zhan Author-X-Name-First: Yang Author-X-Name-Last: Zhan Title: A new method for performance evaluation of decision-making units with application to service industry Abstract: The decision-making units (DMUs) in the modern service industries may produce desirable outputs and undesirable outputs. For the decision makers, some outputs may be more desired than others although all of them are desirable. Considering these characteristics, this work combines the data envelopment analysis (DEA) and the multiple attributes decision-making (MADM) method, to make a reasonable and comprehensive performance evaluation for DMUs. Specifically, three DEA-based models are modified to obtain more reasonable efficiency scores for DMUs. The MADM method is used to determine the weights of outputs based on the preference ratings within the outputs. The efficiency scores are then multiplied by the aggregated outputs quantities to obtain the comprehensive performance scores for evaluation. The effectiveness of the proposed models is demonstrated by extensive numerical experiments. Journal: Journal of Management Analytics Pages: 84-100 Issue: 1 Volume: 8 Year: 2021 Month: 1 X-DOI: 10.1080/23270012.2020.1748527 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1748527 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:1:p:84-100 Template-Type: ReDIF-Article 1.0 Author-Name: Hui Shi Author-X-Name-First: Hui Author-X-Name-Last: Shi Author-Name: Zhongming Ma Author-X-Name-First: Zhongming Author-X-Name-Last: Ma Author-Name: Dazhi Chong Author-X-Name-First: Dazhi Author-X-Name-Last: Chong Author-Name: Wu He Author-X-Name-First: Wu Author-X-Name-Last: He Title: The impact of Facebook on real estate sales Abstract: Businesses have been using social media to promote products and services to increase sales. This paper aims to study the impact of Facebook on real estate sales. First, we examine how realtors’ activities on Facebook business pages are associated with real estate sales. Then, we include time lags in analysis because a time lag can be expected between activates on Facebook and a resulting real estate transaction. For the collected datasets, the results suggest that: (1) The total numbers of Facebook likes, links, and stories are positively associated with real estate sales; (2) The average sentiment score of Facebook posts is negatively associated with real estate sales; (3) The influence of activities on Facebook has a time lag effect on real estate sales. The research findings can be used by real estate stakeholders to promote and potentially forecast sales. Journal: Journal of Management Analytics Pages: 101-112 Issue: 1 Volume: 8 Year: 2021 Month: 01 X-DOI: 10.1080/23270012.2020.1858985 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1858985 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:1:p:101-112 Template-Type: ReDIF-Article 1.0 Author-Name: Soumya Das Author-X-Name-First: Soumya Author-X-Name-Last: Das Author-Name: Sarojananda Mishra Author-X-Name-First: Sarojananda Author-X-Name-Last: Mishra Author-Name: ManasRanjan Senapati Author-X-Name-First: ManasRanjan Author-X-Name-Last: Senapati Title: Improving time series forecasting using elephant herd optimization with feature selection methods Abstract: The time series data is chaotic, non seasonal, non stationary and random in nature. It becomes quite challenging to discover the hidden patterns of time series data. In this paper the time series data is predicted with the help of a machine learning algorithm i.e. Elephant Herd Optimization (EHO). Three different types of time series data are used to testify the superiority of the proposed method namely stock market data, currency exchange data and absenteeism at work. The data are first subjected to feature selection methods namely ANOVA and Friedman test. The feature selection methods provide relevant set of features which is fed to the neural network trained with the method. The proposed method is also compared with other methods such as local linear radial basis functional neural network and particle swarm optimization. The results prove supremacy of EHO over other methods. Journal: Journal of Management Analytics Pages: 113-133 Issue: 1 Volume: 8 Year: 2021 Month: 1 X-DOI: 10.1080/23270012.2020.1818321 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1818321 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:1:p:113-133 Template-Type: ReDIF-Article 1.0 Author-Name: S. Ganesan Author-X-Name-First: S. Author-X-Name-Last: Ganesan Author-Name: R. Uthayakumar Author-X-Name-First: R. Author-X-Name-Last: Uthayakumar Title: EPQ models with bivariate random imperfect proportions and learning-dependent production and demand rates Abstract: In this paper, three production inventory models are constructed for an imperfect manufacturing system by considering a warm-up production run, shortages during the hybrid maintenance period, and the rework of imperfect items. The proportions of imperfect items produced during the warm-up and regular production runs are random and they are represented using a bivariate random variable. The shortage quantity is partially backordered and the supply of backorder quantity is planned simultaneously with regular demand satisfaction. The learning models are designed to accommodate the different learning capabilities of workers in unit production time during warm-up and regular production periods. The production and demand rates of these models are made dependent on the learning exponents. As the resulting models are highly nonlinear in the decision variable, they are optimized using a genetic algorithm. The models are illustrated using numerical examples and sensitivity studies are performed to find the influence of the key parameters. Journal: Journal of Management Analytics Pages: 134-170 Issue: 1 Volume: 8 Year: 2021 Month: 1 X-DOI: 10.1080/23270012.2020.1818320 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1818320 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:1:p:134-170 Template-Type: ReDIF-Article 1.0 Author-Name: Jaqueline Iaksch Author-X-Name-First: Jaqueline Author-X-Name-Last: Iaksch Author-Name: Ederson Fernandes Author-X-Name-First: Ederson Author-X-Name-Last: Fernandes Author-Name: Milton Borsato Author-X-Name-First: Milton Author-X-Name-Last: Borsato Title: Digitalization and Big data in smart farming – a review Abstract: Agriculture is facing increasing challenges due to several factors such as population growth and climate change. Smart Farming is enabling the use of detailed digital information to guide decisions along the agricultural value chain. New technologies and solutions have been applied to provide alternatives to assist in information gathering and processing, and thereby contribute to increased agricultural productivity. Thus, the main objective of this article is to present a bibliometric analysis regarding digitalization and Big Data applications in Smart Farming. A total of 2401 articles were found and, based on ProKnow-C methodology criteria, thirty-nine publications were selected and analysed. Furthermore, the main solutions and opportunities about the topic were recognized aiming to direct future research. Journal: Journal of Management Analytics Pages: 333-349 Issue: 2 Volume: 8 Year: 2021 Month: 04 X-DOI: 10.1080/23270012.2021.1897957 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1897957 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:2:p:333-349 Template-Type: ReDIF-Article 1.0 Author-Name: R. Sundararajan Author-X-Name-First: R. Author-X-Name-Last: Sundararajan Author-Name: S. Vaithyasubramanian Author-X-Name-First: S. Author-X-Name-Last: Vaithyasubramanian Author-Name: A. Nagarajan Author-X-Name-First: A. Author-X-Name-Last: Nagarajan Title: Impact of delay in payment, shortage and inflation on an EOQ model with bivariate demand Abstract: The major challenge of inventory decision makers is to determine an inventory optimization strategy that ensures the right balance between keeping abundant on hand inventory to meet the demand of the customers and optimizing costs related to holding inventory. This article analyzes on providing a general deterministic inventory model in which the rate of demand is determined by price and time over the ordering cycle time. The traditional assumption of zero ending invento ry level is relaxed to a non-zero ending inventory level. Shortages are allowed which are partially backlogged. We develop models with partial backlogging and without backlogging. The aim is to maximize the profit per unit time, assuming delay in payment and inflation. An algorithm is proposed to find the optimal selling price, optimal stockout period, optimal replenishment cycle time and the optimal ending inventory level. All the possible special cases of these two models are also discussed. The numerical examples, graphical representation, and sensitivity analysis are given to illustrate the practical application of the proposed model. Journal: Journal of Management Analytics Pages: 267-294 Issue: 2 Volume: 8 Year: 2021 Month: 4 X-DOI: 10.1080/23270012.2020.1811165 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1811165 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:2:p:267-294 Template-Type: ReDIF-Article 1.0 Author-Name: Jun Ye Author-X-Name-First: Jun Author-X-Name-Last: Ye Title: Entropy measures of simplified neutrosophic sets and their decision-making approach with positive and negative arguments Abstract: Regarding the insufficiency of existing entropy measures-based decision-making approaches, this paper presents trigonometric functions (cosine and sine functions)-based entropy measures of simplified neutrosophic sets (SNSs), including the entropy measures of single-valued neutrosophic sets (SvNSs) and interval-valued neutrosophic sets (IvNSs). Next, a decision-making (DM) approach is developed regarding the ranking method of both entropy values and positive and negative arguments in SNS setting, and then the comparative analysis with existing DM approaches based on the entropy measures of SNSs (SvNSs and IvNSs) is indicated by a numerical DM example to illustrate the effectiveness and rationality of the developed DM approach in SNS setting. Lastly, two actual DM examples are provided to indicate the practicability and sufficiency of the developed DM approach. However, this study not only enriches the simplified neutrosophic entropy measure but also enhances/improves existing DM approaches based on the entropy measures. Journal: Journal of Management Analytics Pages: 252-266 Issue: 2 Volume: 8 Year: 2021 Month: 04 X-DOI: 10.1080/23270012.2021.1885513 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1885513 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:2:p:252-266 Template-Type: ReDIF-Article 1.0 Author-Name: Seyedmohsen Hosseini Author-X-Name-First: Seyedmohsen Author-X-Name-Last: Hosseini Title: A decision support system based on machined learned Bayesian network for predicting successful direct sales marketing Abstract: This paper proposes a decision support system based on a machine-learned Bayesian network (BN) to predict the success rate of telemarketing calls for long-term bank deposits. Telemarketing is one of the most common interactive techniques of direct marketing, widely used by financial institutions such as banks to sell long-term deposits. In this study, we develop a BN model that predicts the likelihood that a potential client subscribes to a long-term deposit, which is considered an output variable. The causal relationship among client attributes and outcomes has been identified using the augmented Naïve Bayes approach, a well-known supervised learning algorithm. The impact of each client's attribute on the likelihood of subscribing is predicted. Further, we carry out multiple simulation scenarios using BN’s unique features (forward and backward propagation) to provide more in-depth discussions and analysis on predicting the likelihood of subscription for clients with particular characteristics. Journal: Journal of Management Analytics Pages: 295-315 Issue: 2 Volume: 8 Year: 2021 Month: 04 X-DOI: 10.1080/23270012.2021.1897956 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1897956 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:2:p:295-315 Template-Type: ReDIF-Article 1.0 Author-Name: Judd D. Bradbury Author-X-Name-First: Judd D. Author-X-Name-Last: Bradbury Author-Name: Rosanna E. Guadagno Author-X-Name-First: Rosanna E. Author-X-Name-Last: Guadagno Title: Enhanced data narratives Abstract: Data narratives are an emerging form of communication that employs enhanced media for effective knowledge transfer of complex information. Researchers in the fields of data visualization and artificial intelligence have begun to pioneer new structures of communication to improve the efficiency of construction and the retention of information provided by the knowledge transfer experience. In this paper, we report the results of an empirical study conducted to compare the performance of various narrative communication techniques including frame based narrative visualization, documentary narrative visualization, computer generated text narratives and human generated text narratives. We assess the knowledge transfer performance for each of these data driven narrative structures. Across all conditions, an identical set of knowledge retention questions assessed participants’ recall of details from their assigned narrative communication. Statistical analysis on group performance answering the knowledge retention questions revealed that some narrative communication techniques perform better with general audiences. Journal: Journal of Management Analytics Pages: 171-194 Issue: 2 Volume: 8 Year: 2021 Month: 04 X-DOI: 10.1080/23270012.2021.1886883 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1886883 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:2:p:171-194 Template-Type: ReDIF-Article 1.0 Author-Name: R. Navodya Gurusinghe Author-X-Name-First: R. Navodya Author-X-Name-Last: Gurusinghe Author-Name: Bhadra J. H. Arachchige Author-X-Name-First: Bhadra J. H. Author-X-Name-Last: Arachchige Author-Name: Dushar Dayarathna Author-X-Name-First: Dushar Author-X-Name-Last: Dayarathna Title: Predictive HR analytics and talent management: a conceptual framework Abstract: Digitisation, new technologies and artificial intelligence demand organisations for new ways of working with a different skill set to accomplish strategic objectives. HR analytics is the scientific solution enabling organisations to make significant human capital and strategic business decisions and thereby gain a competitive advantage. However, theory-based relationships in HR analytics adoption is meagre. Further, there is a paucity of HR analytics literature on the role of contextual factors that affect organisations in building predictive HR analytics (PHRA) capability. Addressing this gap, we develop a conceptual framework through the lens of the Technological-Organisational-Environmental (TOE) framework and Resource-based theory to examine the relationships among the antecedents and consequences of PHRA capability considering talent management under the moderating effect of a data-driven culture. This paper is possibly the first study to propose a theoretical model to examine the effect of PHRA capability on talent management outcomes. Journal: Journal of Management Analytics Pages: 195-221 Issue: 2 Volume: 8 Year: 2021 Month: 04 X-DOI: 10.1080/23270012.2021.1899857 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1899857 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:2:p:195-221 Template-Type: ReDIF-Article 1.0 Author-Name: Shashi K. Shahi Author-X-Name-First: Shashi K. Author-X-Name-Last: Shahi Author-Name: Mohamed Dia Author-X-Name-First: Mohamed Author-X-Name-Last: Dia Title: Comparison of Ontario’s roundwood and recycled fibre pulp and paper mills’ performance using data Envelopment analysis Abstract: The pulp and paper industry converts roundwood and recycled fibre, collected from wastepaper into printing and writing papers, and other specialty grades of paper. The pulp and paper mills in Ontario have been facing extreme competitive pressures, which have affected their performance leading to several mill closures. The purpose of this study is to evaluate and compare the relative performance of three types of Ontario's pulp and paper mills (using all fibre, only roundwood fibre, and only recycled fibre). This study uses bootstrap data envelopment analysis in analyzing and comparing the operational efficiency of the Ontario's pulp and paper mills, with 224 sample data observations over a period of 17 years. The results indicate low levels of overall technical and managerial efficiencies in the pulp and paper mills using recycled fibre. The results of the study highlight that the pulp and paper industry needs to divert their attention to streamlining the manufacturing processes, reducing costs, improving raw material usage, and making capital investments in the new and improved technology, in order to improve the operational efficiency and competitiveness of the Ontario's pulp and paper mills. The pulp and paper mills using recycled fibre require huge capital investments, especially for installing the latest de-inking technology. The results of this study provide policy makers with detailed performance analysis so that future input resources can be reallocated to improve the performance of the pulp and paper mills in Ontario. Journal: Journal of Management Analytics Pages: 222-251 Issue: 2 Volume: 8 Year: 2021 Month: 04 X-DOI: 10.1080/23270012.2021.1884619 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1884619 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:2:p:222-251 Template-Type: ReDIF-Article 1.0 Author-Name: Jin Ho Kim Author-X-Name-First: Jin Ho Author-X-Name-Last: Kim Title: 6G and Internet of Things: a survey Abstract: The 5G networks which have begun to spread worldwide are expected to contribute to an increase in the use of Internet of Things (IoT) technologies and applications, which require massive connectivity, security, and ultra-low latency. However, it is known that 5G alone is not sufficient for many IoT devices to exchange various types of data in real time. These constraints promote the emergence of 6G technologies which can support higher network capacity, lower latency, and faster data transmission than 5G networks. To understand the current trends in 6G research and their relation to IoT, this paper introduces the main drivers of 6G technology, describes 6G’s enabling technologies, summarizes current 6G research, and introduces the possible applications of 6G to IoT technologies and service areas. Journal: Journal of Management Analytics Pages: 316-332 Issue: 2 Volume: 8 Year: 2021 Month: 04 X-DOI: 10.1080/23270012.2021.1882350 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1882350 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:2:p:316-332 Template-Type: ReDIF-Article 1.0 Author-Name: Yang Lu Author-X-Name-First: Yang Author-X-Name-Last: Lu Author-Name: Therese L. Williams Author-X-Name-First: Therese L. Author-X-Name-Last: Williams Title: Modeling analytics in COVID-19: prediction, prevention, control, and evaluation Abstract: The outbreak of COVID-19 has attracted attention from all around the world. Governments and institutions have adopted ways to fight COVID-19, but its prevalence is still strong. The SIR model has important reference value for the novel coronavirus epidemic, offering both preventive measures and the ability to predict future trends. Based on an analysis of the classical epidemiological SIR model along with key parameters, this paper aims to analyze the patterns of COVID-19, to discuss potential anti-COVID-19 measures, and to explain why we need to conduct appropriate measures against COVID-19. The use of the SIR model can play an important role in public health emergencies. Among the parameters of the SIR model, the contact ratio and the reproduction ratio are the factors that have the potential to mitigate the consequences of COVID-19. Anti-COVID-19 measures include wearing a mask, washing one’s hands, keeping social distance, and staying at home if possible. Journal: Journal of Management Analytics Pages: 424-442 Issue: 3 Volume: 8 Year: 2021 Month: 07 X-DOI: 10.1080/23270012.2021.1946664 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1946664 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:3:p:424-442 Template-Type: ReDIF-Article 1.0 Author-Name: Shi Yin Author-X-Name-First: Shi Author-X-Name-Last: Yin Author-Name: Nan Zhang Author-X-Name-First: Nan Author-X-Name-Last: Zhang Author-Name: Junfeng Xu Author-X-Name-First: Junfeng Author-X-Name-Last: Xu Title: Information fusion for future COVID-19 prevention: continuous mechanism of big data intelligent innovation for the emergency management of a public epidemic outbreak Abstract: Information fusion is very effective and necessary to respond to a public epidemic outbreak such as COVID-19. Big data intelligent, as a product of information fusion, plays an important role in the prevention and control of COVID-19. The continuous mechanism of big data intelligent innovation (BDII) is fundamental to effectively prevent and control a public epidemic outbreak. In this study, the continuous mechanism of BDII was fused into a complex network, and a three-dimensional collaborative epidemic prevention model was constructed. Furthermore, adiabatic elimination principle was applied to explore the order parameter of the continuous mechanism. Finally, empirical analysis was conducted based on three-stage epidemic prevention strategies to reveal the effect of continuous epidemic prevention under different big data intelligent emergency management policy levels. The results of this study are as follows. Through the mutual influence and coupling of the subsystems, the continuous mechanism of BDII can be realized to manage a public epidemic outbreak emergency. The big data intelligent subsystem is integrated into the subsystems of public epidemic outbreak management and science and technology innovation. The big data intelligent emergency management policies play a positive role in the overall BDII for the continuous epidemic prevention of a public epidemic outbreak. The convention of BDII transformation is the continuous mechanism of BDII as the order parameter of a public epidemic outbreak. In the early stage of epidemic prevention, the convention is excessively pursued, while the neglect of BDII configuration is not conducive to the long-term collaborative governance of a public epidemic outbreak. The study provides practical guidelines for the formulation of fusion innovation policies, application of big data intelligent, and theoretical basis for the emergency management of a public epidemic outbreak in the medical field. Journal: Journal of Management Analytics Pages: 391-423 Issue: 3 Volume: 8 Year: 2021 Month: 07 X-DOI: 10.1080/23270012.2021.1945499 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1945499 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:3:p:391-423 Template-Type: ReDIF-Article 1.0 Author-Name: Xinlei Wang Author-X-Name-First: Xinlei Author-X-Name-Last: Wang Author-Name: Jianing Zhi Author-X-Name-First: Jianing Author-X-Name-Last: Zhi Title: A machine learning-based analytical framework for employee turnover prediction Abstract: Employee turnover (ET) can cause severe consequences to a company, which are hard to be replaced or rebuilt. It is thus crucial to develop an intelligent system that can accurately predict the likelihood of ET, allowing the human resource management team to take pro-active action for retention or plan for succession. However, building such a system faces challenges due to the variety of influential human factors, the lack of training data, and the large pool of candidate models to choose from. Solutions offered by existing studies only adopt essential learning strategies. To fill this methodological gap, we propose a machine learning-based analytical framework that adopts a streamlined approach to feature engineering, model training and validation, and ensemble learning towards building an accurate and robust predictive model. The proposed framework is evaluated on two representative datasets with different sizes and feature settings. Results demonstrate the superior performance of the final model produced by our framework. Journal: Journal of Management Analytics Pages: 351-370 Issue: 3 Volume: 8 Year: 2021 Month: 07 X-DOI: 10.1080/23270012.2021.1961318 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1961318 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:3:p:351-370 Template-Type: ReDIF-Article 1.0 Author-Name: Sandesh S. Kurade Author-X-Name-First: Sandesh S. Author-X-Name-Last: Kurade Author-Name: Raosaheb Latpate Author-X-Name-First: Raosaheb Author-X-Name-Last: Latpate Title: Demand and deterioration of items per unit time inventory models with shortages using genetic algorithm Abstract: Inventory management is a crucial task for any industry. In this paper, we have determined the optimum profit and economical order quantity under variety of assumptions such as the demand per unit time follows either a log-normal or a generalized exponential distribution. Parametric relationship between these two distributions, the proposed models become comparable. For modeling, we consider the expected demand and variable deterioration. Under these probabilistic assumptions, inventory models are developed for situations like no, complete and partial backlogging. Classical methods are unable to solve these situations under these assumptions. Thus genetic algorithm is proposed to solve these models. Economic order quantity is obtained for maximizing the total profit for the respective demand per unit time distributions. A real-world case study of a deteriorated product is presented to illustrate the procedures of the proposed inventory models. Journal: Journal of Management Analytics Pages: 502-529 Issue: 3 Volume: 8 Year: 2021 Month: 07 X-DOI: 10.1080/23270012.2020.1829113 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1829113 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:3:p:502-529 Template-Type: ReDIF-Article 1.0 Author-Name: Honglu Wang Author-X-Name-First: Honglu Author-X-Name-Last: Wang Author-Name: Min Zhang Author-X-Name-First: Min Author-X-Name-Last: Zhang Author-Name: Hao Ying Author-X-Name-First: Hao Author-X-Name-Last: Ying Author-Name: Xiande Zhao Author-X-Name-First: Xiande Author-X-Name-Last: Zhao Title: The impact of blockchain technology on consumer behavior: a multimethod study Abstract: Blockchain is a ground-breaking technology that is transforming supply chain management. This study aims to empirically investigate the impacts of blockchain technology on consumer behavior. We conduct this research in collaboration with a Chinese e-commerce company that has introduced a blockchain platform for tracing. We use a multimethod design by combining natural experiment- and case study methods. First, we collected data from four industries (i.e. milk powder, seafood, alcohol and nutrition) to conduct the experiment, and the findings reveal that the firms that adopted the blockchain tracing system have an increase in product sales and a decrease in product returns compared to those that did not. Second, we conducted a multiple case study with four cases from the four industries. The findings reveal that the adoption of a blockchain tracing system improves supply chain transparency and process management, which then enhances consumer service and trust. This study contributes to the literature by providing empirical evidence that adopting blockchain technology can improve firms’ marketing performance. The findings also reveal how the adoption of blockchain technology affects consumer behavior. Journal: Journal of Management Analytics Pages: 371-390 Issue: 3 Volume: 8 Year: 2021 Month: 07 X-DOI: 10.1080/23270012.2021.1958264 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1958264 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:3:p:371-390 Template-Type: ReDIF-Article 1.0 Author-Name: Maria Esther F. Medalla Author-X-Name-First: Maria Esther F. Author-X-Name-Last: Medalla Author-Name: Kafferine D. Yamagishi Author-X-Name-First: Kafferine D. Author-X-Name-Last: Yamagishi Author-Name: Ann Myril C. Tiu Author-X-Name-First: Ann Myril C. Author-X-Name-Last: Tiu Author-Name: Reciel Ann B. Tanaid Author-X-Name-First: Reciel Ann B. Author-X-Name-Last: Tanaid Author-Name: Dharyll Prince M. Abellana Author-X-Name-First: Dharyll Prince M. Author-X-Name-Last: Abellana Author-Name: Shirley Ann A. Caballes Author-X-Name-First: Shirley Ann A. Author-X-Name-Last: Caballes Author-Name: Eula Margareth Y. Jabilles Author-X-Name-First: Eula Margareth Y. Author-X-Name-Last: Jabilles Author-Name: Egberto F. Selerio Author-X-Name-First: Egberto F. Author-X-Name-Last: Selerio Author-Name: Miriam F. Bongo Author-X-Name-First: Miriam F. Author-X-Name-Last: Bongo Author-Name: Lanndon A. Ocampo Author-X-Name-First: Lanndon A. Author-X-Name-Last: Ocampo Title: Relationship mapping of consumer buying behavior antecedents of secondhand clothing with fuzzy DEMATEL Abstract: This paper aims to identify the antecedents of buying behavior for secondhand clothing among millennials, as well as to determine their underlying causal relationships. Upon a comprehensive literature search, a total of 18 antecedents were found, and these are categorized into three motives, namely, economic, hedonic and recreational, and critical. As a case study in the Philippines, a focus group discussion among experts who are active millennial secondhand clothing users and buyers were tasked to identify the antecedents they have experienced and further confirm those extracted from the literature. To establish the causal relationships of these antecedents, categorize them into net causes or net effects, and address the vagueness associated with the decision-making process, a fuzzy DEMATEL method is used. Results reveal that avoidance of conventional channels proves to be the antecedent providing the highest impact among all other antecedents. Uniqueness, high quality, and fashion trend found to be the antecedents with the highest impacts received, making them the major net effects. Findings from this work hope to provide a framework among practitioners that would lead to a better understanding of millennials’ buying behavior for secondhand clothing. Journal: Journal of Management Analytics Pages: 530-568 Issue: 3 Volume: 8 Year: 2021 Month: 07 X-DOI: 10.1080/23270012.2020.1870878 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1870878 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:3:p:530-568 Template-Type: ReDIF-Article 1.0 Author-Name: Stanislaus Solomon Author-X-Name-First: Stanislaus Author-X-Name-Last: Solomon Author-Name: William A. Ellegood Author-X-Name-First: William A. Author-X-Name-Last: Ellegood Author-Name: Gertrude Pannirselvam Author-X-Name-First: Gertrude Author-X-Name-Last: Pannirselvam Author-Name: Jason Riley Author-X-Name-First: Jason Author-X-Name-Last: Riley Title: A decision support model for supplier portfolio selection in the retail industry Abstract: In this paper we propose and test a staged knapsack model to create a supplier portfolio for retailers. The first two stages in the model use judgement criteria related to supplier characteristics and supplier fit, gathered from managers to create a portfolio that optimizes supplier rank and fit scores. In the third stage we optimize cost by considering landed costs for each supplier in the portfolio to arrive at the final set of suppliers. The results indicate that the criteria used, and the staged refinement of the supplier pool resulted in a better fit than just considering the landed costs of the final pool of suppliers without rank and fit score constraints. The proposed method will enable purchasing managers to strategically create a supplier portfolio that will suit the retailer’s needs more effectively. Journal: Journal of Management Analytics Pages: 486-501 Issue: 3 Volume: 8 Year: 2021 Month: 07 X-DOI: 10.1080/23270012.2021.1882349 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1882349 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:3:p:486-501 Template-Type: ReDIF-Article 1.0 Author-Name: Hamizan Sharbini Author-X-Name-First: Hamizan Author-X-Name-Last: Sharbini Author-Name: Roselina Sallehuddin Author-X-Name-First: Roselina Author-X-Name-Last: Sallehuddin Author-Name: Habibollah Haron Author-X-Name-First: Habibollah Author-X-Name-Last: Haron Title: Crowd evacuation simulation model with soft computing optimization techniques: a systematic literature review Abstract: Crowd evacuation simulation is an essential element when it comes to planning and preparation in evacuation management. This paper presents the survey based on systematic literature review (SLR) technique that aims to identify the crowd evacuation under microscopic model integrated with soft computing technique from previous works. In the review process, renowned databases were searched to retrieve the primary articles and total 38 studies were thoroughly studied. The researcher has identified the potential optimization factors in simulating crowd evacuation and research gaps based on acquired issues, limitation and challenges in this domain. The results of this SLR will serve as a guideline for the researchers that have same interest to develop better and effective crowd evacuation simulation model. The future direction from this SLR also suggests that there is a potential to hybrid the model with soft-computing optimization focusing on latest nature-inspired algorithms in improving the crowd evacuation model. Journal: Journal of Management Analytics Pages: 443-485 Issue: 3 Volume: 8 Year: 2021 Month: 07 X-DOI: 10.1080/23270012.2021.1881924 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1881924 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:3:p:443-485 Template-Type: ReDIF-Article 1.0 Author-Name: Rubi Das Author-X-Name-First: Rubi Author-X-Name-Last: Das Author-Name: Pijus Kanti De Author-X-Name-First: Pijus Kanti Author-X-Name-Last: De Author-Name: Abhijit Barman Author-X-Name-First: Abhijit Author-X-Name-Last: Barman Title: Pricing and ordering strategies in a two-echelon supply chain under price discount policy: a Stackelberg game approach Abstract: Supply chain management coordinates different strategies for the production system. The manufacturer requires some incentive schemes to motivate the retailer to change his policy, optimal for the whole system. This paper suggests a discount mechanism by which companies can coordinate their ordering and pricing strategies throughout a supply chain model with a single manufacturer and single retailer. Also, the demand curve is iso-elastic price sensitive. Channel members have decided their selling price and order quantity jointly and independently to maximize the supply chain profit. A coordination mechanism is proposed based on quantity discounts to correlate pricing and ordering strategies simultaneously. The decentralized case is analyzed under the manufacturer-Stackelberg game approach. The result of numerical investigation shows that the suggested discount mechanism has improved the supply chain profit as well as each channel member's profit in comparison with the centralized and decentralized decisions without discount. Journal: Journal of Management Analytics Pages: 646-672 Issue: 4 Volume: 8 Year: 2021 Month: 10 X-DOI: 10.1080/23270012.2021.1911697 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1911697 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:4:p:646-672 Template-Type: ReDIF-Article 1.0 Author-Name: Gunjan Bansal Author-X-Name-First: Gunjan Author-X-Name-Last: Bansal Author-Name: Adarsh Anand Author-X-Name-First: Adarsh Author-X-Name-Last: Anand Author-Name: Deepti Aggrawal Author-X-Name-First: Deepti Author-X-Name-Last: Aggrawal Title: Modeling multi-generational diffusion for competitive brands: an analysis for telecommunication industries Abstract: Manufacturers keep bringing necessary rectifications in the products to achieve constant market penetration and high customer satisfaction. The present paper proposes a modeling framework wherein the impact of customer satisfaction with the first generation of a product is measured and its significant role has been incorporated in the adoption of a subsequent generation of the product. The model also integrates the influence of competition which is classified under two categories such that: - (a) competition within successive generations of the brand; and (b) competition among different brands. Further, model validation is performed using telecommunication industry sales data and sales are forecasted in two different ways i.e. using secondary data (a formal way) and primary data; where a survey of first-generation consumers is carried out and the concept of brand switching analysis and logistic regression have applied to analyze the market switching behavior and the satisfaction measure in the proposed models. Journal: Journal of Management Analytics Pages: 715-740 Issue: 4 Volume: 8 Year: 2021 Month: 10 X-DOI: 10.1080/23270012.2021.1881925 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1881925 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:4:p:715-740 Template-Type: ReDIF-Article 1.0 Author-Name: Xiaohui Liu Author-X-Name-First: Xiaohui Author-X-Name-Last: Liu Author-Name: Fei Liu Author-X-Name-First: Fei Author-X-Name-Last: Liu Author-Name: Yijing Li Author-X-Name-First: Yijing Author-X-Name-Last: Li Author-Name: Huizhang Shen Author-X-Name-First: Huizhang Author-X-Name-Last: Shen Author-Name: Eric T.K. Lim Author-X-Name-First: Eric T.K. Author-X-Name-Last: Lim Author-Name: Chee-Wee Tan Author-X-Name-First: Chee-Wee Author-X-Name-Last: Tan Title: Image Analytics: A consolidation of visual feature extraction methods Abstract: Revolutionary advances in machine and deep learning techniques within the field of computer field have dramatically expanded our opportunities to decipher the merits of digital imagery in the business world. Although extant literature on computer vision has yielded a myriad of approaches for extracting core attributes from images, the esotericism of the advocated techniques hinders scholars from delving into the role of visual rhetoric in driving business performance. Consequently, this tutorial aims to consolidate resources for extracting visual features via conventional machine and/or deep learning techniques. We describe resources and techniques based on three visual feature extraction methods, namely calculation-, recognition-, and simulation-based. Additionally, we offer practical examples to illustrate how image features can be accessed via open-sourced python packages such as OpenCV and TensorFlow. Journal: Journal of Management Analytics Pages: 569-597 Issue: 4 Volume: 8 Year: 2021 Month: 10 X-DOI: 10.1080/23270012.2021.1998801 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1998801 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:4:p:569-597 Template-Type: ReDIF-Article 1.0 Author-Name: Ran Jiang Author-X-Name-First: Ran Author-X-Name-Last: Jiang Author-Name: Laijun Zhao Author-X-Name-First: Laijun Author-X-Name-Last: Zhao Title: Modelling the effects of emission control areas on shipping company operations and environmental consequences Abstract: In this study, two integrated game models are developed to explore the possible economic and environmental consequences of Emission control areas (ECA) regulations. Moreover, the analytical solutions compared with a benchmark case are derived. We find that vessel speed and SO2 emissions will decrease under the ECA regulations. However, shipping company’s level of competition has no effect on the equivalent speed. The equivalent freight volume to be reduced or increased is determined by the additional operational cost per voyage due to ECA regulations. Numerical study and sensitivity analysis reveal that the vessel speed and SO2 emission reduction are very sensitive to the inventory costs of in-transit cargo. Furthermore, if low-sulphur marine gas oil is used throughout the voyage, the SO2 emission reduction may be greater than 80%, with a low impact on the shipping company’s profit. Thus, considering the environmental effects, much stricter limits can be set in the future. Journal: Journal of Management Analytics Pages: 622-645 Issue: 4 Volume: 8 Year: 2021 Month: 10 X-DOI: 10.1080/23270012.2021.1993455 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1993455 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:4:p:622-645 Template-Type: ReDIF-Article 1.0 Author-Name: Alireza Amini Author-X-Name-First: Alireza Author-X-Name-Last: Amini Author-Name: Moslem Alimohammadlou Author-X-Name-First: Moslem Author-X-Name-Last: Alimohammadlou Title: Toward equation structural modeling: an integration of interpretive structural modeling and structural equation modeling Abstract: Interpretive structural modeling (ISM) is an interactive process in which a malformed (bad structured) problem is structured into a comprehensive systematic model. Yet, despite many advantages that ISM provides, this method has some shortcomings, the most important one of which is its reliance on participants’ intuition and judgment. This problem undermines the validity of ISM. To solve this problem and further enhance the ISM method, the present study proposes a method called equation structural modeling (ESM), which draws on the capacities of structural equation modeling (SEM). As such, ESM provides a statistically verifiable framework and provides a graphical, hierarchical and intuitive model. Journal: Journal of Management Analytics Pages: 693-714 Issue: 4 Volume: 8 Year: 2021 Month: 10 X-DOI: 10.1080/23270012.2021.1881927 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1881927 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:4:p:693-714 Template-Type: ReDIF-Article 1.0 Author-Name: Ajoy Kumar Maiti Author-X-Name-First: Ajoy Kumar Author-X-Name-Last: Maiti Title: Cloudy fuzzy inventory model under imperfect production process with demand dependent production rate Abstract: The aim of this article is an effort to initiate the cloudy fuzzy number in developing classical economic production lot-size model of an item produced in scrappy production process with fixed ordering cost and without shortages. Here, the market value of an item is cloudy fuzzy number and the production rate is demand dependent. In general, fuzziness of any parameter remains fixed over time, but in practice, fuzziness of parameter begins to reduce as time progresses because of collected experience and knowledge that motivates to take cloudy fuzzy number. The model is solved in a crisp, general fuzzy and cloudy fuzzy environment using Yager’s index method and De and Beg’s ranking index method and comparisons are made for all cases and better results obtained in the cloudy fuzzy model. The model is solved by dominance based Particle Swarm Optimization algorithm to obtain optimal decision and numerical examples and sensitivity analyses are presented to justify the notion. Journal: Journal of Management Analytics Pages: 741-763 Issue: 4 Volume: 8 Year: 2021 Month: 10 X-DOI: 10.1080/23270012.2020.1866696 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1866696 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:4:p:741-763 Template-Type: ReDIF-Article 1.0 Author-Name: Lanndon A. Ocampo Author-X-Name-First: Lanndon A. Author-X-Name-Last: Ocampo Author-Name: Neelesh N. Vasnani Author-X-Name-First: Neelesh N. Author-X-Name-Last: Vasnani Author-Name: Felixter Leone S. Chua Author-X-Name-First: Felixter Leone S. Author-X-Name-Last: Chua Author-Name: Lance Brandon M. Pacio Author-X-Name-First: Lance Brandon M. Author-X-Name-Last: Pacio Author-Name: Brian J. Galli Author-X-Name-First: Brian J. Author-X-Name-Last: Galli Title: A bi-level optimization for a make-to-order manufacturing supply chain planning: a case in the steel industry Abstract: This paper presents an actual case application of a newly developed game-theoretic model in analyzing a single manufacturer-many supplier, multi-period, make-to-order supply chain with fuzzy parameters. The supply chain under consideration comprises an exclusive supplier for every component required by the manufacturer in producing its product. In certain instances, some supply chains enable the manufacturer to opt for a third-party subcontractor to produce a portion of its demand. We assume that the supply chain faces a price and lead-time-sensitive demand, which is relevant in a make-to-order environment. The vertical interaction within the supply chain is played as a Stackelberg game, where the manufacturer is considered the leader and the suppliers as the followers. Results show some important managerial insights in supply chain planning under a make-to-order condition. Journal: Journal of Management Analytics Pages: 598-621 Issue: 4 Volume: 8 Year: 2021 Month: 10 X-DOI: 10.1080/23270012.2020.1871431 File-URL: http://hdl.handle.net/10.1080/23270012.2020.1871431 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:4:p:598-621 Template-Type: ReDIF-Article 1.0 Author-Name: Xuemei Zhang Author-X-Name-First: Xuemei Author-X-Name-Last: Zhang Author-Name: Haitao Yin Author-X-Name-First: Haitao Author-X-Name-Last: Yin Author-Name: Rui Zhao Author-X-Name-First: Rui Author-X-Name-Last: Zhao Title: Consumer willingness to pay for eco-labels in China: A choice experiment approach Abstract: The use of eco-labels has become increasingly popular in China. This study aims to understand whether Chinese consumers are willing to pay for eco-labels and how their willingness to pay (WTP) is determined. Although we find that Chinese consumers are willing to pay more for products that are labeled as having greater energy efficiency (Energy Efficiency Label) or as having been produced using more environmentally-friendly production processes (Environmental Label), some practices have significantly impaired the effectiveness of these labels, e.g. the burgeoning use of eco-labels has led to label confusion; public trust towards eco-labels. The policy implications are discussed in this study. Journal: Journal of Management Analytics Pages: 673-692 Issue: 4 Volume: 8 Year: 2021 Month: 10 X-DOI: 10.1080/23270012.2021.1993096 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1993096 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:4:p:673-692 Template-Type: ReDIF-Article 1.0 Author-Name: Hong Chen Author-X-Name-First: Hong Author-X-Name-Last: Chen Author-Name: Ling Li Author-X-Name-First: Ling Author-X-Name-Last: Li Author-Name: Yong Chen Author-X-Name-First: Yong Author-X-Name-Last: Chen Title: Sustainable growth research – A study on the telecom operators in China Abstract: In recent years, the telecom industry has faced digital transformation challenges and fierce market competition. The challenges push telecom operators to grow their subscriber bases by offering lower prices and improved services and new features, which puts pressure on operators’ profitability. In addition, the rise of Internet companies gradually erodes the profit of the traditional telecom operators. Therefore, paying attention to the critical factors impacting firm sustainable growth can help operators get out of the predicament. Based on the resource-based view (RBV), this study explores the factors that influence the firm sustainable growth. Multiple regression model is applied to empirically test the hypotheses with longitudinal time-series panel data from major telecom operators in China. The study provides empirical evidence for sustainable growth research and useful insights for practitioners on the way to keep sustainable growth. Journal: Journal of Management Analytics Pages: 17-31 Issue: 1 Volume: 9 Year: 2022 Month: 01 X-DOI: 10.1080/23270012.2021.1980445 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1980445 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:1:p:17-31 Template-Type: ReDIF-Article 1.0 Author-Name: Kuldeep Chaudhary Author-X-Name-First: Kuldeep Author-X-Name-Last: Chaudhary Author-Name: Pradeep Kumar Author-X-Name-First: Pradeep Author-X-Name-Last: Kumar Author-Name: Sudipa Chauhan Author-X-Name-First: Sudipa Author-X-Name-Last: Chauhan Author-Name: Vijay Kumar Author-X-Name-First: Vijay Author-X-Name-Last: Kumar Title: Optimal promotional policy of an innovation diffusion model incorporating the brand image in a segment-specific market Abstract: This paper addresses the problem of determining the optimal promotional policy for a diffusion model in a segment-specific market under the assumption that the additional demand of the new product also improves brand image in the form of goodwill of the firm. The model is framed with the assumption that the firm uses the mass and differentiated promotion effort for each segment. The differentiated promotional efforts target each market segment independently and the mass promotional effort reaches different segments with a fixed spectrum. We derive the optimal promotional effort policy for each segment using maximum-principle and also analyze the stability of the dynamical system by constructing a Lyapunov function through the graph theoretic approach. The analysis gives a deep insight into how the promotional effort should be planned by the decision makers keeping in mind the financial constrains without hindering the promotional effort at the end of the planning period. Journal: Journal of Management Analytics Pages: 120-136 Issue: 1 Volume: 9 Year: 2022 Month: 01 X-DOI: 10.1080/23270012.2021.1978883 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1978883 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:1:p:120-136 Template-Type: ReDIF-Article 1.0 Author-Name: Ulf Norinder Author-X-Name-First: Ulf Author-X-Name-Last: Norinder Author-Name: Petra Norinder Author-X-Name-First: Petra Author-X-Name-Last: Norinder Title: Predicting Amazon customer reviews with deep confidence using deep learning and conformal prediction Abstract: In this investigation, we have shown that the combination of deep learning, including natural language processing, and conformal prediction results in highly predictive and efficient temporal test set sentiment estimates for 12 categories of Amazon product reviews using either in-category predictions, i.e. the model and the test set are from the same review category or cross-category predictions, i.e. using a model of another review category for predicting the test set. The similar results from in- and cross-category predictions indicate high degree of generalizability across product review categories. The investigation also shows that the combination of deep learning and conformal prediction gracefully handles class imbalances without explicit class balancing measures. Journal: Journal of Management Analytics Pages: 1-16 Issue: 1 Volume: 9 Year: 2022 Month: 01 X-DOI: 10.1080/23270012.2022.2031324 File-URL: http://hdl.handle.net/10.1080/23270012.2022.2031324 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:1:p:1-16 Template-Type: ReDIF-Article 1.0 Author-Name: Seyedeh Saeedeh Mohammadi Author-X-Name-First: Seyedeh Saeedeh Author-X-Name-Last: Mohammadi Author-Name: Adel Azar Author-X-Name-First: Adel Author-X-Name-Last: Azar Author-Name: Ali Rajabzadeh Ghatari Author-X-Name-First: Ali Rajabzadeh Author-X-Name-Last: Ghatari Author-Name: Moslem Alimohammadlou Author-X-Name-First: Moslem Author-X-Name-Last: Alimohammadlou Title: A model for selecting green suppliers through interval-valued intuitionistic fuzzy multi criteria decision making models Abstract: This study sought to construct a model for selecting green suppliers. The study was conducted in three phases: in phase 1, the literature was reviewed based on the meta-synthesis method and experts’ opinions were analyzed to create a model composed of 11 dimensions and 40 criteria for selecting green suppliers. In phase 2, intuitionistic fuzzy best-worst method was used to weight the dimensions and criteria. The most important dimension was “green image” and the most important criteria were the “environmental protection system” and a good “public image.” In phase 3, four suppliers in the field of medical equipment were assessed and ranked. The study proposed a framework for selecting green supplier selection (GSC) and could prove to be more comprehensive than the existing models as it drew on the meta-synthesis method. Another innovation of the study was its reliance on the intuitionistic fuzzy set theory to overcome ambiguities in selecting suppliers. Journal: Journal of Management Analytics Pages: 60-85 Issue: 1 Volume: 9 Year: 2022 Month: 01 X-DOI: 10.1080/23270012.2021.1881926 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1881926 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:1:p:60-85 Template-Type: ReDIF-Article 1.0 Author-Name: Bibhas C. Giri Author-X-Name-First: Bibhas C. Author-X-Name-Last: Giri Author-Name: Anamika Dash Author-X-Name-First: Anamika Author-X-Name-Last: Dash Title: Optimal batch shipment policy for an imperfect production system under price-, advertisement- and green-sensitive demand Abstract: In this paper, we consider an imperfect production-inventory system which consists of a single manufacturer and a single retailer. The manufacturer delivers the order quantity to the retailer in some unequal-sized batches. To separate the defective items, the retailer performs an error-free screening process after receiving each delivery from the manufacturer. Shortage in retailer's inventory is allowed and completely backlogged. The customer demand is influenced by the retail price, advertisement frequency and greening level of the product. The centralized model and the decentralized model based on a Stackelberg gaming approach are developed to determine optimal pricing, advertising and inventory decisions. A cost-sharing contract between the manufacturer and the retailer is implemented, which enhances the environmental performance, advertisement frequency and profitability of the supply chain significantly. The proposed model is illustrated with a numerical example. Sensitivity analysis for some key parameters is carried out and several managerial insights are also highlighted. Journal: Journal of Management Analytics Pages: 86-119 Issue: 1 Volume: 9 Year: 2022 Month: 01 X-DOI: 10.1080/23270012.2021.1931495 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1931495 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:1:p:86-119 Template-Type: ReDIF-Article 1.0 Author-Name: Chandan Mahato Author-X-Name-First: Chandan Author-X-Name-Last: Mahato Author-Name: Gour Chandra Mahata Author-X-Name-First: Gour Chandra Author-X-Name-Last: Mahata Title: Decaying items inventory models with partial linked-to-order upstream trade credit and downstream full trade credit Abstract: In practice, any member of supply chain may offer full or partial trade credit contract to his downstream level. Full trade credit is the case that the latter is allowed to defer whole payment to the end of credit period. In partial trade credit, however, the downstream supply chain member must pay for a proportion of the purchased goods at first, and can delay paying for the rest until the end of credit period. This paper considers a two-level trade credit, where the supplier offers order-quantity-dependent partial trade credit to a retailer, who suggests full trade credit to his customers. An economic order quantity (EOQ) inventory model of a deteriorating item with expiration dates is formulated here. Theoretical results are developed to obtain the optimal solutions to the problem. Numerical examples and sensitivity analysis are performed to justify the proposed models and theoretical results and managerial insights are provided. Journal: Journal of Management Analytics Pages: 137-168 Issue: 1 Volume: 9 Year: 2022 Month: 01 X-DOI: 10.1080/23270012.2021.1995514 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1995514 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:1:p:137-168 Template-Type: ReDIF-Article 1.0 Author-Name: M. Palanivel Author-X-Name-First: M. Author-X-Name-Last: Palanivel Author-Name: M. Suganya Author-X-Name-First: M. Author-X-Name-Last: Suganya Title: Partial backlogging inventory model with price and stock level dependent demand, time varying holding cost and quantity discounts Abstract: This article deals with an increasing total profit for inventory optimal ordered quantity and partial backlogging with the holding cost depending on the storage time period, and the rate of market demand is assumed to fluctuate as a function, based on level of stock and selling price. Thereafter, using the concept of a Hessian matrix, we have proved the concave nature of the profit function for the case where maximum cost is obtained. Finally, in order to validate the derived models, numerical examples and sensitivity analysis are explained. Through numerical test, we show that the proposed algorithms give quite satisfactory solutions. Hence, it can be concluded that the total profit can be increased by allowing shortage and partial backlogging. Journal: Journal of Management Analytics Pages: 32-59 Issue: 1 Volume: 9 Year: 2022 Month: 01 X-DOI: 10.1080/23270012.2021.1887771 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1887771 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:1:p:32-59 Template-Type: ReDIF-Article 1.0 Author-Name: Changlin Zeng Author-X-Name-First: Changlin Author-X-Name-Last: Zeng Author-Name: Zhaopu Zhang Author-X-Name-First: Zhaopu Author-X-Name-Last: Zhang Author-Name: Tingting Zeng Author-X-Name-First: Tingting Author-X-Name-Last: Zeng Title: Cooperative mechanism on a three-echelon supply chain with remanufacturing outsourcing Abstract: This paper studies the cooperative mechanism for a three-echelon supply chain with remanufacturing outsourcing comparing a supplier, a manufacturer, and a third-party remanufacturer, wherein we take the relative fairness concerns into consideration. The Stackelberg game theory is introduced to analyze the best values for the supply chain and each member. Nash bargaining solution is used as the relative fairness-concerned reference to discuss the corresponding optimal solutions of these models. By determining and comparing the equilibrium solutions across the five models, we discover that given the Nash bargaining fairness-concerned behavior, the system profits in the completely decentralized and three cooperative scenarios are lower than they are for products in the completely centralized decision model. The results show that in the centralized channel, the optimal profit and market demand in the three-echelon supply chain are maximized. Furthermore, it turns out that a cooperative mechanism can bring great benefits to its performance. Journal: Journal of Management Analytics Pages: 185-210 Issue: 2 Volume: 9 Year: 2022 Month: 04 X-DOI: 10.1080/23270012.2021.1995515 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1995515 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:2:p:185-210 Template-Type: ReDIF-Article 1.0 Author-Name: Gaurav Nagpal Author-X-Name-First: Gaurav Author-X-Name-Last: Nagpal Author-Name: Udayan Chanda Author-X-Name-First: Udayan Author-X-Name-Last: Chanda Title: Optimal inventory policies for short life cycle successive generations’ technology products Abstract: In this paper, a new Economic Order Quantity (EOQ) model for a successive generation of technology products has been discussed. The classical EOQ model is based on the assumption that the demand rate is constant. Hence it cannot be used for technology products where competition-substitution among products is a usual phenomenon. To address this problem, the EOQ model proposed in this article is considered a demand model for a technology product that follows the innovation-diffusion process. A numerical example has been illustrated and a comprehensive sensitivity analysis is conducted to understand the path of the optimal planning horizon and optimal costs under varied innovation and imitation effect. The sensitivity analysis of the introduction timing of the second generation has been performed to know the applicability of the model in actual circumstances. The behavior of the model has been discussed in detail in the numerical illustration section. Journal: Journal of Management Analytics Pages: 261-286 Issue: 2 Volume: 9 Year: 2022 Month: 04 X-DOI: 10.1080/23270012.2021.1881922 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1881922 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:2:p:261-286 Template-Type: ReDIF-Article 1.0 Author-Name: Liuliu Fu Author-X-Name-First: Liuliu Author-X-Name-Last: Fu Author-Name: Ling Li Author-X-Name-First: Ling Author-X-Name-Last: Li Author-Name: Lusi Li Author-X-Name-First: Lusi Author-X-Name-Last: Li Author-Name: Wenlu Zhang Author-X-Name-First: Wenlu Author-X-Name-Last: Zhang Author-Name: Zihao Luo Author-X-Name-First: Zihao Author-X-Name-Last: Luo Title: Impact of hospital size on healthcare information system effectiveness: evidence from healthcare data analytics Abstract: With the rapid development of information technology, the increasing use of mobile digital devices and efforts from the whole society, the healthcare information systems (HISs) are moving towards a new era. However, there is still a lack of clear understanding of the benefits of HIS at the hospital level and the influential factors for HIS effectiveness. In this study, we propose a research framework to explain how HIS implementation improves hospital performance. Our results reinforce the positive effect of HIS on hospital performance. In particular, we found that HIS implementation increases both the cost and revenue of the hospitals, but the increasing effect in revenue is much bigger than the increasing effect in cost. We also found that although both small and big hospitals benefit from the implementation of HIS, the effect of size is different. Size has a positive effect on hospital performance for small hospitals but has a negative effect on big hospitals. This indicates that the competitive advantage of economies of scale disappears for big hospitals because the level of information transparency becomes lower and transaction costs become higher as size increases. Journal: Journal of Management Analytics Pages: 211-231 Issue: 2 Volume: 9 Year: 2022 Month: 04 X-DOI: 10.1080/23270012.2022.2036647 File-URL: http://hdl.handle.net/10.1080/23270012.2022.2036647 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:2:p:211-231 Template-Type: ReDIF-Article 1.0 Author-Name: Sudip Adak Author-X-Name-First: Sudip Author-X-Name-Last: Adak Author-Name: G. S. Mahapatra Author-X-Name-First: G. S. Author-X-Name-Last: Mahapatra Title: Two-echelon imperfect production supply chain with probabilistic deterioration rework and reliability under fuzziness Abstract: This paper develops an integrated fuzzy two-layer supply chain for manufacturers and retailers with defective and non-defective types of products. The manufacturer produces up to a specific time, including both faulty and non-defective items, and post-screening only the non-defective item sends to the retailer. The strategy of the retailer is to do the screening of items received from the manufacturer. Subsequently, the perfect quality items use to fulfill the demand of the customer, and the defective items are reworked. The retailer considers that customer demand is time and reliability dependent. The proposed supply chain considers probabilistic deterioration for the manufacturer and retailers. The optimum solution of the given model is evaluated for both the cases of crisp and fuzzy environments on the strategies such as production rate, unit production cost, idle time, screening, rework, etc. Managerial insights and the effect of parameters on the optimal inventory under fuzziness are presented. Journal: Journal of Management Analytics Pages: 287-311 Issue: 2 Volume: 9 Year: 2022 Month: 04 X-DOI: 10.1080/23270012.2021.1882347 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1882347 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:2:p:287-311 Template-Type: ReDIF-Article 1.0 Author-Name: K. V. Geetha Author-X-Name-First: K. V. Author-X-Name-Last: Geetha Author-Name: M. Prabha Author-X-Name-First: M. Author-X-Name-Last: Prabha Title: Effective inventory management using postponement strategy with fuzzy cost Abstract: In this article, we consider a two level supply chain to evaluate the impact of postponement strategy on the retailer. Here the cost parameters are fuzzified. Signed distance method is used to defuzzify and to obtain the estimation of the total cost in the fuzzy sense. The common variable production cost, common fixed cost and the common unit holding cost per unit time are assumed to be fuzzy in nature. Inventory models are formulated for postponement system and independent system such that the total average inventory cost function per unit time is minimized. Algorithms are given to derive the optimal solutions of the proposed model. Theoretical analysis and the computational procedure helps to study the impact of deterioration rate on the optimal inventory policies. A comparative study between the postponement system and independent system considering fuzzy costs is also made. Journal: Journal of Management Analytics Pages: 232-260 Issue: 2 Volume: 9 Year: 2022 Month: 04 X-DOI: 10.1080/23270012.2021.1881923 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1881923 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:2:p:232-260 Template-Type: ReDIF-Article 1.0 Author-Name: Ping-Hung Hsieh Author-X-Name-First: Ping-Hung Author-X-Name-Last: Hsieh Title: Exploratory analysis of grocery product networks Abstract: Finding meaningful sets of co-purchased products allows retailers to manage inventory better and develop market strategies. Analyzing the baskets of products, known as market basket analysis, is typically carried out using association rule mining or community detection approach. This article uses both methods to investigate a transaction dataset collected from a brick-and-mortar grocery store. The findings reveal interesting purchasing patterns of local residents and prompt us to consider dynamic modeling of the product network in the future. Journal: Journal of Management Analytics Pages: 169-184 Issue: 2 Volume: 9 Year: 2022 Month: 04 X-DOI: 10.1080/23270012.2022.2072779 File-URL: http://hdl.handle.net/10.1080/23270012.2022.2072779 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:2:p:169-184 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2113464_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949 Author-Name: Larissa Adamiec Author-X-Name-First: Larissa Author-X-Name-Last: Adamiec Author-Name: Deborah Cernauskas Author-X-Name-First: Deborah Author-X-Name-Last: Cernauskas Author-Name: Andrew Kumiega Author-X-Name-First: Andrew Author-X-Name-Last: Kumiega Title: Understanding the indicative factors of university/college closings Abstract: Higher education has been in a financially precarious position for many years – facing either a total transformation or elimination. Tuition increases and fewer college-age students from shifting demographics are primary reasons for the financial distress. Alternative financial stability models have assumed linear variable relationships and improperly calculate the probability of default. Stakeholders have historically relied upon models such as those developed by Edmit and the Department of Education which are inadequate at separating financially sound from unsound universities. We used an Automated Machine Learning approach utilizing multiple models to explain the relationship between metrics and the probability of default/closure allowing for more informed managerial decisions. This research, although applied to the homogeneous group of small liberal arts universities, can be applied to online and state universities and will allow the opportunity to take preventive steps to mitigate the likelihood of closing due to financial distress. Journal: Journal of Management Analytics Pages: 330-350 Issue: 3 Volume: 9 Year: 2022 Month: 07 X-DOI: 10.1080/23270012.2022.2113464 File-URL: http://hdl.handle.net/10.1080/23270012.2022.2113464 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:3:p:330-350 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2117000_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949 Author-Name: Jun Ye Author-X-Name-First: Jun Author-X-Name-Last: Ye Title: MCGDM model using uncertain linguistic consistency sets of single and interval linguistic term multivalued sets Abstract: This paper presents hybrid linguistic expressions and operations for the hybrid linguistic multiple criteria group decision making (MCGDM) issue with identical and/or different single and interval linguistic term values. First, we propose a single and interval linguistic term multivalued set/element (SILTMS/SILTME) and develop a consistency measure of SILTMEs based on Shannon entropy to measure the consistency degree of single and interval linguistic term values in SILTME. Second, we converse SILTMS/SILTME into an uncertain linguistic consistency set/element (ULCS/ULCE) in terms of the mean and consistency measure of SILTMEs to reasonably perform operations between different information forms/sequence lengths in SILTMSs. Third, we define some operations of ULCEs and the expected values and sorting rules of ULCEs. Fourth, we present the ULCE weighted mean and geometric operators and their characteristics. Finally, we develop a MCGDM model using the weighted mean operation of the two operators and apply it in the mine safety assessment. Journal: Journal of Management Analytics Pages: 351-368 Issue: 3 Volume: 9 Year: 2022 Month: 07 X-DOI: 10.1080/23270012.2022.2117000 File-URL: http://hdl.handle.net/10.1080/23270012.2022.2117000 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:3:p:351-368 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2113161_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949 Author-Name: Maryam Abdirad Author-X-Name-First: Maryam Author-X-Name-Last: Abdirad Author-Name: Krishna Krishnan Author-X-Name-First: Krishna Author-X-Name-Last: Krishnan Author-Name: Deepak Gupta Author-X-Name-First: Deepak Author-X-Name-Last: Gupta Title: Three-stage algorithms for the large-scale dynamic vehicle routing problem with industry 4.0 approach Abstract: Companies are eager to have a smart supply chain especially when they have a dynamic system. Industry 4.0 is a concept which concentrates on mobility and real-time integration. Thus, it can be considered as a necessary component that has to be implemented for a dynamic vehicle routing problem. The aim of this research is to solve large-scale DVRP (LSDVRP) in which the delivery vehicles must serve customer demands from a common depot to minimize transit costs while not exceeding the capacity constraint of each vehicle. In LSDVRP, it is difficult to get an exact solution and the computational time complexity grows exponentially. To find near-optimal answers for this problem, a hierarchical approach consisting of three stages: “clustering, route-construction, route-improvement” is proposed. The major contribution of this paper is dealing with LSDVRP to propose the three-stage algorithm with better results. The results confirmed that the proposed methodology is applicable. Journal: Journal of Management Analytics Pages: 313-329 Issue: 3 Volume: 9 Year: 2022 Month: 07 X-DOI: 10.1080/23270012.2022.2113161 File-URL: http://hdl.handle.net/10.1080/23270012.2022.2113161 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:3:p:313-329 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2113160_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949 Author-Name: Alisa Ableeva Author-X-Name-First: Alisa Author-X-Name-Last: Ableeva Author-Name: Guzel Salimova Author-X-Name-First: Guzel Author-X-Name-Last: Salimova Author-Name: Tatiana Lubova Author-X-Name-First: Tatiana Author-X-Name-Last: Lubova Author-Name: Almira Farrahetdinova Author-X-Name-First: Almira Author-X-Name-Last: Farrahetdinova Author-Name: Raisa Siraeva Author-X-Name-First: Raisa Author-X-Name-Last: Siraeva Title: Evaluation of the efficiency of fixed assets of economic sectors based on index analysis Abstract: This article is devoted to the study of the system of indicators characterizing the volume, structure and efficiency of the functioning of fixed assets of the production and non-production areas of the region based on the index analysis. Through factorial index analysis, models of the relationship between the gross regional product, the cost of fixed assets and capital productivity (capital intensity) of industries producing goods and providing services were obtained, to identify reserves for increasing of the analyzed indicators. The obtained results indicate a trend towards significant decrease in capital productivity in the production sector, which should become the basis for making managerial decisions at the regional level to attract investment. They can be used in the practice of macroeconomic analysis and forecasting, for detailing the factors for determining the potential of economic sectors, the efficiency of using fixed assets, and attracting investments. Journal: Journal of Management Analytics Pages: 369-382 Issue: 3 Volume: 9 Year: 2022 Month: 07 X-DOI: 10.1080/23270012.2022.2113160 File-URL: http://hdl.handle.net/10.1080/23270012.2022.2113160 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:3:p:369-382 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_1882348_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949 Author-Name: Prasanta Kumar Ghosh Author-X-Name-First: Prasanta Kumar Author-X-Name-Last: Ghosh Author-Name: Amalesh Kumar Manna Author-X-Name-First: Amalesh Kumar Author-X-Name-Last: Manna Author-Name: Jayanta Kumar Dey Author-X-Name-First: Jayanta Kumar Author-X-Name-Last: Dey Author-Name: Samarjit Kar Author-X-Name-First: Samarjit Author-X-Name-Last: Kar Title: An EOQ model with backordering for perishable items under multiple advanced and delayed payments policies Abstract: This study investigated an economic order quantity (EOQ) model with complete backorder for fixed lifetime perishable items under multiple advance and delayed payments policies. Here, a new type of business policy is considered where supplier offers the retailer to pay a fraction of the purchasing cost before the order delivery by multiple equal installments starting from the ordering time and the rest amount after the delivery by multiple equal installments. Here, some theoretical results are illustrated to determine the conditions of existence and uniqueness of the optimal solutions. A closed form solution is determined to solve the proposed model under approximation. Some numerical examples are provided to examine the validity of the proposed model. Finally, sensitivity analyses are presented to obtain the effect of optimal policy and provide some managerial insights of the model. Journal: Journal of Management Analytics Pages: 403-434 Issue: 3 Volume: 9 Year: 2022 Month: 07 X-DOI: 10.1080/23270012.2021.1882348 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1882348 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:3:p:403-434 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2089064_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949 Author-Name: Zhiwei Ye Author-X-Name-First: Zhiwei Author-X-Name-Last: Ye Author-Name: Yang Lu Author-X-Name-First: Yang Author-X-Name-Last: Lu Title: Quantum science: a review and current research trends Abstract: Quantum science is accelerating the transition from research to industrialized applications and scenarios, and its potential disruptive power in the development of future technological transformation, operational modes, and economy is emerging. In this study, we describe the state of the art of quantum science, and we attempt to provide an overview of quantum mechanics and its relevant prospects. On the other hand, we employ a certain tool (Biblioshiny from R Project) to analyze the relevant articles from Web of Science (WoS). The analysis shows that quantum science is an interdisciplinary field that is attracting more and more attention from both academia and practice. The application of quantum computer needs more time to be realized, it is potential to improve and change the whole society in many aspects. Journal: Journal of Management Analytics Pages: 383-402 Issue: 3 Volume: 9 Year: 2022 Month: 07 X-DOI: 10.1080/23270012.2022.2089064 File-URL: http://hdl.handle.net/10.1080/23270012.2022.2089064 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:3:p:383-402 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2113159_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949 Author-Name: Huaige Zhang Author-X-Name-First: Huaige Author-X-Name-Last: Zhang Author-Name: Xianpei Hong Author-X-Name-First: Xianpei Author-X-Name-Last: Hong Author-Name: Menghuan Zhou Author-X-Name-First: Menghuan Author-X-Name-Last: Zhou Title: Optimal technology licensing contract with quality improvement innovation under Cournot competition Abstract: This paper analyzes the optimal licensing contract for the patentor with a quality improvement innovation in a Cournot duopoly market. We examine and compare three licensing contracts (fixed-fee licensing, royalty licensing, and two-part ad valorem licensing) in terms of the patent-holding firm’s profit, consumer surplus, and social welfare. We also study the impact of quality differences on the choice of licensing contract. One might expect that consumer surplus and social welfare are greater under fixed-fee licensing. However, we show that this conclusion seems to be untrue under quality improvement technology licensing. Moreover, we find that (1) there exists a threshold for the degree of quality difference above which fixed-fee licensing will be listed for the consideration of the patent-holding firm and below which it will be abandoned; (2) royalty licensing and two-part ad valorem licensing are always profitable for the patent-holding firm and two-part ad valorem licensing brings the patentee the most profit. Journal: Journal of Management Analytics Pages: 496-513 Issue: 4 Volume: 9 Year: 2022 Month: 10 X-DOI: 10.1080/23270012.2022.2113159 File-URL: http://hdl.handle.net/10.1080/23270012.2022.2113159 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:4:p:496-513 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2089063_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949 Author-Name: Mengmeng Wang Author-X-Name-First: Mengmeng Author-X-Name-Last: Wang Author-Name: Xiaojing Feng Author-X-Name-First: Xiaojing Author-X-Name-Last: Feng Title: Price squeeze under fairness: the road to supply chain coordination with a powerful retailer Abstract: This research investigates the impacts of the manufacturer’s fairness concerns on the supply chain performance when the power retailer implements a price squeeze and market service investment together. Through game-theoretic modeling, we find that 1) in the absence of fairness, although the manufacturer may be worse off due to possessing imperfect information on the price squeeze rate, the channel may be coordinated through an ex-ante negotiation between the two parties. 2) When the manufacturer has fairness concerns for price squeeze, both channel performance and brand goodwill are made worse by disadvantageous inequality and improved by advantageous inequality versus the case of no fairness concerns. Furthermore, channel members’ ex-ante negotiations regarding a profit reallocation scheme under certain conditions may achieve the following three objectives: generating a channel profit of the coordination level, promoting brand goodwill to the level of the integrated channel, and creating an equitable channel relationship. Journal: Journal of Management Analytics Pages: 448-479 Issue: 4 Volume: 9 Year: 2022 Month: 10 X-DOI: 10.1080/23270012.2022.2089063 File-URL: http://hdl.handle.net/10.1080/23270012.2022.2089063 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:4:p:448-479 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2156303_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949 Author-Name: Fei Liu Author-X-Name-First: Fei Author-X-Name-Last: Liu Author-Name: Yijing Li Author-X-Name-First: Yijing Author-X-Name-Last: Li Author-Name: Xiaofei Song Author-X-Name-First: Xiaofei Author-X-Name-Last: Song Author-Name: Zhao Cai Author-X-Name-First: Zhao Author-X-Name-Last: Cai Author-Name: Eric T. K. Lim Author-X-Name-First: Eric T. K. Author-X-Name-Last: Lim Author-Name: Chee-Wee Tan Author-X-Name-First: Chee-Wee Author-X-Name-Last: Tan Title: Effects of age on live streaming viewer engagement: a dual coding perspective Abstract: Though the emerging live streaming industry has attracted growing attention, the dominant yanzhi category where streamers mostly interact with the audience through amateur talent shows and casual chats has not been widely investigated. To decode the mechanism behind the popularity of yanzhi streamers, this study draws on Dual Coding Theory (DCT) to posit that age estimated from a streamer’s face and voice can influence the level of viewer engagement. To validate our hypothesized relationships, 274 one-minute video records ahead of a viewer commenting or/and gifting were collected and analyzed via deep learning algorithms. Analytical results attest to the negative effects of both facial and vocal age on viewer engagement, while their interaction has a positive impact on viewer engagement. Journal: Journal of Management Analytics Pages: 435-447 Issue: 4 Volume: 9 Year: 2022 Month: 10 X-DOI: 10.1080/23270012.2022.2156303 File-URL: http://hdl.handle.net/10.1080/23270012.2022.2156303 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:4:p:435-447 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2051153_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949 Author-Name: B. Karthick Author-X-Name-First: B. Author-X-Name-Last: Karthick Author-Name: R. Uthayakumar Author-X-Name-First: R. Author-X-Name-Last: Uthayakumar Title: A two-tier supply chain model under two distributions with MTTF, rework, variable production rate and lead time Abstract: This article considers the two-level supply chain model incorporating an imperfect production process under a variable lead time. The cost of producing a unit item is calculated as a function of the production rate. In addition, two alternative production functions (linear and quadratic functions) are used to relate process quality and production rate. Lead time demand follows two different distributions, based on which two mathematical formulations are described in this paper. In the first model, the lead time demand follows a normal distribution. In the second model, the lead time demand doesn't follow any particular distribution, but the mean and the standard deviation are known. The lead time length is minimized by incorporating the lead time crashing cost. This research aims to analyze the optimized total cost of the supply chain under two different distributions. Journal: Journal of Management Analytics Pages: 532-558 Issue: 4 Volume: 9 Year: 2022 Month: 10 X-DOI: 10.1080/23270012.2022.2051153 File-URL: http://hdl.handle.net/10.1080/23270012.2022.2051153 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:4:p:532-558 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2073571_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949 Author-Name: R. Udayakumar Author-X-Name-First: R. Author-X-Name-Last: Udayakumar Title: An EOQ model for non-instantaneous deteriorating items with time-dependent demand under partial backlogging Abstract: This article discusses an economic order quantity model for non-instantaneous deteriorating items in which the demand is assumed to be a linear function of time over an infinite planning horizon. In addition, the salvage value associated with the deteriorated units is also considered. The shortages are allowed and partially backlogged. A mathematical model is framed to obtain the replenishment policy which aids the retailer to minimize the total inventory cost. The objective of this work is to minimize the total inventory cost and to find the optimal length of replenishment and the optimal order quantity. The theory developed in this article is illustrated using numerical examples. A computational algorithm is designed to find the optimal solution. Sensitivity analysis is carried out to study the changes in the effect on the optimal solutions and some managerial insights are obtained. Journal: Journal of Management Analytics Pages: 514-531 Issue: 4 Volume: 9 Year: 2022 Month: 10 X-DOI: 10.1080/23270012.2022.2073571 File-URL: http://hdl.handle.net/10.1080/23270012.2022.2073571 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:4:p:514-531 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_1944350_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949 Author-Name: Rita Yadav Author-X-Name-First: Rita Author-X-Name-Last: Yadav Author-Name: Sarla Pareek Author-X-Name-First: Sarla Author-X-Name-Last: Pareek Author-Name: Mandeep Mittal Author-X-Name-First: Mandeep Author-X-Name-Last: Mittal Author-Name: Mahesh Kumar Jayaswal Author-X-Name-First: Mahesh Kumar Author-X-Name-Last: Jayaswal Title: Two-level supply chain models with imperfect quality items when demand influences price and marketing promotion Abstract: The present paper studies a supply chain model with items that are of imperfect quality and with the assumption that end demand is responsive to price and promotion cost. The seller delivers items to the buyer in a lot. After an inspection process, it is observed that few articles produced are not of perfect quality. These defects might be the result of common operations or static maintenance. These defective items are then collected and are sold at a lower/discounted price. In this paper, supply chain models are developed to approve the interaction among the players, in the supply chain channel. This interaction between the players is demonstrated by non-cooperative and cooperative game theoretical approaches. In non-cooperative approach, optimal solutions are attained by game theoretic approaches named as Seller–Stackelberg and Buyer–Stackelberg. In the cooperative approach, a Pareto efficient solution is outlined. In the last, numerical illustrations with sensitivity scrutiny are presented to support the theory of the present paper. Journal: Journal of Management Analytics Pages: 480-495 Issue: 4 Volume: 9 Year: 2022 Month: 10 X-DOI: 10.1080/23270012.2021.1944350 File-URL: http://hdl.handle.net/10.1080/23270012.2021.1944350 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:9:y:2022:i:4:p:480-495 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2030255_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20 Author-Name: B. Malleeswaran Author-X-Name-First: B. Author-X-Name-Last: Malleeswaran Author-Name: R. Uthayakumar Author-X-Name-First: R. Author-X-Name-Last: Uthayakumar Title: A single-manufacturer multi-retailer sustainable reworking model for green and environmental sensitive demand under discrete ordering cost reduction Abstract: This paper develops an economic production quantity (EPQ) model for a single-manufacturer multi-retailer (SMMR) production and reworking system with green and environmental sensitive customer demand. The minimum cost of the manufacturer has obtained under carbon emissions (CE) policies and discrete ordering cost reduction. The model is used to optimize the total number of shipments, greening investment level, environmental measure, and lot size for productions and rework. This research work determines that the manufacturer's and retailer's profits will be increased after considering the environmental and green dependent demand of customers. Further, the development of green and environmental demand is proposed to minimize the CE and maximize the demand for the customers. In the existing literature, no discrete investment is developed for reducing the cost of ordering for the retailer/buyer. However, in this paper, we have introduced it. We provide numerical examples to explain the models and determine the significance of model parameters. Journal: Journal of Management Analytics Pages: 109-128 Issue: 1 Volume: 10 Year: 2023 Month: 01 X-DOI: 10.1080/23270012.2022.2030255 File-URL: http://hdl.handle.net/10.1080/23270012.2022.2030255 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:1:p:109-128 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2179432_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20 Author-Name: G. Durga Bhavani Author-X-Name-First: G. Durga Author-X-Name-Last: Bhavani Author-Name: G. S. Mahapatra Author-X-Name-First: G. S. Author-X-Name-Last: Mahapatra Author-Name: Akhilesh Kumar Author-X-Name-First: Akhilesh Author-X-Name-Last: Kumar Title: An integrated fuzzy production inventory model for manufacturer–retailer coordination under simple carbon tax system Abstract: This paper develops twin models towards integrated production inventory planning for manufacturer–retailer ecosystem in a sustainable supply chain setup. Decision-making models are developed in fuzzy environment and under purview of carbon taxation system. Novel conception of Fermatean fuzzy numbers is introduced for handling parameters imprecision. The first model addresses planning problem without considering green investments, whereas the second one additionally identifies optimal green investments for each player of ecosystem. Models are formulated as nonlinear optimization problems with objective of maximizing profit. Comparison of results from both models enables decision-makers to figure out the profitability of green investment option. Numerical instance with data from the existing literature is solved using Mathematica 12.1. Computational results for studied case report profitability of green investments for supply chain partners and significant reduction in carbon emissions as well. Variation analysis demonstrates stability of the proposed model. Developed models equip small-scale retailer-manufacture tie-ups prevalent in developing economies for discussed decisions. Journal: Journal of Management Analytics Pages: 38-88 Issue: 1 Volume: 10 Year: 2023 Month: 01 X-DOI: 10.1080/23270012.2023.2179432 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2179432 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:1:p:38-88 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2089062_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20 Author-Name: Ran Jiang Author-X-Name-First: Ran Author-X-Name-Last: Jiang Author-Name: Laijun Zhao Author-X-Name-First: Laijun Author-X-Name-Last: Zhao Author-Name: Lei Guo Author-X-Name-First: Lei Author-X-Name-Last: Guo Author-Name: Qin Wang Author-X-Name-First: Qin Author-X-Name-Last: Wang Author-Name: Yujing Xie Author-X-Name-First: Yujing Author-X-Name-Last: Xie Author-Name: Jian Xue Author-X-Name-First: Jian Author-X-Name-Last: Xue Title: A Stackelberg game model with tax for regional air pollution control Abstract: The command-and-control regulation is likely inefficient and costly. This study investigates a regional pollution control scheme with tax (RPCST) under which the central government sets the tax rate under a given pollutant reduction quota and local governments determine their pollution removal rates based on the central government’s policy. First, a one-leader-multi-follower (OLMF) Stackelberg game model is formulated, in which the central government is the leader and the local governments are the followers. Then, a procedure based on bilevel programming and relaxation method is applied to solve the OLMF model. Finally, a case study analyzing the SO2 reduction of the Yangtze River Delta in China is conducted to demonstrate the effectiveness of the RPCST. The results show that RPCST works better than the current command-and-control scheme. Our analysis provides a guideline for governments to design optimal tax schemes to effectively solve the regional air pollution crisis. Journal: Journal of Management Analytics Pages: 1-21 Issue: 1 Volume: 10 Year: 2023 Month: 01 X-DOI: 10.1080/23270012.2022.2089062 File-URL: http://hdl.handle.net/10.1080/23270012.2022.2089062 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:1:p:1-21 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2179431_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20 Author-Name: Falguni Mahato Author-X-Name-First: Falguni Author-X-Name-Last: Mahato Author-Name: Mukunda Choudhury Author-X-Name-First: Mukunda Author-X-Name-Last: Choudhury Author-Name: Gour Chandra Mahata Author-X-Name-First: Gour Chandra Author-X-Name-Last: Mahata Title: Inventory models for deteriorating items with fixed lifetime, partial backordering and carbon emissions policies Abstract: In this study, a sustainable inventory model is devised to obtain the retailer’s optimal pricing and replenishment policies for degrading items having a certain lifetime incorporating partial back order as a shortage and dynamic demand under two different scenarios (a) carbon cap and trade policy (b) carbon tax policy. The primary objective of this study is to maximize the retailer’s annual total profit. The retailer’s profit function has been optimized with the help of convexity/concavity criteria employing classical optimization techniques. Based on a real case study, two different numerical examples and corresponding optimal solutions have been shown for both models with the help of Lingo 17 software. Moreover, the impact of the major inventory parameters and prominent managerial insights are presented for the robustness of the proposed model that can cooperate with industrial managers/decision-makers for the overall improvement of his/her industry to take effective and qualitative decisions. Journal: Journal of Management Analytics Pages: 129-190 Issue: 1 Volume: 10 Year: 2023 Month: 01 X-DOI: 10.1080/23270012.2023.2179431 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2179431 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:1:p:129-190 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2073570_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20 Author-Name: Totan Garai Author-X-Name-First: Totan Author-X-Name-Last: Garai Author-Name: Arpita Paul Author-X-Name-First: Arpita Author-X-Name-Last: Paul Title: The effect of supply disruption in a two-layer supply chain with one retailer and two suppliers with promotional effort under random demand Abstract: This paper considers a closed-loop supply chain comprising one retailer and two suppliers. It assumes one supplier as the main supplier and another as the backup. The coordination issue of the supply chain has been discussed in this work. The main supplier's yield is considered subject to disruption. The demand considered here is stochastic. We aim to calculate the supplier's optimum production quantity. Similarly, the retailer's optimal ordering quantity is found out. Additionally, in the centralized supply chain model, we want to maximize the expected profit under certain restriction. Numerical illustrations are discussed to the benefit some characteristic insights over the supply chain model. Journal: Journal of Management Analytics Pages: 22-37 Issue: 1 Volume: 10 Year: 2023 Month: 01 X-DOI: 10.1080/23270012.2022.2073570 File-URL: http://hdl.handle.net/10.1080/23270012.2022.2073570 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:1:p:22-37 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2180676_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20 Author-Name: Weiru Chen Author-X-Name-First: Weiru Author-X-Name-Last: Chen Author-Name: Wu He Author-X-Name-First: Wu Author-X-Name-Last: He Author-Name: Jiayue Shen Author-X-Name-First: Jiayue Author-X-Name-Last: Shen Author-Name: Xin Tian Author-X-Name-First: Xin Author-X-Name-Last: Tian Author-Name: Xianping Wang Author-X-Name-First: Xianping Author-X-Name-Last: Wang Title: Systematic analysis of artificial intelligence in the era of industry 4.0 Abstract: Artificial Intelligence has been playing a profound role in the global economy, social progress, and people’s daily life. With the increasing capabilities and accuracy of AI, the application of AI will have more impacts on manufacturing and service areas in the era of industry 4.0. This study conducts a systematic literature review to study the state-of-the-art on AI in industry 4.0. This paper describes the development of industries and the evolution of AI. This paper also identifies that the development and application of AI will bring not only opportunities but also challenges to industry 4.0. The findings provide a valuable reference for researchers and practitioners through a multi-angle systematic analysis of AI. In the era of industry 4.0, AI system will become an innovative and revolutionary assistance to the whole industry. Journal: Journal of Management Analytics Pages: 89-108 Issue: 1 Volume: 10 Year: 2023 Month: 01 X-DOI: 10.1080/23270012.2023.2180676 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2180676 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:1:p:89-108 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2179430_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20 Author-Name: Amirhossein Dehkhodaei Author-X-Name-First: Amirhossein Author-X-Name-Last: Dehkhodaei Author-Name: Bahar Amiri Author-X-Name-First: Bahar Author-X-Name-Last: Amiri Author-Name: Hasan Farsijani Author-X-Name-First: Hasan Author-X-Name-Last: Farsijani Author-Name: Abbas Raad Author-X-Name-First: Abbas Author-X-Name-Last: Raad Title: Barriers to big data analytics (BDA) implementation in manufacturing supply chains Abstract: Big data analysis (BDA) can increase the capability of supply chain analysis of manufacturing companies. Therefore, many manufacturing companies want to use BDA, but it has been seen that BDA implementation is difficult, especially in developing countries due to the existence of various barriers related to finance, government regulations, etc. This paper aims to investigate the barriers to BDA implementation in Iranian companies. In literature, limited work has been done on identifying barriers to implementing BDA in developing countries. In this regard, 34 barriers were identified to BDA adoption in Iran by employing a literature review and feedback received from experts. Then, the most important barriers (14) were analyzed using integrated Interpretive Structural Modeling and MICMAC approach. Results show that two barriers; namely, lack of sufficient knowledge of senior managers and weakness of governance policies, are the most significant. Finally, crucial policy measures and recommendations are proposed to assist managers and government bodies. Journal: Journal of Management Analytics Pages: 191-222 Issue: 1 Volume: 10 Year: 2023 Month: 01 X-DOI: 10.1080/23270012.2023.2179430 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2179430 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:1:p:191-222 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2211073_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20 Author-Name: Mohammad Rahiminia Author-X-Name-First: Mohammad Author-X-Name-Last: Rahiminia Author-Name: Sareh Shahrabifarahani Author-X-Name-First: Sareh Author-X-Name-Last: Shahrabifarahani Author-Name: Zahra Mojaradi Author-X-Name-First: Zahra Author-X-Name-Last: Mojaradi Author-Name: Amir Aghsami Author-X-Name-First: Amir Author-X-Name-Last: Aghsami Author-Name: Fariborz Jolai Author-X-Name-First: Fariborz Author-X-Name-Last: Jolai Title: A queueing-inventory model to control the congestion of patients and medical waste in the medical centers, a case study Abstract: During epidemics, controlling the patients’ congestion is a way to reduce disease spreading. Raising medical demands converts hospitals into one of the sources of disease outbreaks. The long patient waiting time in queues to receive medical services leads to more casualties. The rise of patients increases their waste, which is another source of disease outbreak. In this study, a mathematical model is developed to control patients’ congestion in a medical center and manage their waste, considering environmental issues. Besides a queueing system controlling the patients’ congestion in the treatment center, another queue is considered for vehicles. An inventory model is employed to prevent waste accumulation. The developed model is solved and reaches an exact solution in small size, and obtains an acceptable solution in large size using the Grasshopper algorithm. A case study is considered to demonstrate the model’s applicability. Also, Sensitivity analysis and valuable managerial insights are presented. Journal: Journal of Management Analytics Pages: 416-445 Issue: 2 Volume: 10 Year: 2023 Month: 04 X-DOI: 10.1080/23270012.2023.2211073 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2211073 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:2:p:416-445 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2211081_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20 Author-Name: Biswarup Samanta Author-X-Name-First: Biswarup Author-X-Name-Last: Samanta Author-Name: Bibhas C. Giri Author-X-Name-First: Bibhas C. Author-X-Name-Last: Giri Author-Name: Kripasindhu Chaudhuri Author-X-Name-First: Kripasindhu Author-X-Name-Last: Chaudhuri Title: A supply chain model with two competitive buyers under a hybrid greening cost and revenue-sharing contract Abstract: This article considers a vendor-buyer supply chain model where a single vendor produces a single product and markets it through two competitive buyers to a group of customers. The customer demand for the product depends on the selling price, green level and warranty period of the product. Successive deliveries from the vendor are scheduled at a fixed time interval wherein the subsequent shipments appear when each of the buyer’s inventories from the former delivery has just been cleared out. A hybrid greening cost and revenue sharing (HGRS) contract is introduced, which provides more profit to individual members than their decentralised profits. The numerical study reveals that, under the HGRS contract, customers are influenced to buy a more reliable product at a reasonable price with a higher green level. A sensitivity analysis is also carried out to examine the impact of key model parameters on the optimal results. Journal: Journal of Management Analytics Pages: 270-307 Issue: 2 Volume: 10 Year: 2023 Month: 04 X-DOI: 10.1080/23270012.2023.2211081 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2211081 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:2:p:270-307 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2187716_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20 Author-Name: Shengkun Xie Author-X-Name-First: Shengkun Author-X-Name-Last: Xie Author-Name: Chong Gan Author-X-Name-First: Chong Author-X-Name-Last: Gan Title: Classification of territory risk by generalized linear and generalized linear mixed models Abstract: Territory risk analysis has played an important role in the decision-making of auto insurance rate regulation. Due to the optimality of insurance loss data groupings, clustering methods become the natural choice for such territory risk classification. In this work, spatially constrained clustering is first applied to insurance loss data to form rating territories. The generalized linear model (GLM) and generalized linear mixed model (GLMM) are then proposed to derive the risk relativities of obtained clusters. Each basic rating unit within the same cluster, namely Forward Sortation Area (FSA), takes the same risk relativity value as its cluster. The obtained risk relativities from GLM or GLMM are used to calculate the performance metrics, including RMSE, MAD, and Gini coefficients. The spatially constrained clustering and the risk relativity estimate help obtain a set of territory risk benchmarks used in rate filings to guide the rate regulation process. Journal: Journal of Management Analytics Pages: 223-246 Issue: 2 Volume: 10 Year: 2023 Month: 04 X-DOI: 10.1080/23270012.2023.2187716 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2187716 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:2:p:223-246 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2212381_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20 Author-Name: Shijin Cai Author-X-Name-First: Shijin Author-X-Name-Last: Cai Author-Name: Wei Jiang Author-X-Name-First: Wei Author-X-Name-Last: Jiang Author-Name: Lai Wei Author-X-Name-First: Lai Author-X-Name-Last: Wei Title: A point-wise minimization model for data envelopment analysis considering environmental variables Abstract: Environmental variables are widely recognized as a cause of differences in efficiency measurement. However, the existing literature on data envelopment analysis (DEA) in environmental factors ignores the impact of demand on output. To address this gap, we propose the Point-wise Minimization DEA model (PWMDEA), which considers contextual variables that affect demand and lead to differences in efficiency. The model obtains efficiency value by considering the minimum of virtual inputs and virtual demand. Then, efficiency is evaluated by minimizing the ratio of above minimum to virtual output. This one-step model avoids issues of multi-stage assumptions and requires less data, making it more applicable. Moreover, we demonstrate the accuracy of our new model by conducting simulations with given true efficiency values. The simulation results demonstrate that our model has the lowest ranking error when the output is affected by multiple inputs or when demand has a significant impact. In addition, we evaluate the efficiency of healthcare in 31 Chinese provinces by considering two environmental factors. The results suggest that provinces with lower financial investments or population loss received higher rankings from our proposed model. These findings provide plausible explanations and demonstrate the practical usefulness of our model. Journal: Journal of Management Analytics Pages: 336-358 Issue: 2 Volume: 10 Year: 2023 Month: 04 X-DOI: 10.1080/23270012.2023.2212381 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2212381 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:2:p:336-358 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2212380_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20 Author-Name: Wei Gao Author-X-Name-First: Wei Author-X-Name-Last: Gao Author-Name: Ning Jiang Author-X-Name-First: Ning Author-X-Name-Last: Jiang Author-Name: Feng Gu Author-X-Name-First: Feng Author-X-Name-Last: Gu Title: Understanding the role of streamers in livestreaming commerce: a vocal–visual perspective Abstract: Despite streamers having earned widespread attention, no studies have explored the relationship between streamers and customer engagement from a vocal–visual perspective in the livestreaming commerce context. Drawing on the elaboration likelihood model, we examine how streamers’ speech rate and facial attractiveness influence customer engagement using 434 pieces of unstructured livestreaming video data extracted from Taobao. The findings show that speech rate is positively related to customer engagement behaviors. Facial attractiveness has a significant positive effect on the number of comments and viewers obtained, but it has no impact on the number of likes received in a livestream. Speech rate and facial attractiveness demonstrate a significant interaction effect, increasing customer engagement behaviors. Additionally, the numbers of comments and viewers obtained are positively related to sales performance. These results offer new insights into the vital role of streamers and provide practical implications for improving customer engagement in livestreaming commerce. Journal: Journal of Management Analytics Pages: 247-269 Issue: 2 Volume: 10 Year: 2023 Month: 04 X-DOI: 10.1080/23270012.2023.2212380 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2212380 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:2:p:247-269 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2209883_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20 Author-Name: Priyanka Chawla Author-X-Name-First: Priyanka Author-X-Name-Last: Chawla Author-Name: Jerry Zeyu Gao Author-X-Name-First: Jerry Zeyu Author-X-Name-Last: Gao Author-Name: Teng Gao Author-X-Name-First: Teng Author-X-Name-Last: Gao Author-Name: Chengchen Luo Author-X-Name-First: Chengchen Author-X-Name-Last: Luo Author-Name: Huimin Li Author-X-Name-First: Huimin Author-X-Name-Last: Li Author-Name: Yiqin We Author-X-Name-First: Yiqin Author-X-Name-Last: We Title: An interactive web-based solar energy prediction system using machine learning techniques Abstract: Solar energy being one of the most inexpensive renewable energy sources, has shown to be a viable alternative to traditional fossil-fuel and wood-based electricity generation. For the purpose of creating a more trustworthy and successful energy planning strategy, accurate projections of sun irradiation, solar energy generation, and revenues are crucial. Hence, in this work we have proposed web-based optimal prediction system that estimates solar radiation based on location and meteorological data using Machine Learning techniques. Furthermore, an interactive dashboard solar digital map has been developed that enables real-time investigation of solar energy consumption, production, solar radiation, and investment potential for a specific county in California. The model's performance has been measured using Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Average Error (MAE), and Mean Absolute Percentage Error (MAPE) scores. Experimental results demonstrate that stacking model outperformed all the models with the lowest RMSE, MSE, and MAE. Journal: Journal of Management Analytics Pages: 308-335 Issue: 2 Volume: 10 Year: 2023 Month: 04 X-DOI: 10.1080/23270012.2023.2209883 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2209883 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:2:p:308-335 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2207184_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20 Author-Name: Disha Garg Author-X-Name-First: Disha Author-X-Name-Last: Garg Author-Name: Mansaf Alam Author-X-Name-First: Mansaf Author-X-Name-Last: Alam Title: Smart agriculture: a literature review Abstract: Industry 4.0 brings revolutionary changes to farming businesses by integrating emerging technologies such as the Internet of things (IoT), big data analytics (BDA), cloud computing (CC), and artificial intelligence (AI). These Emerging technologies are the potential enablers of data-driven smart farming. Realizing the importance of data-driven agriculture, we provide a complete picture of current literature in smart agriculture by using a review classification framework divided into four categories: (i) Smart Farming Activities, (ii) BDA Levels, (iii) BDA Models, and (iv) BDA Techniques. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to review the current literature on intelligent farming. A total of 90 papers have been identified, and content analysis was conducted to mine knowledge in the domain for 2011–2022. The primary intention of this review is to clarify the most prominent farming activity, level of analytics, BDA models, and techniques in smart farming. Finally, the findings of our review analysis are discussed, and work suggestions are addressed for further research. Journal: Journal of Management Analytics Pages: 359-415 Issue: 2 Volume: 10 Year: 2023 Month: 04 X-DOI: 10.1080/23270012.2023.2207184 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2207184 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:2:p:359-415 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2229312_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20 Author-Name: Raosaheb Latpate Author-X-Name-First: Raosaheb Author-X-Name-Last: Latpate Author-Name: Maruti Bhosale Author-X-Name-First: Maruti Author-X-Name-Last: Bhosale Author-Name: Sandesh Kurade Author-X-Name-First: Sandesh Author-X-Name-Last: Kurade Title: Cold supply chain inventory models for agricultural products with multi-stage stochastic deterioration rates Abstract: In India, majority of the families depend upon agricultural business. Also, 40% of agricultural food and vegetables are rotted due to improper planning. It is predicted in 2023 that US$ 293.27 billions will be wastage of food. That's why, the cost of these items are high in the retail market and farmers get least price for their produce. It is essential to avoid the loss by adopting cold supply chain and its impact will help to increase the GDP of country. Here, we proposed the supply chain model which includes one warehouse and multi-retailers. Cold storage facility is available at warehouse, transport facility and retailers. To support the proposed model, we have collected the numerical data of coriander (highly perishable) product from the market yard, Pune, India. The proposed model solved by using real world numerical case study. The life of this product is very less hence it needs to develop a cold supply chain for this item. Also, we solved multiple inventory differential equations by using boundary value problem. To solve the proposed models for single retailer and multiple retailers, we have developed a solution methodology based on evolutionary algorithm. We have observed that, optimum profit is highly sensitive to the warehouse demand and purchase cost. Hence it is essential to reduce the deteriorated items at storage point. The warehouse, retailer and customer demand significantly affect on the deteriorated items. In single and multiple retailer, for low warehouse and retailer demand, we get maximum profit. Thus, less number of deteriorated items of the supply chain will helps to the society, as well as country to increase the profit of the supply chain. Journal: Journal of Management Analytics Pages: 516-549 Issue: 3 Volume: 10 Year: 2023 Month: 07 X-DOI: 10.1080/23270012.2023.2229312 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2229312 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:3:p:516-549 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2232804_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20 Author-Name: Rui Yong Author-X-Name-First: Rui Author-X-Name-Last: Yong Author-Name: Shigui Du Author-X-Name-First: Shigui Author-X-Name-Last: Du Author-Name: Jun Ye Author-X-Name-First: Jun Author-X-Name-Last: Ye Title: Linguistic neutrosophic matrix energy and its application in multiple criteria group decision-making Abstract: Matrix energy is an important representation tool of collective information. Then, it is not applied to various fuzzy and linguistic environments. To compensate for this gap, this article aims to extend the matrix energy to propose the energy of a linguistic neutrosophic matrix (LNM) for solving a multiple criteria group decision-making (MCGDM) problem, which fully contains LNMs of decision-maker weights, criteria weights, and alternative evaluations. To realize the objective, this study first presents the energy of LNM in view of the true matrix energy, the false matrix energy, and the indeterminate matrix energy. Then, a MCGDM technique is established in view of the LNM energy method in a LNM circumstance. Finally, the developed MCGDM technique using the LNM energy is used to solve the hospital location choice problem in the full LNM scenario. Meanwhile, the decision results indicate the validity and usability of the established MCGDM technique. Journal: Journal of Management Analytics Pages: 477-492 Issue: 3 Volume: 10 Year: 2023 Month: 07 X-DOI: 10.1080/23270012.2023.2232804 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2232804 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:3:p:477-492 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2219999_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20 Author-Name: Xiaojun Xu Author-X-Name-First: Xiaojun Author-X-Name-Last: Xu Author-Name: Linzhong Xu Author-X-Name-First: Linzhong Author-X-Name-Last: Xu Author-Name: Xiaoli Wang Author-X-Name-First: Xiaoli Author-X-Name-Last: Wang Title: Study on coopetition relationship simulation among M-commerce information service subjects based on Lotka-Volterra model Abstract: In order to explore the evolution law of coopetition relationship among M-commerce information service (MIS) subjects and further reveal the allocation mechanism of information resources, the research extends Lotka-Volterra model, builds the coopetition relationship model of MIS subjects and simulates their coopetition relationship by Python and MATLAB to obtain the evolution trend of information resources possession (IRP) of MIS subjects in the coopetition process. The results show that the mutualism cooperation pattern dominated by promoting effect can maximize the value of information resources. This paper has significant reference value for how to optimize the allocation of information resources among MIS subjects. Journal: Journal of Management Analytics Pages: 583-606 Issue: 3 Volume: 10 Year: 2023 Month: 07 X-DOI: 10.1080/23270012.2023.2219999 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2219999 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:3:p:583-606 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2209859_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20 Author-Name: Kittiphod Charoontham Author-X-Name-First: Kittiphod Author-X-Name-Last: Charoontham Author-Name: Jirawat Worakantak Author-X-Name-First: Jirawat Author-X-Name-Last: Worakantak Author-Name: Kessara Kanchanapoom Author-X-Name-First: Kessara Author-X-Name-Last: Kanchanapoom Author-Name: Nartraphee Tancho Author-X-Name-First: Nartraphee Author-X-Name-Last: Tancho Title: A countermeasure designed to restrain self-serving behavior and strategic rating disclosure of credit rating agencies Abstract: This study examines an incentive of the credit rating agency (CRA) to exert effort to observe projects’ signals and strategically disclose ratings when the upfront fee and performance-based fee scheme are imposed. Under the upfront fee scheme, the CRA obtains an upfront fee in exchange for its services but gains a performance-based fee only if its ratings accurately foresee the rated project’s outcome. In the setting, an issuer solicits a rating from the CRA, whose conduct of inflating and deflating ratings is considered. In addition, the CRA can endogenously exert effort to observe a project's signal, which specifies the signal accuracy and how much operating costs the CRA incurs. After receiving the observed signal, the CRA can strategically decide to announce a rating corresponding to or contradicting the observed signal. The findings reveal that the performance-based fee scheme incentivizes the CRA to exert greater effort and truthfully disclose a more accurate rating. Journal: Journal of Management Analytics Pages: 550-565 Issue: 3 Volume: 10 Year: 2023 Month: 07 X-DOI: 10.1080/23270012.2023.2209859 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2209859 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:3:p:550-565 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2229842_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20 Author-Name: Ali Arishi Author-X-Name-First: Ali Author-X-Name-Last: Arishi Author-Name: Krishna Krishnan Author-X-Name-First: Krishna Author-X-Name-Last: Krishnan Title: A multi-agent deep reinforcement learning approach for solving the multi-depot vehicle routing problem Abstract: The multi-depot vehicle routing problem (MDVRP) is one of the most essential and useful variants of the traditional vehicle routing problem (VRP) in supply chain management (SCM) and logistics studies. Many supply chains (SC) choose the joint distribution of multiple depots to cut transportation costs and delivery times. However, the ability to deliver quality and fast solutions for MDVRP remains a challenging task. Traditional optimization approaches in operation research (OR) may not be practical to solve MDVRP in real-time. With the latest developments in artificial intelligence (AI), it becomes feasible to apply deep reinforcement learning (DRL) for solving combinatorial routing problems. This paper proposes a new multi-agent deep reinforcement learning (MADRL) model to solve MDVRP. Extensive experiments are conducted to evaluate the performance of the proposed approach. Results show that the developed MADRL model can rapidly capture relative information embedded in graphs and effectively produce quality solutions in real-time. Journal: Journal of Management Analytics Pages: 493-515 Issue: 3 Volume: 10 Year: 2023 Month: 07 X-DOI: 10.1080/23270012.2023.2229842 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2229842 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:3:p:493-515 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2219993_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20 Author-Name: Yan Liu Author-X-Name-First: Yan Author-X-Name-Last: Liu Author-Name: Bryan Fuller Author-X-Name-First: Bryan Author-X-Name-Last: Fuller Author-Name: Kim Hester Author-X-Name-First: Kim Author-X-Name-Last: Hester Author-Name: Hong Chen Author-X-Name-First: Hong Author-X-Name-Last: Chen Title: Authentic leadership and employees’ job performance: mediation effect of positive employee health Abstract: In light of global competition and the COVID-19 pandemic, organizations are encountering an increasingly challenging and unpredictable environment. Consequently, employees are experiencing heightened levels of job strain. This study aims to explore the impact of various organizational mechanisms on promoting positive employee health within the organization, ultimately affecting employees’ job performance. The findings of this study indicate that authentic leadership and the absence of organizational politics are significant predictors of positive employee health. Moreover, positive employee health has a positive influence on supervisor-rated job performance through its effect on job engagement. This study serves as a valuable resource for organizations, shedding light on the fundamental factors that contribute to positive employee health. It also raises managers’ awareness of the importance of nurturing and sustaining employees’ emotional and physical well-being to maintain competitiveness in the market. Journal: Journal of Management Analytics Pages: 566-582 Issue: 3 Volume: 10 Year: 2023 Month: 07 X-DOI: 10.1080/23270012.2023.2219993 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2219993 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:3:p:566-582 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2209863_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20 Author-Name: Quanchen Liu Author-X-Name-First: Quanchen Author-X-Name-Last: Liu Author-Name: Mengli Yu Author-X-Name-First: Mengli Author-X-Name-Last: Yu Author-Name: Bingqing Xiong Author-X-Name-First: Bingqing Author-X-Name-Last: Xiong Author-Name: Zhao Cai Author-X-Name-First: Zhao Author-X-Name-Last: Cai Author-Name: Pengzhu Zhang Author-X-Name-First: Pengzhu Author-X-Name-Last: Zhang Author-Name: Chee-Wee Tan Author-X-Name-First: Chee-Wee Author-X-Name-Last: Tan Title: Health analytics in business research: a literature review Abstract: Technological advances have enabled the collection of large quantities of valuable data for health-related use. Particularly, a growing number of scholars are paying attention to the application of health analytics to business topics. A comprehensive analysis of health analytics application in business research is necessary to realize the commercial value of health analytics. In this article, we summarize peer-reviewed articles that have been published in business journals, with an eye toward formulating a roadmap for applying health analytics across multiple business domains. First, we demonstrate how health-related data can be harnessed to inform business research. Second, we endeavor to consolidate the available datasets and analytical techniques commonly employed to explore health analytics in business domains. Finally, we discuss the practical challenges confronting scholars in health analytics, as well as future research opportunities. Insights from our study yields insights that can be leveraged by business scholars interested in health analytics research. Journal: Journal of Management Analytics Pages: 447-476 Issue: 3 Volume: 10 Year: 2023 Month: 07 X-DOI: 10.1080/23270012.2023.2209863 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2209863 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:3:p:447-476 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2258372_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857 Author-Name: Siqi Pan Author-X-Name-First: Siqi Author-X-Name-Last: Pan Author-Name: Qiang Ye Author-X-Name-First: Qiang Author-X-Name-Last: Ye Author-Name: Wen Shi Author-X-Name-First: Wen Author-X-Name-Last: Shi Title: Using a novel ensemble learning framework to detect financial reporting misconduct Abstract: Our research focuses on detecting financial reporting misconduct and derives a comprehensive misconduct sample using AAERs and intentional restatements. We develop a novel ensemble learning method, Multi-LightGBM, for highly imbalanced classification learning. We adopt a human-machine cooperation feature selection method, which can mitigate the limitation of incomplete theories, enhance the model performance, and guide researchers to develop new theories. We propose a cost-based measure, expected benefits of classification, to evaluate the economic performance of a model. The out-of-sample tests show that Multi-LightGBM, coupled with the features we selected, outperforms other predictive models. The finding that introducing intentional material restatements into our predictive model does not reduce the effectiveness of capturing AAERs has important implications for research on AAERs detection. Moreover, we can identify more misconduct firms with fewer resources by the misconduct sample relative to the standalone AAERs sample, which is quite beneficial for most model users. Journal: Journal of Management Analytics Pages: 607-624 Issue: 4 Volume: 10 Year: 2023 Month: 10 X-DOI: 10.1080/23270012.2023.2258372 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2258372 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:4:p:607-624 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2258376_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857 Author-Name: Chenxia Jin Author-X-Name-First: Chenxia Author-X-Name-Last: Jin Author-Name: Fachao Li Author-X-Name-First: Fachao Author-X-Name-Last: Li Author-Name: Yuqing Xia Author-X-Name-First: Yuqing Author-X-Name-Last: Xia Author-Name: Sohail S. Chaudhry Author-X-Name-First: Sohail S. Author-X-Name-Last: Chaudhry Title: Commodity layout in supermarkets: using the integration of the comprehensive related value method and genetic algorithm Abstract: The existing shelf layout methods do not explicitly consider the attention and relevancy of the commodity systematically and thus have failed to capture the invalid associations, resulting in poor sales impact and customer satisfaction. For such shortcomings, in this paper, we propose a mathematical programming approach for shelf layout problems based on comprehensive related value. First, we introduce the concepts of related value considering both attention and relevancy; second, we give the concept of adjacent utility value and the freedom of placement, and further analyze the impact of the same commodity on surrounding commodities due to different placement positions; third, we establish a new comprehensive related value-based commodity layout optimization model (CRV-CL) and provide the solution steps integrating with a genetic algorithm. Finally, we analyze the characteristics of CRV-CL through a specific case. The simulation results indicate the overall relevancy after applying the CRV-CL model. Journal: Journal of Management Analytics Pages: 625-648 Issue: 4 Volume: 10 Year: 2023 Month: 10 X-DOI: 10.1080/23270012.2023.2258376 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2258376 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:4:p:625-648 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2239818_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857 Author-Name: B. Karthick Author-X-Name-First: B. Author-X-Name-Last: Karthick Title: An inventory analysis in a multi-echelon supply chain system under asymmetry fuzzy demand: a fmincon optimization Abstract: In this article, the three-echelon supply chain model is developed with a single supplier, a single vendor and a single buyer in a fuzzy environment. The three-stage inspection technique is considered at the end of the vendor's process to supply high-quality products to the buyer. Moreover, the demand rate of the product in the market is often assumed to be uncertain, and accordingly, in this paper, the uncertainty is handled using fuzzy numbers. The primary goal of this model is to minimize the overall inventory cost relative to the asymmetry hexagonal fuzzy demand. Additionally, a backorder strategy is incorporated to gain customer satisfaction and reduce lost sales. To validate this model, numerical examples are tested and constrained non-linear optimization (i.e. fmincon solver) is performed to derive the optimal solution for the decision variables. Finally, conclusions with potential future directions are provided. Journal: Journal of Management Analytics Pages: 649-675 Issue: 4 Volume: 10 Year: 2023 Month: 10 X-DOI: 10.1080/23270012.2023.2239818 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2239818 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:4:p:649-675 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2239824_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857 Author-Name: Harriman Samuel Saragih Author-X-Name-First: Harriman Samuel Author-X-Name-Last: Saragih Title: Predicting song popularity based on Spotify's audio features: insights from the Indonesian streaming users Abstract: Using regression and classification machine learning algorithms, this study explores audio features on Spotify that contribute to the popularity of songs streamed in Indonesia, and then evaluates the feature importance for prediction. The publicly accessible Kaggle data consists of 92,755 rows and 20 columns. Using multiple model comparisons for regression and classification, this study identifies Extra Trees Regressor and Random Forest Classifier as the two predictive approaches with the highest accuracy. This study contributes to the scientific literature on hit songs by examining the influence of audio features on a song's popularity using both classification and regression machine learning methods, with an emphasis on Indonesia based on consumer culture theory. Journal: Journal of Management Analytics Pages: 693-709 Issue: 4 Volume: 10 Year: 2023 Month: 10 X-DOI: 10.1080/23270012.2023.2239824 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2239824 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:4:p:693-709 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2244503_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857 Author-Name: Xuemei Li Author-X-Name-First: Xuemei Author-X-Name-Last: Li Author-Name: Alexander Sigov Author-X-Name-First: Alexander Author-X-Name-Last: Sigov Author-Name: Leonid Ratkin Author-X-Name-First: Leonid Author-X-Name-Last: Ratkin Author-Name: Leonid A. Ivanov Author-X-Name-First: Leonid A. Author-X-Name-Last: Ivanov Author-Name: Ling Li Author-X-Name-First: Ling Author-X-Name-Last: Li Title: Artificial intelligence applications in finance: a survey Abstract: Finance is in our daily life. We invest, borrow, lend, budget, and save money. Finance also provides guidelines for corporation and government spending and revenue collection. Traditional statistical solutions such as regression, PCA, and CFA have been widely used in financial forecasting and analysis. With the increasing interest in artificial intelligence in recent years, this paper reviews the Artificial Intelligence (AI) techniques in the finance domain systematically and attempts to identify the current AI technologies used, major applications, challenges, and trends in Finance. It explores AI-related articles in Finance in IEEE Xplore and EI compendex databases. Findings suggest AI has been engaged in Finance in financial forecasting, financial protection, and financial analysis and decision-making areas. Financial forecasting is one of the main sub-fields of Finance affected by AI technology. The major AI technology used is supervised learning. Deep learning has gained popular in recent years. AI could be used to address some emerging topics. Journal: Journal of Management Analytics Pages: 676-692 Issue: 4 Volume: 10 Year: 2023 Month: 10 X-DOI: 10.1080/23270012.2023.2244503 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2244503 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:4:p:676-692 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2264294_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a Author-Name: Jun Ye Author-X-Name-First: Jun Author-X-Name-Last: Ye Author-Name: Shigui Du Author-X-Name-First: Shigui Author-X-Name-Last: Du Author-Name: Rui Yong Author-X-Name-First: Rui Author-X-Name-Last: Yong Title: MCDM technique using single-valued neutrosophic trigonometric weighted aggregation operators Abstract: Motivated based on the trigonometric t-norm and t-conorm, the aims of this article are to present the trigonometric t-norm and t-conorm operational laws of SvNNs and then to propose the SvNN trigonometric weighted average and geometric aggregation operators for the modelling of a multiple criteria decision making (MCDM) technique in an inconsistent and indeterminate circumstance. To realize the aims, this paper first proposes the trigonometric t-norm and t-conorm operational laws of SvNNs, which contain the hybrid operations of the tangent and arctangent functions and the cotangent and inverse cotangent functions, and presents the SvNN trigonometric weighted average and geometric operators and their properties. Next, a MCDM technique is proposed in view of the presented two aggregation operators in the circumstance of SvNNs. In the end, an actual case of the choice issue of slope treatment schemes is provided to indicate the practicability and effectivity of the proposed MCDM technique. Journal: Journal of Management Analytics Pages: 45-61 Issue: 1 Volume: 11 Year: 2024 Month: 01 X-DOI: 10.1080/23270012.2023.2264294 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2264294 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:11:y:2024:i:1:p:45-61 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2291836_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a Author-Name: Xiaojun Xu Author-X-Name-First: Xiaojun Author-X-Name-Last: Xu Author-Name: Lu Wang Author-X-Name-First: Lu Author-X-Name-Last: Wang Author-Name: Xiaoli Wang Author-X-Name-First: Xiaoli Author-X-Name-Last: Wang Title: Evolutionary game analysis of information service quality control of e-commerce platforms under information ecology Abstract: Due to the information asymmetry and imperfect supervision system, the problem of information service quality of e-commerce platforms is becoming increasingly prominent. Based on the perspective of information ecology, this paper constructs a three-party evolutionary game model including merchants, e-commerce platforms and governments, and analyzes the dynamic process of the three-party game under bounded rationality, thereby characterizing the behavior and optimal strategies of information service quality control. We carry out numerical simulation using data of the Pinduoduo platform. The results show that the cost of each party, control strength of e-commerce platforms, proportion of margin deducted and other factors are important factors affecting the quality of information service; Only when the sum of the costs of passive management of e-commerce platforms, penalties for merchants, and the additional revenue generated from the active management exceeds the cost of the active management of the e-commerce platforms, it ensures effective control over the quality of information service in a stable market environment. Finally, some suggestions are put forward to optimize the e-commerce information service quality control. Journal: Journal of Management Analytics Pages: 135-159 Issue: 1 Volume: 11 Year: 2024 Month: 01 X-DOI: 10.1080/23270012.2023.2291836 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2291836 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:11:y:2024:i:1:p:135-159 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2306624_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a Author-Name: Katarzyna Miszczyńska Author-X-Name-First: Katarzyna Author-X-Name-Last: Miszczyńska Author-Name: Elżbieta Antczak Author-X-Name-First: Elżbieta Author-X-Name-Last: Antczak Title: Financial and non-financial determinants of the indebtedness of hospitals. The case of Poland Abstract: The performance of Polish healthcare is still far from satisfactory, which is connected with growing indebtedness that adversely affects not only the development of healthcare but also the quality of medical services provided. In turn, the financial situation of hospitals depends on various indicators and the relationship between them. This paper investigates the determinants of healthcare sector functioning in the context of hospitals’ financial situation (dependent variable measured by total debt ratio). 321 individual public units were examined between years 2007–2017. More specifically, the fixed effects panel data model in question measures and evaluates the impact of economic, regional, institutional, and social factors in determining the dependent variable. This study confirmed dependencies between financial and non-financial ratios along with their impact on the financial condition of studied entities. The management implications of our study lay the groundwork for the improvement of efficient resource management for public hospitals. Journal: Journal of Management Analytics Pages: 26-44 Issue: 1 Volume: 11 Year: 2024 Month: 01 X-DOI: 10.1080/23270012.2024.2306624 File-URL: http://hdl.handle.net/10.1080/23270012.2024.2306624 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:11:y:2024:i:1:p:26-44 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2301748_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a Author-Name: H. D. Arora Author-X-Name-First: H. D. Author-X-Name-Last: Arora Author-Name: Anjali Naithani Author-X-Name-First: Anjali Author-X-Name-Last: Naithani Title: On some new fuzzy entropy measure of Pythagorean fuzzy sets for decision-making based on an extended TOPSIS approach Abstract: Fuzzy entropy measures are valuable tools in decision-making when dealing with uncertain or imprecise information. There exist many entropy measures for Pythagorean Fuzzy Sets (PFS) in the literature that fail to deal with the problem of providing reasonable or consistent results to the decision-makers. To deal with the shortcomings of the existing measures, this paper proposes a robust fuzzy entropy measure for PFS to facilitate decision-making under uncertainty. The usefulness of the measure is illustrated through an illustration of decision-making in a supplier selection problem and compared with existing fuzzy entropy measures. The Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) approach is also explored to solve the decision-making problem. The results demonstrate that the proposed measure can effectively capture the degree of uncertainty in the decision-making process, leading to more accurate decision outcomes by providing a reliable and robust ranking of alternatives. Journal: Journal of Management Analytics Pages: 87-109 Issue: 1 Volume: 11 Year: 2024 Month: 01 X-DOI: 10.1080/23270012.2024.2301748 File-URL: http://hdl.handle.net/10.1080/23270012.2024.2301748 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:11:y:2024:i:1:p:87-109 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2304540_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a Author-Name: Shoude Li Author-X-Name-First: Shoude Author-X-Name-Last: Li Title: Dynamic control of a firm’s process innovation with knowledge accumulation in a vertically differentiated monopoly Abstract: This paper explores a multiproduct firm’s process innovation of high-and low-quality goods with knowledge accumulation in a vertically differentiated monopoly. We show that: (i) the system admits a saddle-point steady-state equilibrium under firm decision-making and social planner adjustment, respectively; (ii) the firm’s learning rates of knowledge accumulation of process innovation call for more intense efforts in both directions; (iii) there is a substitution between the process innovation for these two kinds of goods; (iv) the social incentive towards both kinds of innovation efforts is always larger than the private incentive characterizing the profit-seeking firm. Our contribution extends the dynamic innovation literature that focuses on single-good and considers the effects of corresponding knowledge accumulation on process innovation of these two kinds of goods. Further, this theoretical work contributes to the design of efficient innovation effort strategies for firms’ process innovation with corresponding knowledge accumulation in a vertically differentiated monopoly market. Journal: Journal of Management Analytics Pages: 1-25 Issue: 1 Volume: 11 Year: 2024 Month: 01 X-DOI: 10.1080/23270012.2024.2304540 File-URL: http://hdl.handle.net/10.1080/23270012.2024.2304540 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:11:y:2024:i:1:p:1-25 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2301709_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a Author-Name: Jorge Iván Pérez Rave Author-X-Name-First: Jorge Iván Author-X-Name-Last: Pérez Rave Author-Name: Carlos Mario Zapata Jaramillo Author-X-Name-First: Carlos Mario Author-X-Name-Last: Zapata Jaramillo Author-Name: Gloria Patricia Jaramillo Álvarez Author-X-Name-First: Gloria Patricia Author-X-Name-Last: Jaramillo Álvarez Title: Mental health in organizations from a healthcare analytics framework: taxonomic model, trends, and impact of COVID-19 Abstract: Mental disorders negatively affect employee well-being and organizational performance. Organizations face a challenge in terms of how to manage mental health. This paper clarifies three issues (underlying patterns, trends, and impact of COVID-19) regarding the scientific study of mental health in organizations from a healthcare analytics framework. The framework comprises eight stages considering a text-driven approach with scientific corpora assisted by linguistic/computational and statistical resources. This study discovers a new taxonomic model comprising five patterns in the scientific discourse on the topic. Trend analyses reveal imbalances and concerns regarding the interests associated with the patterns, which is reinforced by examining patterns “before” and “during” COVID-19. This paper complements psychological/epidemiological studies on mental health in organizations from a healthcare analytics perspective. Journal: Journal of Management Analytics Pages: 62-86 Issue: 1 Volume: 11 Year: 2024 Month: 01 X-DOI: 10.1080/23270012.2023.2301709 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2301709 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:11:y:2024:i:1:p:62-86 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2291835_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a Author-Name: Supriya Tiwari Author-X-Name-First: Supriya Author-X-Name-Last: Tiwari Author-Name: Kunal Shah Author-X-Name-First: Kunal Author-X-Name-Last: Shah Author-Name: Kajal Bhimani Author-X-Name-First: Kajal Author-X-Name-Last: Bhimani Title: EPQ model with the effect of inflation and reliability for partial trade credit under fuzzy and cloudy fuzzy environment Abstract: The proposed study offers the first-of-its-kind economic production quantity model for deteriorating items having a demand rate to be price dependent under the effect of inflation and reliability with partial trade credit. The model is extended under an uncertain environment by assuming inventory parameters to be triangular fuzzy numbers and cloudy triangular fuzzy numbers. The objective of the study is to maximize the profit of the inventory system and to identify the most suitable environment for the proposed problem. Results are verified using the numerical study. Furthermore, the comparative study is presented to justify the nature of fuzzy and cloudy fuzzy environments. Sensitivity analysis under all environments is conducted to identify the most sensitive parameters of all. Journal: Journal of Management Analytics Pages: 110-134 Issue: 1 Volume: 11 Year: 2024 Month: 01 X-DOI: 10.1080/23270012.2023.2291835 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2291835 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:11:y:2024:i:1:p:110-134 Template-Type: ReDIF-Article 1.0 # input file: TJMA_A_2261746_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a Author-Name: The Editors Title: Correction Journal: Journal of Management Analytics Pages: 160-160 Issue: 1 Volume: 11 Year: 2024 Month: 01 X-DOI: 10.1080/23270012.2023.2261746 File-URL: http://hdl.handle.net/10.1080/23270012.2023.2261746 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:taf:tjmaxx:v:11:y:2024:i:1:p:160-160