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Sustainable Pricing-Production-Workforce-Routing Problem for Perishable Products by Considering Demand Uncertainty; A Case Study from the Dairy Industry
No full textInternational audienceThe production routing problem seeks to simultaneously optimize production, routing, and inventory decisions for the plant and the suppliers. In this article an integrated multi-objective sustainable pricing-production-workforce-routing problem is presented for perishable products. Total profit, workforce planning, and vehicle fuel consumption are considered as objective functions due to the importance of operational performance, social, and environmental concerns. The application of the proposed approach is investigated using real case data from a dairy product supply chain. Furthermore, a new solution approach, called Fuzzy Domination Self-Learning Non-Dominated Sorting Algorithm (FDSL-NSGA-II), is developed to solve the problem. The results show that the Pareto solutions of FDSL-NSGA-II outperform those of the classic NSGA-II. Moreover, the findings show that the proposed model can create a surpassing tradeoff between the various aspects of a supply chain, including production, distribution, and workforce planning. In addition, it concurrently optimizes the selling price and protects the environment from the negative impacts of greenhouse gas emissions (GHGs). A comprehensive analysis of the results reveals several managerial insights for decision makers in the logistics industry
Does Target Country Cultural Orientation Influence M&A?
No full textInternational audienceWe examine whether the cultural orientation of target firms influences the outcomes of international mergers and acquisitions (M&As). Prior research shows that the national culture of the acquiring firm country influences M&As, as well as the distance between the acquiring and target country cultures. Not previously studied from a cultural perspective of M&As has been the target country culture, despite estimates that about 40% of M&As are target-initiated. Our focus is on target firm cultural orientation, including how cultural orientation affects the likelihood of, and returns from, M&As. Our testing applies three cultural orientation factors (results, tradition and people orientation) extracted from a GLOBE cultural framework to a dataset of firm-level M&A data across 39 countries, in 1990–2016. We find that firms from cultures with a results orientation are less likely to become M&A targets and also experience higher cumulative abnormal returns if acquired, while firms from countries with cultures with a tradition orientation or a people orientation are more likely to become targets but experience lower cumulative abnormal returns if acquired. These results are robust to a comprehensive range of robustness tests. Our findings suggest that understanding the cultural orientation of target firms is important to understanding M&A outcomes
Does Shariah compliance affect investor behaviour in the COVID-19 times: evidence from herding in the global energy market
No full textInternational audienceThis paper investigates whether the Shariah compliance matters in determining investor behaviour in herding across firms in the global energy market. Our sample comprises 2501 globally listed energy equities from 10 April 2019 to 8 April 2020 from the Refinitv Eikon database, which also flags firms as compliant or otherwise with Shariah or Islamic law. Using closing price data for the selected firms, we analyse herding behaviour across the two groups, in addition to various firm and market characteristics such as size, profitability, analyst recommendations about future performance and up and down market days. Our results suggest herding in both Shariah and non-Shariah-compliant energy firms, and on down market days in particular. Cross-sectional tests indicate higher herding in larger and more-profitable Shariah firms, and those with positive analyst forecasts for the future, which is consistent with pressure-driven behaviour to maintain performance. In particular, we find that the COVID-19 pandemic does not significantly alter herding behaviour for the sample firms
OM Forum—Pandemics/Epidemics: Challenges and Opportunities for Operations Management Research
No full textInternational audienceWe reviewed research papers related to pandemics/epidemics (disease outbreaks of a global/regional scope) published in major operations management, operations research, and management science journals through the end of 2019. We evaluate and categorize these papers. We study research trends, explore research gaps, and provide directions for more efficient and effective research in the future. In addition, our recommendations include the lessons learned from the ongoing pandemic, COVID-19. We discuss papers in the following categories: (a) warning signals/surveillance, (b) disease propagation leading to pandemic conditions, (c) mitigation, (d) vaccines and therapeutics development, (e) resource management, (f) supply chain configuration, (g) decision support systems for managing pandemics/epidemics, and (h) risk assessment
Regional Expansion of Emerging Market Banks: Evidence from the Middle East
No full textInternational audienceThis study investigates challenges and opportunities that regionally expanding emerging market banks face. We focus on four leading Middle Eastern banks’ internationalization trajectories and performances by employing a case study approach. We first examine the four banks’ choices of target markets, entry sequencing, and entry modes over time and then analyze their entry strategies and post-entry financial performances in one of their key markets, Turkey. We show that the success of regional expansion strategies depends on parent bank characteristics such as scale and capital strength, strategic decisions regarding entry mode and timing, and host market structure and competitiveness
From Policy-Practice to Means-Ends Decoupling in Organizations: A Systematic Review and Paths for Future Research
No full textInternational audienceRecent developments in the innovation literature suggest that even when an organisation truthfully implements the adopted R&D policy, it may still fail to achieve its intended goals, a phenomenon called means-ends decoupling. We employ a systematic literature review to answer the question of "what is the current state of knowledge in the phenomenon of means-ends decoupling in the literature" and "where it can move in the future". Our paper provides a framework that delineates means-ends decoupling from policy-practice decoupling and identifies the underlying mechanisms that explain when and how means-ends decoupling may occur within an organisation's activities
Understanding and harnessing the potential of front-line employees’ self-governance in technologised museums and theme parks: insights from a qualitative study
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A joint production-workforce-delivery stochastic planning problem for perishable items
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The good, the bad, and the ugly: impact of analytics and artificial intelligence-enabled personal information collection on privacy and participation in ridesharing
No full textInternational audienceBig data analytics (BDA) and artificial intelligence (AI) may provide both bright and dark sides that may affect user participation in ridesharing. We do not know whether the juxtaposed sides of these IT artefacts influence users’ cognitive appraisals, and if so, to what extent will their participative behaviour be affected. This paper contributes to the IS research by uncovering the interplay between the dark and bright sides of BDA and AI and the underlying mechanisms of cognitive appraisals for user behaviour in ridesharing. We performed two phases of the study using mixed-methods. In the first study, we conduct 21 semi-structured interviews to develop the research model. The second study empirically validated the research model using survey data of 332 passengers. We find that the usage of BDA and AI on ridesharing platforms have a bright side (usefulness, “the good”) but also a dark side (uncertainty and invasion of privacy, “the bad and the ugly”). The bright side generates perceived benefits, and the dark side shape perceived risks in users, which discount the risks from the benefits of using the ridesharing platform. Privacy control exerts a positive effect on the perceived benefits to encourage individuals to use the ridesharing platform
Artificial intelligence and machine learning in finance: A bibliometric review
No full textInternational audienceThis study reviewed the artificial intelligence (AI) and machine learning (ML) literature in the finance field. Using a bibliometric approach, we collected 348 articles published in 2011–2021 from journals indexed in the Scopus database. Multiple software (RStudio, VOSviewer, and Excel) were employed to analyze the data and depict the most active scientific actors in terms of countries, institutions, sources, documents, and authors. Our review revealed an upward trajectory in the publication trend starting from 2015 and found the application of AI and ML in bankruptcy prediction, stock price prediction, portfolio management, oil price prediction, anti-money laundering, behavioral finance, big data analytics, and blockchain. Moreover, the United States, China, and the United Kingdom were the top three contributors to the literature. Our results provide practical guidance to market participants, especially, fintech and finance companies, on how AI and ML can be used in their decision-making