Real-time information sharing, customer orientation, and the exploration of intra-service industry differences : Malaysia as an emerging market

Differences in business practices and preferences are vital for understanding specific industries, particularly in relation to downstream operations in emerging markets. This study explores the effects of real-time information sharing (RTIS) on downstream operations in three service sub-sectors that are dominated by small and medium-sized enterprises (SMEs) - wholesale & retail, food & beverages, and accommodation. Drawing on information processing theory, we examine the differences in the adoption and perceived benefits for customers of RTIS through a survey of 221 middle-level managers from Malaysia. Our findings indicate that, overall, RTIS is significantly associated with customer purchase behavior (PB) in the presence of customer orientation (CO) that plays a two-fold mediating role in purchase and repurchase behavior. Our results also point to sectoral differences. RTIS—with customer PB and post-purchase behavior in the presence of CO—is more effective in the wholesale & retail and food & beverages sub-sectors than in accommodation. The article concludes with a discussion of theoretical and practical implications.©2021 Elsevier. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions.© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed

Survey dataset on Muslim’s religiosity, Muslim personality and work behavior

Data were collected from administration officers ranging from middle-management to top management of the five universities of Malaysia. The data was collected through a standardized and structured questionnaire. The variables of the study were religiosity, personality and work behavior of Muslims. Muslim work behavior construct formulated on the basis on collected data