Industrial Management & Data Systems

Name: Industrial Management & Data Systems
Creator: vLex
License: https://vlex.com/terms-of-service/

Publisher:: Emerald Group Publishing Limited
Publication date:: 2021-02-01

ISBN:: 0263-5577

Issue Number

No. 125-3, January - February 2025

Latest documents

Exploring impact of production volume and product quality on manufacturers' profitability: analytical modeling and empirical validation
Purpose: This study aims to develop analytical models that consider product quality and production volume as essential drivers for profitability in the marketplace. It also considers product demand and price dynamics to understand related nuances backed by empirical validation. Design/methodology/approach: The pricing mechanism is influenced by production quality, while product demand is influenced by both price and quality. The study considers cost elements, including production cost and quality loss cost which in turn are influenced by production volume and product quality. It establishes analytical conditions for optimal product quality and applies them to numerical analyses considering four distinct industry settings. Findings: The study reveals that unique solutions exist for optimal product quality at each production level in four industry scenarios. The optimal production volume depends on product quality, and empirical research validates these findings from analytical models and numerical analysis. Originality/value: This study represents a pioneering effort to investigate operational strategies in both analytical and empirical contexts, thus contributing to the existing body of knowledge in this area.
Channel selection and pricing optimization in multichannel e-tailing
Purpose: This paper considers an e-tailer planning to distribute a product under one direct sales channel and multiple asymmetric agency platforms. Based on the multinomial logit (MNL) choice model, this study optimizes the pricing strategy and channel selection strategy to maximize the e-tailer’s profit. Design/methodology/approach: A two-stage channel selection and pricing problem is formulated, where the profit-maximizing e-tailer first optimally selects a specified number of agency platforms from a set of alternatives to distribute the product and then determines the optimal prices in those channels. Findings: An optimal pricing strategy is proposed to maximize the e-tailer’s total profit on multiple asymmetric channels. The results show that the e-tailer can obtain a higher profit by selling products on more asymmetric agency platforms. Moreover, an effective channel selection algorithm is provided to help the e-tailer optimally select the M agency platforms from N alternatives. Originality/value: This study enriches the relevant research on multichannel selection and pricing by proposing an optimal pricing strategy and an effective channel selection algorithm. Evaluation results based on real-world industrial data show that the proposed optimal multichannel pricing strategy in this paper can significantly improve the profit of a real-world e-tailer compared to the e-tailer’s actual profit.
Understanding user acceptance of robotic process automation: the user resistance perspective
Purpose: Robotic process automation (RPA) is an automation technology that replaces front-end information technology (IT) human workers with software that handles simple and repetitive tasks. Despite its importance, RPA research remains limited to topics related to the introduction of the technology itself due to its novelty. This study examines users’ resistance attitudes to understand their intentions to use RPA. Design/methodology/approach: By integrating the theory of planned behavior (TPB) and user resistance theory, this paper develops a research model to explain users’ intentions to use RPA. We employ a structural equation model (SEM) to analyze an online survey of individuals’ (N = 309) intentions to use RPA. Findings: The results show that subjective norms are the largest positive significant factor explaining the intention to use RPA, whereas user resistance is the next largest significant negative factor for the intention to use RPA. Additionally, dehumanization is found to have the most significant effect on user resistance. Originality/value: This study has meaningful implications for researchers and practitioners in the prediction of hyperautomation acceptance, such as RPA.
Convergent or not? The effect of linguistic convergence on the effectiveness of online physician-patient communication
Purpose: The purpose of this study is to investigate whether the convergence of linguistic features between physicians and patients with chronic diseases facilitates the effectiveness of physician–patient communication in online health communities (OHCs). Drawing on communication accommodation theory (CAT), the authors develop a research model that illustrates how the convergence of semantic features (language concreteness and emotional intensity) and stylistic features (language style) influence patient satisfaction and compliance. The model also incorporates the moderating effects of the physician's social status and the patients' complications. Design/methodology/approach: The data, collected from a prominent online health platform in China, include 15,448 consultation records over five years. The logistic regression is leveraged to test the hypotheses. Findings: The findings reveal that convergent semantic features, such as language concreteness and emotional intensity, along with stylistic features like language style, enhance patient satisfaction, which in turn leads to increased compliance. Additionally, the physician’s social status strengthens the effect of convergent emotional intensity but weakens the effect of convergent language concreteness. The physician’s social status has no significant impact on the link between convergent language style and satisfaction. Patients' complications weaken the effect of satisfaction on their compliance. Originality/value: This study contributes to the CAT and OHC literature by enhancing the understanding of the role of linguistic convergence in the effectiveness of online physician–patient communication and provides managerial implications for physicians on how to accommodate their communicative styles toward chronic patients to improve patient satisfaction and compliance.
Understanding determinants of GenAI usage and its effect on SCM performance using dynamic capability view
Purpose: Generative artificial intelligence (GenAI) can potentially improve supply chain management (SCM) processes across levels and verticals. However, despite its promise, the implementation of GenAI for SCM remains challenging, mainly due to the lack of knowledge regarding its key drivers. To address this gap, this study examines the factors driving GenAI implementation in an SCM environment and how these factors optimize SCM performance. Design/methodology/approach: A thorough literature review was followed to identify the drivers. The resultant model from the drivers was validated using a quantitative study based on partial least squares structural equation modeling (PLS-SEM) that used responses from 315 expert respondents from the field of SCM. Findings: The results confirmed the positive effect of performance expectancy, output quality and reliability, organizational innovativeness and management commitment to GenAI usage. Further, they showed that successful GenAI usage improved SCM performance through improved transparency, better decision-making, innovative design, robust development and responsiveness. Practical implications: This study reports the potential drivers for the contemporary development of GenAI in SCM and highlights an action plan for GenAI’s optimal performance. The findings suggest that by increasing the rate of GenAI implementation, organizations can continuously improve their strategies and practices for better SCM performance. Originality/value: This study establishes the first step toward empirically testing and validating a theoretical model for GenAI implementation and its effect on SCM performance.
Achieving food supply chain sustainability through digital sharing platforms: prospects in China
Purpose: Characterised by simultaneous food waste and shortages, our current food system is far from sustainable. Industry 4.0 has responded with technology-enabled innovations, including digital food-sharing platforms aimed at facilitating the efficient redistribution of surplus food. However, potential users often express reluctance to adopt such platforms, prompting this study to explore the underlying reasons for their hesitations. Design/methodology/approach: This study was conducted in China, the world’s largest platform economy, where food-sharing platforms are notably absent. Using a vignette-based qualitative approach, semi-structured interviews were conducted with 35 potential users. The data were analysed through thematic analysis to uncover insights into adoption intentions. Findings: The findings highlight the relevance of factors identified in existing technology acceptance theories, such as performance expectancy, effort expectancy, social influence, hedonic motivation, facilitating conditions and price value, in shaping adoption intentions. Additionally, content-specific and context-specific factors – such as trust in other users and the platform, concerns about “losing face” (mianzi) and safety concerns during the pandemic – emerged as critical influences on users' decisions to engage with these platforms. Originality/value: This study contributes to scholarly discussions on enhancing the effectiveness of new technological innovations for food supply chain sustainability. The theoretical contributions expand the technology acceptance literature by incorporating factors related to platform service content and operating context.
Decision rights partitioning and sharing platform performance: a combination of cluster and configurational analyses
Purpose: Research on decision rights partitioning and its impact on platform performance has predominantly focused on single rights, leading to inconclusive results. This study is driven by a more nuanced objective of exploring diverse governance models that can enhance the performance of sharing platforms across different contexts. Rather than delegating single decision right to users, this approach partitions several essential decision rights concurrently throughout the transaction process. By examining the complex relationships between multiple decision rights partitioning and platform performance, this study identifies and explains suitable governance models that are tailored to specific contextual factors for improving the performance of sharing platforms. Design/methodology/approach: Collecting data from 60 sharing platforms in China, this study employs a combination of cluster and configuration analyses to address research questions. Findings: The study explores three strategic decision rights partitioning modes widely adopted by sharing platforms. It further identifies four governance models for sharing platforms, which are termed as conservative seller model, conservative buyer model, aggressive seller model and aggressive buyer model, related to certain contextual factors. Originality/value: In addressing platform governance as key to sharing platform success, the study contributes to the literature by investigating how multiple-rights partitioning portfolios and strategic differentiation in decision rights partitioning can enhance platform performance.
Tacit knowledge-informed approximate dynamic programming for last-mile delivery operations in online-to-offline pharmacies
Purpose: Tacit knowledge in frontline operations is primarily reflected in the holders’ intuition about dynamic systems. Despite the implicit nature of tacit knowledge, the understanding of complex systems it encapsulates can be displayed through formalization methods. This study seeks to develop a methodology for formalizing tacit knowledge in a dynamic delivery system. Design/methodology/approach: This study employs a structured survey to gather experiential knowledge from dispatchers engaged in last-mile delivery operations. This knowledge is then formalized using a value function approximation approach, which transforms tacit insights into structured inputs for dynamic decision-making. We apply this methodology to optimize delivery operations in an online-to-offline pharmacy context. Findings: The raw system feature data are not strongly correlated with the system’s development trends, making them ineffective for guiding dynamic decision-making. However, the system features obtained through preprocessing the raw data increase the predictiveness of dynamic decisions and improve the overall effectiveness of decision-making in delivery operations. Research limitations/implications: This research provides a foundational framework for studying sequential dynamic decision problems, highlighting the potential for improved decision quality and system optimization through the formalization and integration of tacit knowledge. Practical implications: This approach proposed in this study offers a method to preserve and formalize critical operational expertise. By embedding tacit knowledge into decision-making systems, organizations can enhance real-time responsiveness and reduce operational costs. Originality/value: This study presents a novel approach to integrating tacit knowledge into dynamic decision-making frameworks, demonstrated in a real-world last-mile delivery context. Unlike previous research that focuses primarily on explicit data-driven methods, our approach leverages the implicit, experience-based insights of operational staff, leading to more informed and effective decision-making strategies.
What drives trust building in live streaming E-commerce? From an elaboration likelihood model perspective
Purpose: Live streaming e-commerce, which integrates real-time video interaction with online shopping, has quickly become a popular sales channel. It not only allows for immediate feedback but also builds a sense of trust and connection between streamers and consumers. Drawing on the elaboration likelihood model (ELM), we investigate how central and peripheral route factors affect consumers’ trust building and purchase intentions. Additionally, we identify consumer involvement as a key moderator affecting the relationship between central route factors and trust in product as well as the relationship between peripheral route factors and trust in streamer. Design/methodology/approach: To test the research model, we collected data from 423 consumers on TaoBao Live. Findings: The findings show that information completeness, accuracy and currency positively affect trust in the product, while perceived physical characteristic similarity, streamer humor attractiveness and passion attractiveness positively affect trust in the streamer. Trust in the streamer positively influences trust in the product, which subsequently impacts purchase intention. Moreover, involvement moderates the effects of information accuracy, currency, perceived physical characteristic similarity and passion attractiveness on trust. Originality/value: First, we examine the direct influence of product- and streamer-related cues on consumer trust and purchase intention through distinct pathways. Second, we adopt ELM to explain the process of consumer trust building by investigating how central and peripheral route factors influence purchase intention through consumer trust in live streaming settings. Third, we incorporate involvement as a crucial moderator, shedding light on the boundary conditions of trust building in live streaming e-commerce.
Inventory and pricing decisions of brand owners and streamers under demand uncertainty
Purpose: In the practice of live streaming e-commerce, the consumer demand is usually uncertain, and the inventory and prices can be decided by brand owners or streamers. To this end, this study examines the inventory and pricing decisions of the brand owner and streamer in a live streaming e-commerce supply chain under demand uncertainty. Design/methodology/approach: In this study, four scenarios are considered, i.e. the brand owner determines the inventory and price (Scenario BB), the brand owner determines the inventory and the streamer determines the price (Scenario BS), the streamer determines the inventory and the brand owner determines the price (Scenario SB), and the streamer determines the inventory and price (Scenario SS). Findings: The results show that the inventory and prices, as well as the profits of the brand owner and streamer increase with the consumer sensitivity to streamer’s sales effort level under the four scenarios. The inventory (price) is the highest under Scenario SS (SB), while that is the lowest under Scenario BB (BS). In addition, when the sensitivity is low, the brand owner’s profit is the highest under Scenario BB, otherwise, the profit is the highest under Scenario SS. Regardless of the sensitivity, the streamer’s profit is always the highest under Scenario SS. Originality/value: Few studies focused on the inventory and pricing decisions of brand owners and streamers in live streaming e-commerce supply chains under demand uncertainty, while this work bridges the research gap. This study can provide theoretical basis and decision support for brand owners and streamers.