Power asymmetry often undermines collaboration in buyer-supplier relationships, particularly during times of global disruption. This study adopts an institutional perspective to explore how external actors help smaller suppliers restore balance with powerful buyers. Drawing on a multiple case study of ten Chinese small and medium-sized enterprises and four major buyers, based on 37 interviews during the COVID19 pandemic, this research identifies the process power-balancing under institutional interventions. The process is driven by dual-pathway model where institutional tactics simultaneously, including supplier empowerment and supplier constraint. We identify four specific mechanisms enabling the process: multi-actor institutional synergy, the alteration of buyers' cost-benefit evaluation, the temporary suspension of structural privileges, and industry norm transformation. The study advances institutional theory by theorising its enabling dimension as essential for power reconfiguration and provides a mechanism-based framework to strategically leverage institutional support, thereby fostering more equitable and resilient supply chains.

Purpose This investigation explores the increasingly prevalent role of data-driven approaches in hospitality and tourism research, which predominantly relies on expansive datasets from singular sources of social media data. The focus of this study is on uncovering distinct variations across different online travel agencies (OTAs) regarding key analytical metrics, including review topics, sentiment and review length on consumer ratings and their implications for research and practice. Design/methodology/approach Employing a comparative analysis method, this study analyzed over 11,000 user reviews from a premier boutique hotel in China, gathered from three leading Chinese OTAs. It integrated diverse social media analytics techniques, such as Word Cloud analysis, latent Dirichlet allocation (LDA), traditional regression and PLS structural equation modeling multi-group analysis (MGA) to evaluate review characteristics among the OTAs. Findings The results reveal significant discrepancies in review topics, sentiments, review lengths and their effects on consumer ratings across the OTAs examined. These findings highlight the risks of relying on a solitary data source for making generalizations. To improve the data-driven research’s validity and reliability in hospitality and tourism, the study advises the adoption of multi-source data for a more holistic understanding of consumer sentiments and behaviors. Originality/value By pointing out the methodological limitations of present hospitality and tourism research, this paper emphasizes the challenges of a single-source data-driven analytical approach. It suggests several avenues for enhancing the reliability and validity of future research in the field, marking a significant contribution to methodological advancements in hospitality and tourism studies.

PurposeThis study addresses the unclear influence of metaverse retail interactivity on behavioral intention across product categories by integrating a modified stimulus-organism-response (S-O-R) framework with the experiential hierarchy model (EHM) and examining dual emotional-cognitive pathways and product type (hedonic vs utilitarian) as moderators.Design/methodology/approachA 2 (interactivity: high/low) x 2 (product type: hedonic/utilitarian) between-subjects experiment (N = 261) was conducted in the Unity simulated shopping environment. High interactivity included grab/rotate/put-in-cart; low interactivity included click-for-information. Partial least squares structural equation modeling and endogeneity testing ensured robustness.FindingsInteractivity enhances behavioral intention via emotional (pleasure) and cognitive (media usefulness and choice confidence) synergies, with arousal indirectly acting through pleasure. Hedonic products amplify these effects, whereas utilitarian products require balanced interactivity to avoid cognitive overload.Originality/valueThis study clarifies context-dependent interactivity mechanisms in immersive retail by integrating S-O-R and EHM and identifying product type as a key moderator. This work offers the following guidelines: maximize interactivity for hedonic goods and prioritize functional clarity for utilitarian ones.

With the significant growth of the e-commerce business, the retail industry is experiencing rapid developments, leading to the explosion of the number of stock-keeping units (SKUs). Therefore, it calls for forecasting algorithms to forecast a large number of product-level demands over a short forecasting horizon. We developed a novel machine learning algorithm-the spatial-temporal gradient boosting tree (ST-GBT)-for demand forecasting for the retail industry. By incorporating the cross-section and time-series information in the existing gradient-boosting decision tree algorithm, our new algorithm can accurately forecast tremendous SKUs in one process. Furthermore, we show potential factors related to the retail industry, while new factors, such as higher-order statistics and risk-free interest, are also proposed for demand forecasting tasks. The numerical experiment results based on a large e-commerce company's historical transaction records support the comparative merits of the new algorithm with superior accuracy and automation ability.

In an era of big data, corporations have access to an abundance of employee details. While few inferences about employee performance can be made from these data, discarding them may be potentially detrimental to a business. Likewise, employee applications contain substantial amounts of information that cannot necessarily be used to indicate the potential performance of employees should they be appointed. "Persistent homology" considers the topography of data, identifying clusters of behavior that may be associated with performance levels, as well as "holes" in the data cloud that may be filled with suitable job applicants. Therefore, this study presents a theoretical application of persistent homology to human resource management, which considers the topography of data to identify clusters of behavior associated with performance levels and fill gaps with suitable job applicants. Our study demonstrates the potential of persistent homology that offers a breakthrough contribution to the wider research agenda.

Amidst the growing focus on media engagement and customer value in retail marketing literature, mobile commerce (MC) research has gained prominence. This research explores how customers employ mobile operating systems to engage with retailers and extract value within the context of Fast Moving Consumer Goods (FMCG) in retail. In Study 1, a survey involving 398 users uncovered that the customer handset OS moderates the effects of social media, traditional media engagement, and retail "place" on customer value. In Study 2, leveraging data from a foreign FMCG brand deeply immersed in social media platforms, we scrutinize how such engagement dynamics affect the influence of "place" on product sales across e-commerce and conventional retail channels. Our findings make significant theoretical contributions to comprehending customer value in MC, with practical implications for marketers, emphasizing the potential of customer mobile operating system as a valuable tool for effective marketing strategies.

PurposeThe purpose of this paper is to provide small and medium-sized enterprises (SMEs) in emerging markets with an updated Purchasing Portfolio Matrix (PPM) specifically for international sourcing. This data-driven PPM matrix is designed to provide a dynamic and process perspective that can help SMEs survive the disruptions caused by emergency situations such as the global COVID-19 pandemic.Design/methodology/approachThis research reports on qualitative interviews with experienced informants from 15 SMEs in the manufacturing industry. The authors follow process-based research using a combination of retrospective and real-time case study approaches to gradually unveil the dynamics in segmentation and sourcing strategies in the international sourcing context during the COVID-19 pandemic.FindingsThe findings reveal the dynamics of segmentation and international sourcing strategies during global disruptions and unpack the underlying logic behind the dynamics that is specific to SMEs in emerging economies.Originality/valueExisting literature on PPM predominantly focuses on static and normal sourcing circumstances. This paper addresses this gap by adopting a dynamic approach to study how sourcing strategies of SMEs from emerging economies evolve in a highly volatile environment from an international sourcing perspective.

Small- and medium-sized enterprises (SMEs) have faced criticism for their use of adversarial and deceptive marketing communication practices, which present challenges to ethical and sustainable development. This study aims to examine the factors influencing ethical marketing communication and their impact on value creation. With a robust dataset comprising responses from 183 participants and an impressive 85% response rate, structural equation modeling through ADANCO was employed to analyze the influence of each ethical communication factor on value creation. The findings reveal a positive relationship between ethical communication and value creation, benefiting all stakeholders involved. Moreover, the study emphasizes the importance of adhering to ethical principles and establishing mutually beneficial agreements with stakeholders to achieve successful outcomes in ethical communication. These findings underscore the significance of adopting ethical marketing communication practices to drive value creation and promote societal well-being. By incorporating ethical principles into their communication strategies, businesses can enhance their brand reputation, cultivate trust among customers, and contribute to the overall betterment of society.

The knowledge management process (KMP) requires continuous enhancement and is closely intertwined with the corresponding organizational business processes (BPs). Despite efforts to improve the integration of KMP and BPs, many knowledge management practices are ineffective due to the organizations' failure to incorporate their KMP with BPs. The lack of a structured approach to automate KMP improvement, driven by BP changes, is a major challenge. This study presents a BP-driven KMP modeling methodology, which has been employed in an aviation manufacturing enterprise's design institute to assist its KMP. The evaluation findings reveal that the approach can effectively facilitate the integration of BPs and KMP, as well as achieve BP-driven automation, ensuring the KMP's dynamic and continuous improvement. By incorporating this approach, organizations can automate their KMP improvement and respond to changes in their BPs more effectively, thereby improving their overall performance and competitiveness.

The growth of India’s electric vehicle (EV) market has been exponential. This research investigates the factors influencing purchase intention (PI) of EV customers drawing upon the extended theory of planned behavior (TPB). We employ a qualitative study involving ten semi-structured interviews of highly experienced professionals in India across various automobile companies (passenger cars, commercial vehicles, and 2-wheeler). The factors that were revalidated by this study include price value, range confidence and infrastructure readiness, attitude, subjective norm, and perceived behavioral control. However, there were mixed opinions about environmental concerns and emotional value. The result has further identified four new factors: competition, new technology, previous experience, and safety that align with the proposed model’s exploratory nature while adhering to TPB’s nomology. Our results suggest a mix of push (government policies, technology improvement, infrastructure, etc.) and pull (customers purchase intention) strategies to accelerate the EV market growth.

Live stream platforms have transformed the production and consumption of music, allowing KPop music to expand globally. Successful KPop idols are contrasted with large numbers of retired KPop performers, some of whom live in undesirable conditions. Drawing on the attachment theory, loyalty theory, and parasocial interaction theory, this study focuses on a unique group, comeback KPop performers, to examine how they acquire empathetic attachment and sustained loyalty from audiences through live stream shows, and the antecedents (i.e., sustained attractiveness, nostalgic experience, and parasocial interactions) of these two variables. Answering these questions seems important because comeback KPop performers have to interact with audiences without the financial and marketing support from entertainment agencies. The structural equation modeling of 288 responses from 176 Chinese and 112 Korean KPop audiences confirmed that empathetic attachment and sustained loyalty are positively associated with audience purchase intentions; sustained attractiveness and parasocial interactions function as antecedents of these two factors. The findings shed light on the comeback KPop performers who co-create value with audiences through live stream platforms, with theoretical contributions to the three theories mentioned above and managerial suggestions to KPop entertainment agencies, comeback KPop performers, and managers of live stream platforms.

AI-based applications (apps) have presented tremendous ethical challenges such as AI biases and privacy breaches, leading to the issue of privacy paradox. The paradox is more salient for dating apps than ordinary shopping apps, as data breaches in dating apps could relate to users' close social circles such as families and colleagues, suggesting more serious ethical and even legal consequences. Given the limited attention to user' arousal-ethics paradox, we developed and empirically examined a conceptual framework regarding how the arousing benefits of dating apps, users' ethical misgivings, users' perceived autonomy and perceived risks collectively affect their adoption of dating apps. Survey data from 319 construction workers confirmed that arousing benefits are associated with users' perceived autonomy, which leads to dating apps adoption. In contrast, users' ethical misgivings, associated with perceived risks, are negatively related to dating app adoption. This study contributes to the interdisciplinary field of privacy paradox that involves big data, artificial intelligence, user experience, and ethics by examining ethical consumption and practical suggestions to AI-based dating app developers.

Purpose Electronic commerce (EC) strategy – performance logic has gained significant popularity in the literature, particularly from the resource-based view (RBV) of theoretical underpinning. However, such an obsession of focusing on organizations' complementary resources has been increasingly challenged, which has pressed the RBV to examine the possibility of external factors that can also impact firm performance. In this study, the authors shed light on the firm's external readiness—defined as the extent to which a firm's customers and suppliers perceive EC as important—in the relationship between SME's complementary resources and firm performance. Design/methodology/approach The authors employed a refined data set based on the British EC Award database, in which the authors sampled 430 British SMEs' senior managements and examined how EC investments made by the SMEs influenced firm performance, and how their external readiness moderated this main relationship. Findings The results showed that, in line with the RBV perspective, SMEs' complementary business resources and human resources both had strong and direct impacts on the firm performance. They were also strongly mediated by EC functionality. In addition, SMEs' external readiness moderated the relationship between human resources and firm performance and that of EC functionality on firm performance. Originality/value The findings contribute to RBV theory building by extending earlier research on the role of technology as performance enablers for SMEs and shed light on the often-overlooked role of SMEs' external readiness.

Purpose This paper attempts to identify key factors (i.e., personalization, privacy awareness and social norms) that affect user experiences (UXs) of mobile recommendation systems according to the user involvement theory (push-based and pull-based) and their relationships. Design/methodology/approach The study is based on an online survey with students from an international business school located in southwestern China. The sample population for the study included randomly selected 600 university students who are active mobile phone users. A total of 470 questionnaires were returned; 456 were valid (14 were invalid due to the incompleteness of their responses), providing a response rate of 65%. Findings Social norms have the largest impact on user experience quality, followed by personalization and privacy awareness. User involvement in mobile recommendation systems has mediating effects on the above relationships, with larger effects on pull-based systems than on push-based systems. Originality/value This study provides an integrated framework for researchers to measure the effects of social, personal and risk factors on the quality of user experience. The results enrich the literature on user involvement, mobile recommendation systems and UX. The findings provide significant implications for both retailers and developers of mobile recommendation systems.

Research on supply chain resilience (SCRE) capabilities and its performance measurement has been growing in recent years. However, the investigation of these concepts has primarily been conducted independently despite the interdependence of these concepts. A systematic literature review of 153 papers was conducted based on the principles of rigour, transparency and replicability required by the methodology. For the first time, we structurally reviewed the 11 SCRE performance metrics categories and its capabilities in SCRE Capabilities-Performance Metrics Framework (SCPM) developed based on the three resilience dimensions (readiness, response and recovery). The framework enables researchers to seek fundamental knowledge and to pursue further research regarding SCRE assessment. This study also provides practical value offering a guidance for decision-makers considering the trade-off among different capabilities and performance metrics.

Marketing a destination is costly so efficiency of promotion expenditure is critical; identifying improved techniques to achieve this will be of great value. Clustering techniques have sought to identify target markets but are widely criticised for the biases they induce. Persistent Homology identifies key tourist groupings with similar behaviours without the prejudice of the functional forms inherent in most regression models. It further produces more focused, and therefore easily promoted to, markets. Consequently Persistent Homology can highlight obtainable promotion opportunities that otherwise would be missed. This paper provides an example of its application to identify the highest, and lowest, spenders amongst tourists visiting the United Kingdom. We further provide an intuitive theoretical background highlighting the inherent value of the methodology for tourism research. Potential for impact in applications to other aspects of tourism practice is also great as we signpost therein.

In an era of big data corporations have a wealth of employee detail upon which little natural performance inference can be made, yet to discard such is potentially dangerous. Likewise employee applications are full of information that cannot immediately signal performance should they be appointed. Persistent homology considers the topography of data, identifying clusters of behavior which can be associated with performance levels, but for which there are gaps between members in the data cloud; these “holes” may be filled with job applicants. Presenting a theoretical application of persistent homology within human resource management we demonstrate the power of the methodology, the advancements it offers and signpost a wider research agenda on using data not mapped to desired outcomes by conventional statistical techniques.

Purpose Exponential growth in online video content makes viewing choice and video promotion increasingly challenging. While explicit recommendation systems have value, they inherently distract the user from normal behaviour and are open to numerous biases. To enhance user interest evaluation accuracy, the purpose of this paper is to comprehensively examine the relationship between implicit feedback and online video content, and reviews gender differentials in the interest indicated by a comprehensive set of viewer responses. Design/methodology/approach This paper includes 200 useable observations based on an experiment of user interaction with the Youku platform (one of the largest video-hosting websites in China). Logistic regression was employed for its simple interpretation to test the proposed hypotheses. Findings The findings demonstrate gender differentials in cursor movement behaviour, explainable via well-studied splits in personality, biological factors, primitive behaviour and emotion management. This work offers a solution to the sparsity of work on implicit feedback, contributing to the literature that combines explicit and implicit feedback. Practical implications This study offers a launch point for further work on human–computer interaction, and highlights the importance of looking beyond individual metrics to embrace wider human traits in video site design and implementation. Originality/value This paper links implicit feedback to online video content for the first time, and demonstrates its value as an interest capturing tool. By reviewing gender differentials in the interest indicated by a comprehensive set of viewer responses, this paper indicates how user characteristics remain critical. Consequently, this work signposts highly fruitful directions for both practitioners and researchers.