Equitable profit allocation in strategic alliances within technology supply chains remains a formidable challenge, exacerbated by the inability of conventional game-theoretic models such as Nash bargaining and Stackelberg to effectively analyze dynamic, knowledge-driven contributions. These models, constrained by their bilateral and static design, fail to capture the intricacies of multi-agent interactions. This study introduces a hybrid game-theoretic framework utilizing the Shapley value's axiomatic fairness to allocate coalition profits by marginal contributions. The Shapley value surpasses equilibrium-based or power-centric approaches, offering superior suitability for complex multi-agent scenarios. Leveraging meticulously validated data including knowledge investment, absorptive capacity, and coordination costs—this framework employs Monte Carlo simulations to deliver statistically reliable contribution estimates, thereby overcoming the shortcomings of prior methodologies. Applied to an automotive supply chain, the model demonstrates substantial profit gains attributable to optimized knowledge-sharing processes. Absorptive capacity reflects the efficiency of organizational learning, coordination costs indicate potential frictions in collaborative processes, and knowledge investment captures the level of innovation intensity. The proposed model offers a robust, data-driven foundation for developing equitable profit-sharing mechanisms tailored to engineering management needs. At the policy level, it provides a scalable framework for strengthening supply chain resilience. This integrated approach contributes meaningfully to both the advancement of theoretical perspectives and the enhancement of practical strategies in supply chain management.

Sustainable supply chain operations that emphasize environmental and social welfare have drawn significant attention. Supply chain members and governments are increasingly implementing incentive strategies to promote sustainability. This paper develops a game-theoretical model of a sustainable supply chain to examine the effectiveness of cooperative advertising under different government subsidy schemes. We consider that the supply chain consists of a green manufacturer and a retailer who sells a green product through advertising. The manufacturer can share advertising costs with the retailer, a strategy known as cooperative advertising, while the government can offer either the advertising subsidy or the consumption subsidy to support sustainability. Our analytical findings uncover that cooperative advertising may not always benefit the sustainable supply chain. Without the government subsidy, cooperative advertising is beneficial to the manufacturer, consumers, and the government only when the negative effect of cooperative advertising is mild; however, it is always detrimental to the retailer. In contrast, when a government subsidy is provided, not engaging in cooperative advertising is always superior. On the other hand, we find that government subsidies are always effective in facilitating sustainable supply chain operations, with the consumption subsidy outperforming the advertising subsidy. We further extend the model to explore the scenarios under (i) the manufacturer encroachment and (ii) a marginal advertising cost. We find the major findings remain valid in two extensions. This study not only contributes to the existing literature on sustainable supply chain management, but also offers practical insights for firms and governments to design incentive mechanisms.

The increasing prevalence of store brands has attracted both retailers and national manufacturers to participate in store brand operations. However, retailers face a strategic decision regarding whether to disclose the national manufacturer's identity information, especially considering the impacts of the brand spillover effect and online reviews. This research investigates the retailer's disclosure strategy by developing a two-period model and exploring how this decision impacts online review ratings. Our findings illustrate that although the brand spillover effect of disclosing the national manufacturer's identity information can elevate consumers' quality expectations for store brands, the influence of online reviews may diminish or even reverse this benefit. Specifically, a stronger brand spillover effect may adversely affect both parties and thus inhibit the retailer from disclosing such information, which is different from the conventional wisdom. Moreover, we demonstrate that disclosing the national manufacturer's identity is not advantageous for the retailer when the degree of the brand spillover effect is relatively low, consumers are more reference-dependent and rely more on online reviews to formulate quality expectations, and the quality level of the store brand is perceived as moderate. Finally, we consider several extensions, including strategic consumers, distinct pricing power structures, the effect of the national brand's online reviews, asymmetric consumer numbers, and heterogeneous consumer ratings, which either confirm the robustness of our findings or enrich the basic findings.

This integrative editorial introduces the Special Issue (SI) on the evaluation of innovation ecosystem (IE) performance. While ecosystem perspectives have gained prominence across innovation, entrepreneurship, and strategy research, the systematic measurement of ecosystem performance remains theoretically fragmented and methodologically underdeveloped. Drawing on eleven SI articles, this editorial provides an integrative synthesis that advances understanding of how ecosystem performance can be conceptualised, operationalised, and evaluated across dynamic and contextual settings. We aim to identify cross-cutting patterns, unresolved challenges, and emerging research trajectories by synthesising the SI papers. The paper concludes with suggestions for future research aimed at fostering a richer conceptualization of innovation ecosystem performance.

In today's rapidly evolving business environment, organizations are increasingly leveraging artificial intelligence (AI) to unlock the full potential of Lean Six Sigma (LSS). Although the integration of AI with LSS has been identified as a critical area of research, existing review studies have yet to present an in-depth analysis of this intersection. This study aims to uncover critical knowledge clusters and propose future research directions on the integration of AI with LSS. It also seeks to develop an integrative framework elucidating the role of AI in enhancing an organization's dynamic capabilities. A systematic literature review and bibliometric analysis were conducted to synthesize existing research and identify thematic structures. Our analysis reveals that AI research within the LSS context remains in its early developmental stages, despite the pioneering efforts of scholars. The literature on AI and LSS demonstrates a limited effort to incorporate established theories. This study identifies four distinct knowledge clusters and presents five key propositions that offer valuable directions for future research. This study contributes to the literature by providing a comprehensive overview of AI's role in supporting the implementation of LSS practices. It proposes an AI-supported LSS dynamic capability framework, explaining how AI facilitates the sensing, seizing, and reconfiguring cycle within LSS. To bridge theory and practice, a decision matrix is presented, mapping AI to the DMAIC phases with illustrative industry examples.

E-commerce platforms adopt differentiated delivery strategies for various products, where the application and investment of digital technologies play a vital role in shaping delivery services. We consider a hybrid product market in which the platform sells standard products and competes with manufacturers of augmented products that require value-added processing, such as engraving or printing. The platform may provide only basic delivery services for augmented products while applying digital technologies to enhance the delivery of standard products. Alternatively, it may establish value-added processing centers to support a unified delivery strategy and adjust its digital investment accordingly. Counterintuitively, our analysis shows that even when product value and service fees are high, the platform may still refrain from establishing value-added processing centers. Moreover, the adoption of these centers can stimulate further digital investment and enhance coordination, benefiting manufacturers through improved delivery and wider market reach. While customer surplus may decline due to weakened competition, overall welfare can increase through better alignment of processing and delivery decisions. We further demonstrate the robustness of these findings through two model extensions (i.e., asymmetric processing costs and heterogeneous product valuation) as well as numerical verification of the analytical results. This study contributes to the engineering management literature by explaining how platforms align digital technology investment with the establishment of value-added processing centers and how this alignment reshapes delivery strategy, platform-manufacturer interaction, and market outcomes. —

The growing popularity of social media over the past decade has attracted substantial attention from researchers seeking to understand its implications for organizations, resulting in a significant and expanding body of literature. To navigate recent developments and identify future research opportunities, we review 828 relevant articles from leading journals across diverse disciplines, including operations management, information systems, marketing, finance and organizational strategy. This study employs a combination of quantitative and qualitative methods, specifically cocitation network analysis, citation analysis, and thematic analysis, to systematically examine the existing research. Through this approach, we identify six distinct research clusters and their associated thematic areas. Moreover, we map the dynamic evolution trend of each cluster. We also develop a conceptual framework that outlines the antecedents, pathways, and outcomes explored in social media studies. This framework synthesizes the major theoretical foundations underpinning the literature, offering valuable guidance for both academic research and managerial practice. Based on our thematic analysis and conceptual model, we propose three underexplored research directions to support the development of impactful research agendas and advance the field of social media research.

Cyber-security plays a critical role in e-commerce operations; however, the influence of e-tailer's risk preference on the strategic response to cyber-security challenges remains underexplored. This study aims to contribute to this underexplored area by examining a supply chain consisting of a supplier and a risk-sensitive e-tailer, whose risk preference may be risk-seeking, risk-neutral, or risk-averse. We examine the equilibrium outcomes under two scenarios: one with and one without the government penalty on the e-tailer for failing to defend against cyber-attacks. Interestingly, risk aversion of the e-tailer tends to hurt consumers, whereas a risk-seeking attitude could benefit consumers. More importantly, increased risk sensitivity of the e-tailer can lead to higher consumer surplus. However, compared to a risk-seeking e-tailer, a risk-averse e-tailer tends to generate greater social welfare. When the government penalty is implemented, it proves beneficial to consumers irrespective of the e-tailer's risk attitude. Counter intuitively, we also find that with or without the government penalty, the risk-seeking e-tailer invests more in cyber-security than the risk-averse e-tailer. To test the robustness of our main models, we extend the analysis to consider blockchain adoption and alliances among supply chain members. Our research provides valuable insights for policymakers and platforms in enhancing e-commerce platform security management by taking into account the e-tailer's risk attitude.

Many studies on big data analytics provide empirical evidence showing that Big Data Analytics Capabilities (BDAC) do not always result in value creation or competitive advantage. This study sheds light on how entrepreneurial orientation (EO) mediates the relationship between big data analytics capabilities (BDAC) and value creation in SMEs. The study employed a mixed-method design, integrating desk research, executive interviews, and a survey of 447 Canadian SME leaders. Partial least squares structural equation modelling(PLS-SEM) analysis, supported by reliability and validity diagnostics, common-method-bias tests, and model-fit assessments, ensured the robustness of results and enabled triangulation across methods. Findings reveal that EO mediates the BDAC–value creation relationship, suggesting that SMEs with stronger EO leverage BDAC more effectively. Thus, developing EO is essential for unlocking BDAC's value-creation potential. A practical implication for senior executives is that employing EO – proactiveness, risk-taking, and innovativeness – can lead to better leverage of BDAC insights for value creation. A theoretical implication is that EO can complement other organizational capabilities such as analytics culture to extract greater value from BDAC. The empirical evidence provided in this study complements previous studies that have examined other mediators such as analytics culture and absorptive capacity. It provides additional insights into how SMEs can leverage their BDAC for value creation by being more innovative, proactive and encouraging risk-taking across the organization.