PurposeThis paper investigates factors influencing initial and continuous user engagement with emotional AI chatbots for mental health, using technostress theory as the theoretical lens. We conceptualize human interaction, emotional competence and perceived pleasantness as challenge techno-stressors, while perceived risk and system quality defects are treated as threat techno-stressors. User appraisals of technostress, including self-efficacy, personal innovativeness and effort expectancy, are also examined.Design/methodology/approachA two-step research design was adopted, combining user review analysis and semi-structured interviews with a survey tested through hierarchical regression. Both linear and curvilinear relationships were explored, with particular attention to potential U-shaped effects. Moderator analyses of gender, occupation and education were conducted to capture subgroup differences.FindingsResults indicate that human interaction, self-efficacy, personal innovativeness and perceived risk significantly influenced engagement, with U-shaped effects for human interaction and self-efficacy. Emotional competence and pleasantness showed no main effects but were conditionally important (e.g. students, highly educated users). Gender moderated personal innovativeness, occupation moderated pleasantness, effort expectancy and risk, while education moderated emotional competence, system quality and effort expectancy.Originality/valueWe distinguish initial versus continuous engagement with emotional AI chatbots, extend technostress theory into an emotional health support context and reveal both curvilinear effects and subgroup-specific variations. These findings enrich theory by clarifying the dual roles of challenge and threat stressors and offer actionable guidance for tailoring chatbot design to diverse user groups.

Analytics technologies are vital for improving operational performance, yet the configurations that maximise their value remain unclear. Drawing on complexity theory and contingency theory, this study examines how supply chain governance, organisational capability, and environmental conditions interact with different levels of analytics capability. Using secondary data from 205 Chinese high-tech firms over two years, we combine content analysis with fuzzy-set qualitative comparative analysis to uncover configurations that enhance performance. Results show that firms must align governance, capabilities, and environmental strategies with their analytics maturity, aspirational, experienced, or transformed. The study advances understanding of analytics implementation and offers practical guidance.

PurposeGenerative artificial intelligence (GAI) is increasingly embedded in service operations, particularly in crafting managerial responses (MRs) to online negative and positive reviews. Yet, little is known about how firms should optimally allocate responsibilities between human staff and GAI across distinct response tasks, nor how such allocations shape consumer perceptions and downstream behavioral intentions. This study investigates how customers evaluate MRs produced by different agents including humans, GAI and multiple forms of human and GAI collaboration across specific response tasks.Design/methodology/approachWe conducted a randomized online experiment. Main effects were examined using ANOVA with data from 879 participants, and the underlying mechanisms were further analyzed using the PROCESS macro with a parallel mediation model.FindingsOur findings show that to improve customer satisfaction and increase booking intentions, negative reviews are most effectively addressed through an augmented human approach in which GAI creates an initial draft and experienced professional staff refine it. In contrast, positive reviews are best handled solely by experienced professional staff.Originality/valueThis study advances knowledge on how to combine GAI with human efforts in service operations, identifies key psychological mechanisms that mediate the effects of different collaboration modes on consumer perceptions and behavioral intentions and offers actionable guidance for service operations managers. The results suggest that GAI should be deployed strategically to augment, rather than replace, human intelligence.

The rapid advancement of medical technologies has increased electronic obsolescence and e-waste, yet research on this issue within the medical sector remains limited. Existing studies mainly emphasize environmental and health risks from improper disposal, overlooking the role of consumers, particularly elderly individuals with chronic conditions who depend on wearable health devices. This study addresses this gap by examining how sustainable consumption of wearable healthcare devices among senior citizens can help reduce e-waste and support net-zero goals. Using a mixed-methods design, we conducted 20 semi-structured interviews and surveyed 647 senior citizens, analysing the data through Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings reveal that user experience strongly drives sustainable consumption. Moreover, user experience is shaped by design leadership, technology leadership, and brand leadership. Several factors, including self-health management efficacy, perceived severity, perceived vulnerability, and disclosure policy, moderate these relationships, while regulation shows no significant moderating effect. The study highlights the importance of enhancing user experience and leadership attributes in wearable healthcare devices to promote sustainable consumption and mitigate e-waste among elderly users.

The integration of generative AI into healthcare presents a profound trust dilemma for older adults, a demographic highly sensitive to risk and algorithmic opacity. While existing literature primarily focuses on post-adoption AI interaction design, it largely overlooks the critical pre-adoption phase: how strategic marketing communication persuades vulnerable populations to engage with AI. Addressing this gap, this study investigates how marketing communication orientations: goal-oriented (emphasizing instrumental performance) versus duty-oriented (emphasizing safety and ethical governance) shape older adults’ trust and intention to adopt medical AI. Across three scenario-based experiments, we test a comprehensive behavioral model. Study 1 establishes a critical boundary condition, demonstrating that duty-oriented messaging significantly increases adoption intentions and trustworthiness in abstract, high-uncertainty preventive contexts, whereas this advantage diminishes in concrete, outcome-focused diagnostic and treatment settings. Studies 2 and 3 probe the underlying attributional mechanisms and individual differences by testing corporate motive attributions, AI literacy, and AI risk perception. Our findings reveal that duty-oriented framing enhances values-driven motive attributions, which in turn increase trust and adoption intentions among older adults. Furthermore, the persuasive advantage of duty-oriented communication is amplified for seniors with higher AI literacy. This study contributes to the digital innovation literature by shifting the paradigm from algorithmic interaction design to pre-adoption strategic framing and motive attribution. We provide actionable guidelines for practitioners to embed duty and normative transparency into their marketing to bridge the generative AI adoption gap for older adults.

Blockchain technology has garnered significant attention in recent years, with the belief that it can enhance the efficiency of corporate supply chain management. Numerous Chinese listed companies have disclosed the application of supply chain technologies through their financial reports. This study explicitly addresses the research question: Does blockchain attention genuinely improve supply chain efficiency, or is it merely a symbolic response to institutional pressures? Using data from Chinese A-share listed firms from 2013 to 2021, we empirically examine the relationship between blockchain attention and supply chain efficiency (SCE), and test moderating effects of blockchain implementation cost and managers’ digital background. The findings reveal that: (1) blockchain attention is significantly negatively correlated with supply chain efficiency measured by inventory turnover rate, indicating that corporate technology disclosure is a gild disclosure under institutional pressure rather than substantive technological application; (2) the above correlated mechanism is also affected by the experience of executives in supply chain management, supply chain intensity, and the regional supply chain efficiency. This study challenges the optimistic expectations of blockchain technology, uncovers the dissipative path of technological alienation under institutional pressure. It also provides a theoretical basis for regulatory authorities to identify "fake innovation" disclosures and for enterprises to prudently implement digital transformation.

Organisational decision-makers need to manage the ever-evolving frontiers (e.g. autonomy, learning, inscrutability) of artificial intelligence (AI) responsibly, in order to navigate ethical challenges and realise organisational goals. Yet, much of the extant research on AI appears siloed and spans multiple disciplines, making it challenging to learn about management at the frontiers of AI that can support complex decision-making. The evolving expectations regarding managing AI necessitate theoretical advancements in understanding well-developed, evidence-based practices. This study approaches this challenge by situating responsible AI management practices within the design and governance of AI through a sociotechnical lens. Drawing on qualitative data, this study develops an empirically grounded model for managing AI through an interpretive approach. The model (1) delineates multifaceted responsibility (i.e. evidence, epistemic, outcome) related to AI design of and identifies associated design tactics for each dimension; (2) specifies AI governance mechanisms (i.e. structural, procedural, relational) for enacting responsible AI management practices; and (3) identifies organisational performance outcomes (i.e. instrumental, humanistic) of management practices. The research findings contribute to AI ethics, governance, and management literature, offering researchers and practitioners an empirical exposition of managing AI with actionable guidance. By turning to theory for a guide, researchers can approach managerial issues concerning future frontiers of AI with theoretical foundations, and practitioners can plan responsible initiatives that effectively manage emerging ethics challenges.

PurposeThis study uses machine learning approach to detect food safety risks and identify potential solutions by analyzing consumer reviews in the online catering platform.Design/methodology/approachThe approach involves analyzing over 10,000 consumer reviews to develop dictionary of food safety risk-related keywords. These methods allow for the effective identification of specific food safety risks from consumer-generated online reviews. Additionally, a best-worst scaling experiment complemented by mixed logit model analysis ranks potential solutions according to consumer preferences, integrating these insights into practical strategies.FindingsThe study evaluates consumer preference to various food safety risk solutions using best-worst scaling experiment, indicating strong preferences for the food safety traceability system and legislative measures as effective strategies. Meanwhile, other strategies such as science popularization and merchants' elimination evoke mixed reactions, reflecting diverse consumer perceptions on their efficacy. These insights are pivotal for shaping targeted, effective food safety practices in online catering platforms.Research limitations/implicationsWhile the findings offer significant insights, the research is limited by the specificity of the data to online reviews, which may not fully capture the breadth of food safety risks encountered by consumers. Future research could expand the scope of data sources to include more direct consumer interactions and feedback mechanisms.Practical implicationsThis study provides valuable strategies for online catering platforms to integrate the solutions of addressing food safety risks into their business models effectively.Social implicationsConsumer preferences for online food safety solutions determine their efficacy. Few studies have examined this problem from consumers' perspectives. This study reminded academics of consumer desire for different solutions. The preceding data analysis showed that respondents expect internet food safety issues can be solved before they occur. Thus, developing food safety solutions before they reach customers is advised.Originality/valueThis research extends the conventional use of text mining for sentiment analysis by applying it within the food safety context in the online catering platform. It uniquely combines the analytical rigor of machine learning with practical marketing strategies to address a critical public health issue.

To address growing geographical disparities in healthcare access and quality, connected health platforms (CHPs) have emerged as promising solutions. However, rapidly scaling CHPs poses significant challenges, particularly in managing tensions among multiple entities. This study examines the digital scaling process of a CHP through the lens of paradox theory, focusing on tensions and their management in resource-constrained environments. We conducted a 13-year longitudinal case study of a CHP encompassing over 300 hospitals in China. Our findings reveal three distinct yet interconnected phases of digital scaling: digital foundation building; system integration and governance; and continuous improvement and innovation. We demonstrate that tensions evolve over time during the scaling process, with one dominant type prevailing in each phase, challenging previous assumptions that different tensions emerge simultaneously across multiple entities. We also identify phase-specific "both-and" responses employed by rural hospitals to manage tensions despite experiencing resource constraints. We provide guidance to organizations operating in resource-constrained environments on the management of paradoxical tensions across a complex digital scaling process.

This study advances our understanding of how multinational enterprises (MNEs) can foster local retailer commitment in emerging markets. Using a dyadic dataset of 153 brand-retailer pairs in China's B2B consumer electronics sector, we tested a conceptual framework examining how relationship strength, mutual knowledge acquisition, and knowledge asymmetries affect retailers' commitment to MNE brands. The findings confirm that strong brand-retailer relationships, brand knowledge acquired by retailers, and retailer knowledge acquired by brands all significantly enhance retailer commitment. Furthermore, the results support the moderating role of mutual knowledge exchange and confirm that knowledge asymmetries significantly shape commitment levels: a brand's knowledge advantage increases retailer commitment, while a retailer's knowledge advantage reduces it. This study thus enriches the B2B and international marketing literatures by showing that managing retailer commitment in emerging markets is not just about building strong relationships, but about strategically leveraging and aligning knowledge resources, both symmetrically and asymmetrically, within interdependent channel structures.

Academic Summary This article draws on the attention-based view (ABV) to examine whether, how, and under what conditions top management team (TMT) attention to responsible artificial intelligence (AI) influences firm innovation. We developed a 480-word responsible AI dictionary grounded in 155 academic sources and 527 corporate case descriptions, and applied it to 2452 S&P 500 earnings call transcripts (2011-2021) using natural language processing (NLP) and large language model (LLM) techniques, yielding 2670 firm-year observations. Linking these measures to US patent data, we find that greater responsible AI attention predicts more and higher-impact patents. The effect is stronger in low-technology industries and under short-term investor pressure, while the presence of a chief technology officer (CTO) does not amplify it. Mechanism analyses reveal that responsible AI attention fosters innovation by increasing investment in AI-relevant human capital and mitigating innovation risk. Theoretically, this article enriches the AI and innovation management literature by positioning responsible AI attention as a dynamic strategic asset that mobilizes resources, reduces risk, and enables contextual adaptation. Practically, findings suggest that firms can strengthen innovation by prioritizing managerial attention to responsible AI, distributing responsibility beyond technical specialists, balancing ethical safeguards with strategic flexibility, and aligning governance with investor and industry conditions.Managerial Summary This article examines how managerial attention to responsible artificial intelligence (AI) can enhance firm innovation. Using text analytics on 2452 earnings call transcripts from S&P 500 firms (2011-2021) and a panel of 2670 firm-year observations linked to patent outcomes, we show that firms whose top management teams (TMT) devote greater attention to responsible AI produce more and higher-impact patents. This effect is stronger in low-technology industries and when firms face short-term investor pressure; it is not amplified by having a chief technology officer (CTO). In practice, sustained attention to responsible AI tends to build AI-related skills and reduce project risk, thereby supporting a more reliable innovation pipeline. Executives should treat responsible AI as a strategic priority rather than a compliance task by establishing cross-functional governance, investing in role-based governance training, and sharing accountability across the C-suite. Innovation managers can embed ethics checkpoints (bias audits, design reviews) into project workflows to enhance stability and organizational learning. Policymakers can reinforce responsible innovation by providing clear regulatory frameworks and incentives that align ethical safeguards with competitiveness. Together, these actions can help build more durable organizational capability for responsible innovation and support long-term performance and adaptation to ongoing technological change.

This study explores how the financial market environment reshapes corporate social responsibility using a quasi-natural experiment provided by China's New Asset Management Regulation. Our research focuses on the adaptive strategies of non-financial firms in response to stringent financial market regulation, and we use a generalized DID model to identify the causal link between the NAMR and CSR. The findings reveal a decline in non-financial firms' CSR performance following the more stringent financial market regulation. Mechanism testing suggests that the negative impact is primarily due to the reduction in the return on financial asset investments. Furthermore, we assess the heterogeneity influences of financial regulation on the three dimensions of CSR (environment, society, and governance). Our analyses underline a significant decrease in the environment and governance CSR among non-financial firms, while no significant impact is observed on the social dimension of CSR. This study contributes to a greater understanding of the relationship between financial market regulation and CSR. It offers valuable insights for the development of effective policy guidance to ensure the optimal functioning of the real economy.

The superior computing power of modern technologies, combined with their ability to provide insights through analytics on rich data sets, is helping companies enhance performance across all areas. Consequently, companies are increasingly deploying Artificial Intelligence (AI) to synchronise, reshape, and restructure their resources to achieve business objectives and enhance customer engagement in marketing efforts. AI facilitates marketing strategies by delivering personalised experiences to both local and international customers. However, the rapid pace of innovation in AI and the absence of comprehensive regulatory frameworks pose concerns. These gaps make it challenging for companies to ensure that their AI applications are compliant and safeguard stakeholder interests. Therefore, transitioning into the AI domain carries risks for companies aiming to maintain an ethical and responsible image. To mitigate these risks, integrating responsible AI usage into corporate policies can serve as a critical first step for companies.

Although workforce diversity, equity, and inclusion (DEI) have been recognized as essential components in healthcare organizations, robust empirical research is scarce on how workforce DEI influences patient safety outcomes, particularly concerning the boundary conditions that may moderate this relationship. This study analyzes a longitudinal dataset from 2017 to 2021 that includes DEI metrics, staff-reported patient safety incidents, and employee feedback on DEI from Glassdoor and Indeed for 120 NHS Trusts in England’s acute care sector. We examine workforce DEI through both its demographic and experiential dimensions to provide a comprehensive view. Employing a double machine learning approach, our findings indicate that a one-unit increase in workforce DEI scores is associated with a reduction of 8.108 patient safety incidents per 1000 admissions. Moreover, regions with greater patient racial diversity and healthcare organizations with lower complexity experience significantly greater benefits from DEI initiatives. This study provides healthcare policymakers and institutions with actionable insights to strategically tailor DEI initiatives and effectively improve patient safety.

Online reviews exert a considerable influence on consumer purchase behavior, yet there remains ambiguity about the most effective managerial response strategies for positive and negative reviews. Addressing this gap, our study introduces a Strategy-Aware, Deep Learning-Based Natural Language Processing (Sa-DLNLP) model designed to optimize firm responses. The proposed model underwent rigorous evaluation through a human-coded study and was subsequently validated by a separate user response study. Our findings reveal that active-constructive responses significantly enhance the impact of positive reviews, whereas passive-constructive strategies are more effective in mitigating the damage from negative reviews. Additionally, the study underscores the importance of concise, personalized, and prompt responses across the board. Interestingly, responses that are overly explanatory, excessively empathetic, or challenge customers were found to be counterproductive when dealing with negative reviews. This study not only demystifies the art of managing online reviews but also offers an advanced deep learning methodology that can directly benefit the disciplines of Information Systems and Management.

Drawing from the Knowledge Based View (KBV) of the firm and Contingency Theory, this paper examines the extent to which the relationship between Customer Analytics (CA) and new product performance is contingent on the strategic fit of CA with certain internal and external contingencies. The paper first conducts a multiple case study based on secondary data analysis. It then undertakes an empirical analysis based on a survey data of 249 high and medium tech firms based in China. We find that while some internal contingencies (such as exploitative learning strategy and market knowledge breadth) negatively moderate the effect of CA on new product performance, others (such as internal capability and knowledge integration mechanisms) mediate its effect on performance. Technological turbulence, as an external contingency, was found to reduce the positive impact of CA deployment on new product performance. This study contributed to the literature by focusing on how several internal and external contingencies of a firm may affect the relationship between CA and new product performance.

As mergers and acquisitions (M&A) increasingly become a staple in emerging markets, understanding the role of leadership in these activities is paramount. Despite the proliferation of research on M&A integration and leadership's impact on performance, there is a notable scarcity of studies that integrate these elements into a unified framework, particularly within non-Western contexts. Addressing this gap, this study constructs and empirically tests a framework that evaluates the influence of brand-specific transformational leadership (TFL) on M&A performance in China. Utilizing a sample of 295 respondents from B2B markets, the research confirms the significant role of brand-specific TFL in enhancing M&A integration. A direct correlation was established between the degree of integration and improved employee satisfaction and engagement, reduced top management team (TMT) turnover, and overall M&A success. However, the speed of integration did not show a significant impact on these outcomes. These findings underscore the critical role of transformational leadership in shaping successful M&A strategies, particularly in emerging markets. The study not only contributes to the theoretical understanding of how specific leadership styles influence M&A outcomes but also offers practical guidance for corporations in emerging economies to design more effective integration strategies by focusing on leadership development and branding alignment.

Prior research has primarily focused on electronic Word of Mouth (e-WOM) in consumer markets, lacking in-depth exploration of its dynamics within B2B contexts. Addressing this gap, our study investigates e-WOM in B2B markets on industrial internet platforms. Specifically, we examine how digital platform capabilities can be leveraged to create positive e-WOM through multi-actor value co-creation processes. Utilizing a case study approach, we analyze heterogeneous data from multiple sources to understand how digital platform capabilities-such as digital ecological capability, digital coordination capability, and digital innovation capability-promote multi-actor value co-creation (including intrapreneurship within the focal firm, support for complementary products or services, and users' reciprocal participation), thereby disseminating positive e-WOM in B2B markets. Our findings provide insights into e-WOM in B2B markets and reveal the mechanisms of multi-actor value co-creation through which digital platform capabilities activate e-WOM. This study enriches the theoretical framework of e-WOM in B2B contexts and underscores the significance of digital transformation in enhancing interaction and collaboration among multiple actors on industrial internet platforms.