Purpose- This study examines how users' experiences of AI inclusion, reflected in feelings of belonging and authenticity during human-AI interaction, are associated with user satisfaction and interactional justice. Design/methodology/approach-The study assesses the proposed relationships between the constructs using two separately analyzed datasets: 219 Amazon Alexa users and 577 ChatGPT users. Regression analysis was used to assess the proposed relationships within each dataset. Additionally, the study adopts Latent Profile Analysis (LPA) to identify distinct AI user profiles. Findings-The findings indicate that AI inclusion dimensions (i.e., belonging and authenticity) are positively associated with user satisfaction and interactional justice. The results of LPA reveal different user profiles based on inclusion, satisfaction, and justice scores. Research limitations/implications-While this study assesses AI inclusion in the contexts of Amazon Alexa and ChatGPT, future research may explore AI inclusion in other AI systems and employ longitudinal or experimental methodologies. Previous research has largely approached AI inclusion through access, participation, or design-oriented perspective, whereas this study examines AI inclusion from a micro-level experiential perspective. Practical implications-The findings of this study have several implications for AI developers, organizations, policy makers, society, and education by showing that AI systems should be designed not only for functional performance but also to foster users' feelings of belonging and authenticity. Hence, users' emotional experiences should be treated as an important dimension of AI evaluation and governance. Originality-This study advances research on AI and inclusion by conceptualizing AI inclusion as users' felt experience during human-AI interactions rather than access to, participation in, or governance of AI systems. It defines AI inclusion as a micro-level experiential construct and highlights its importance for understanding user satisfaction and interactional justice.
Purpose The technological advancements aid in improving healthcare quality and public accessibility through the adoption of online pharma (OP). However, the adoption of online pharma service (OPS) faces resistance; therefore, it is necessary to understand what motivates consumers towards its wide acceptance with enhanced technology. This study aims to investigate the critical factors and their causal relationships influencing OPS adoption within Indian context. Design/methodology/approach Fourteen motivational factors for OPS adoption have been extracted from the literature and from industry veterans. Two different multi-criteria decision-making (MCDM) methods, namely, interpretive structural modelling (ISM) and decision-making trial and evaluation laboratory (DEMATEL), have been used to examine the motivational factors for OPS adoption. In addition, common motivational factors from each method, their hierarchies and interrelationships have been established. Findings Results of ISM method revealed that lower prices (MF13), discounts or offers (MF12) and 24×7 availability (MF5) were the critical motivational factor that influenced the OPS adoption. DEMATEL method showed that confidentiality (MF1), reduced health risks (MF2) and enhanced accessibility for individuals with disabilities (MF9) were the outcome variables and revealed a high degree of dependence on fast shipping options (MF6), medicine shortage (MF8) and quality online choices (MF11). Practical implications The findings of this research will be beneficial to online pharmacy companies, pharmaceutical manufactures, logistics and supply chain partners, policy makers and regulators to devise strategies for facilitating the development of enhanced strategic approaches for inflowing or expanding OPS businesses in emerging economies. This study will also assist in designing consumer-centric health policies by understanding consumer motivational factors associated with OPS adoption, in line with the UN-SDGs #3 and #11. Originality/value An ISM–DEMATEL based hierarchical model and causal analysis for OP adoption factors, demonstrating their contextual interrelationships, has been proposed depicting a unique contribution towards understanding and facilitating the adoption of OPS in Indian context.
Purpose This study aims to develop a process-oriented framework integrating digital technologies, organizational structures, capabilities and performance outcomes to clarify the digitalization paradox and advance understanding of digital transformation (DT) as an enterprise-level renewal process. Design/methodology/approach The study uses a conceptual synthesis approach integrating adaptive structuration theory (AST), the resource-based view (RBV) and dynamic capabilities within an input–process–output analytical structure. AST explains how digital technologies and organizational structures are recursively enacted through human agency; RBV clarifies how enacted configurations generate sustained competitive advantage; and dynamic capabilities bridge process and performance by enabling sensing, seizing and transforming of the resource base. The framework identifies three interrelated transformation pathways – technology-driven, structure-driven and DT-driven – that jointly shape capability development and strategic renewal. Findings The framework demonstrates that DT outcomes depend not solely on technological investment but on recursive alignment among technology enactment, structural adaptation and capability reconfiguration. By organizing these mechanisms into iterative learning cycles, the model provides a process-based explanation for the digitalization paradox: transformation failures often stem from misaligned structuration and insufficient capability renewal rather than technological deficiencies. Sustained performance emerges when digital technologies, organizational structures and dynamic capabilities co-evolve over time. Originality/value This study advances DT research by integrating process theory and resource theory into a unified temporal logic. It reconciles AST’s duality of structure with RBV’s value-creation perspective through a dynamic capability lens, offering both theoretical clarity and managerial guidance. The framework shifts attention from isolated technology adoption toward recursive organizational renewal, providing scholars and practitioners with a structured approach to diagnosing and managing DT in dynamic environments.
Purpose Decision support systems (DSS) are increasingly integrated into high-stakes decision-making across industries, with the core objective of delivering timely, data-driven insights that inform and enhance strategic and operational choices. Despite decades of development, many DSS initiatives fail to deliver expected value, not due to lack of technology, but due to gaps in problem definition, design alignment, implementation readiness or contextual fit. Therefore, understanding the considerations, challenges and opportunities across the DSS life cycle, the purpose of this paper is to advance both the theoretical foundations and practical applications of DSS. Design/methodology/approach The study uses the theory of innovation diffusion, which underscores the stages of digital innovation of Discovery, Development, Diffusion and Impact. The inquiry employs a two-phase mixed-method synthesis of DSS journal articles published over the past five decades in the Scopus database. In Phase one, latent themes were identified through text mining; in Phase two, qualitative content analysis was used for theoretical discourse. Findings The resulting framework provides a life cycle-integrated perspective characterizing (discovering) Decision Environment, (developing) Decision Support (DS) artifacts, (diffusing) DS applications and (impact on) domains for evaluating DSS contributions. In continuation, to advance the DSS discourse we pose pertinent research questions for the future scholarship. Originality/value This study integrates the theory of innovation diffusion with the DSS life cycle to present a comprehensive framework. Unlike prior reviews, this research identifies latent thematic structures within five decades of DSS scholarship using a mixed-methods synthesis, thereby revealing underexplored theoretical and practical opportunities for advancing cumulative DSS theorization.
Purpose The emergence of generative artificial intelligence (GenAI) has substantially transformed the e-retailing landscape by enhancing business efficiency and long-term competitiveness. Nevertheless, the factors influencing retailers’ continued adoption of GenAI and the effects of this adoption on e-retailing performance are not yet fully understood. This study aims to elucidate the mechanisms underlying GenAI continuance intention and business performance by applying the extended Motivation–Opportunity–Ability (MOA) theory. Design/methodology/approach A quantitative research design was employed to collect data from 537 retailers through a structured online survey, and the research model was validated using structural equation modeling. Findings The findings indicate that the extended MOA theory offers a comprehensive explanation of retailers’ post-adoption intentions regarding GenAI and its role in enhancing e-retailing performance. Utilitarian and hedonic motivations, as well as the opportunity and ability to employ GenAI, significantly influence continuance intention. Moreover, continuance intention toward GenAI serves as a key driver of e-retailing performance. Originality/value This study provides a robust theoretical foundation and practical implications for retailers and AI providers operating in emerging economies. By applying the extended MOA framework from a retailer-centric perspective, this study advances research on GenAI and highlights the essential role of motivation, alongside opportunity and ability, in sustaining GenAI adoption. The findings offer comprehensive insights into the significance of GenAI for retail operations and present actionable strategies to support sustained performance within the context of digital transformation.
