Trolling on social media has a profound impact on its victims, yet existing literature offers a limited understanding of the factors driving this behavior. This study applies deindividuation and contagion theories to explore the phenomenon, surveying 337 Facebook users and 275 Instagram users and analyzing the data using SEM-PLS and fsQCA methods. The SEM results indicate that digital anonymity and dispersed collectivity both directly and indirectly impact trolling behavior, mediated by a loss of self-consciousness and a diffused sense of responsibility. The fsQCA analysis reveals four distinct equifinal configurations that predict trolling behavior, one for each platform, providing new insights into the research on trolling. This study contributes to the theoretical understanding of trolling and offers practical implications for addressing this issue.

The data-centric revolution generally celebrates the proliferation of business analytics and AI in exploiting firm’s potential and success. However, there is a lack of research on how the unintended consequences of AI integrated business analytics (AI-BA) influence a firm’s overall competitive advantage. In this backdrop, this study aims to identify how factors, such as AI-BA opacity, suboptimal business decisions and perceived risk are responsible for a firm’s operational inefficiency and competitive disadvantage. Drawing on the resource-based view, dynamic capability view, and contingency theory, the proposed research model captures the components and effects of an AI-BA opacity on a firm’s risk environment and negative performance. The data were gathered from 355 operational, mid-level and senior managers from various service sectors across all different size organisations in India. The results indicated that lack of governance, poor data quality, and inefficient training of key employees led to an AI-BA opacity. It then triggers suboptimal business decisions and higher perceived risk resulting in operational inefficiency. The findings show that operational inefficiency significantly contributes to negative sales growth and employees’ dissatisfaction, which result in a competitive disadvantage for a firm. The findings also highlight the significant moderating effect of contingency plan in the nomological chain.

The value of customer analytics (CA) and artificial intelligence (AI) has been discussed separately at the forefront of research for business, marketing, and operations management. In spite of the strategic importance of CA and AI, there has been a paucity of research regarding the role of AI in operationalizing customer analytics (CA) capability. To address the gap, this study draws on a systematic literature review and thematic analysis for identifying the value-based CA capability antecedents that operationalize through AI in the context of retailing. The findings of this study extend the resource-based view (RBV)-capability theory in the spectrum of market orientation, and technology orientation to generate a better intelligence of CA capability in the retail context; while also providing theoretically grounded guidance to the practitioners. Hence, retail practitioners will likely be able to engage customers and enhance customer delight by incorporating CA capability dimensions, which is powered by AI.

Research on AI has gained momentum in recent years. Many scholars and practitioners increasingly highlight the dark sides of AI, particularly related to algorithm bias. This study elucidates situations in which AI-enabled analytics systems make biased decisions against customers based on gender, race, religion, age, nationality or socioeconomic status. Based on a systematic literature review, this research proposes two approaches (i.e., a priori and post-hoc) to overcome such biases in customer management. As part of a priori approach, the findings suggest scientific, application, stakeholder and assurance consistencies. With regard to the post-hoc approach, the findings recommend six steps: bias identification, review of extant findings, selection of the right variables, responsible and ethical model development, data analysis and action on insights. Overall, this study contributes to the ethical and responsible use of AI applications.

Understanding the growth paths of artificial intelligence (AI) and its impact on branding is extremely pertinent of technology-driven marketing. This explorative research covers a complete bibliometric analysis of the impact of AI on branding. The sample for this research included all 117 articles from the period of 1982-2019 in the Scopus database. A bibliometric study was conducted using co-occurrence, citation analysis and co-citation analysis. The empirical analysis investigates the value propositions of AI on branding. The study revealed the nine clusters of co-occurrence: Social Media Analytics and Brand Equity; Neural Networks and Brand Choice; Chat Bots-Brand Intimacy; Twitter, Facebook, Instagram-Luxury Brands; Interactive Agent-Brand Love and User Choice; Algorithm Recommendations and E-Brand Experience; User-Generated Content-Brand Sustainability; Brand Intelligence Analytics; and Digital Innovations and Brand Excellence. The findings also identify four clusters of citation analysis—Social Media Analysis and Brand Photos, Network Analysis and E-Commerce, Hybrid Simulating Modelling, and Real-time Knowledge-Based Systems—and four clusters of co-citation analysis: B2B Technology Brands, AI Fostered E-Brands, Information Cascades and Online Brand Ratings, and Voice Assistants-Brand Eureka Moments. Overall, the study presents the patterns of convergence and divergence of themes, narrowing to the specific topic, and multidisciplinary engagement in research, thus offering the recent insights in the field of AI on branding.

To revamp with new creative age characterized by ongoing digital transformation, more and more industries are capitalizing on digital innovation for their sustainable business growth. Drawing on a systematic literature review, thematic analysis, and using the theories of dynamic capabilities and market orientation, this research scrutinizes a systematic process for developing analytics-based data-driven innovation (DDI). Findings suggest a standardized seven-step process for DDI, including product conceptualization, data acquisition, data refinement, data storage and retrieval, distribution, presentation, and market feedback.

Textbooks are an important information resource for tertiary students. E-textbooks are now widely available and accessible to students offering them distinct advantages over print books at lower costs. However the uptake of e-textbooks has been slow and student preferences for either medium are not well understood. This study adopts a qualitative approach using an expectation-confirmation theory (ECT) lens and revealed causal mapping to understand from students participating in focus groups, their intentions to continue using e-textbooks. We extend ECT by including two new constructs: perceived quality and perceived value. The results assist in interpreting students’ behavior regarding intentions to continue using e-textbooks and the efficacy of the extended ECT model.

Increasingly, e‐books are becoming alternatives to print books in academic libraries, thus providing opportunities to assess how well the use of e‐books meets the requirements of academics. This study uses the task‐technology fit (TTF) model to explore the interrelationships of e‐books, the affordances offered by smart readers, the information needs of academics, and the “fit” of technology to tasks as well as performance. We propose that the adoption of e‐books will be dependent on how academics perceive the fit of this new medium to the tasks they undertake as well as what added‐value functionality is delivered by the information technology that delivers the content. The study used content analysis and an online survey, administered to the faculty in Medicine, Science and Engineering at the University of New South Wales, to identify the attributes of a TTF construct of e‐books in academic settings. Using exploratory factor analysis, preliminary findings confirmed annotation, navigation, and output as the core dimensions of the TTF construct. The results of confirmatory factor analysis using partial least squares path modeling supported the overall TTF model in reflecting significant positive impact of task, technology, and individual characteristics on TTF for e‐books in academic settings; it also confirmed significant positive impact of TTF on individuals' performance and use, and impact of using e‐books on individual performance. Our research makes two contributions: the development of an e‐book TTF construct and the testing of that construct in a model validating the efficacy of the TTF framework in measuring perceived fit of e‐books to academic tasks.

The aim of this research is to advance both the theoretical conceptualization and the empirical validation of trustworthiness in mHealth (mobile health) information services research. Conceptually, it extends this line of research by reframing trustworthiness as a hierarchical, reflective construct, incorporating ability, benevolence, integrity, and predictability. Empirically, it confirms that partial least squares path modeling can be used to estimate the parameters of a hierarchical, reflective model with moderating and mediating effects in a nomological network. The model shows that trustworthiness is a second-order, reflective construct that has a significant direct and indirect impact on continuance intentions in the context of mHealth information services. It also confirms that consumer trust plays the key, mediating role between trustworthiness and continuance intentions, while trustworthiness does not have any moderating influence in the relationship between consumer trust and continuance intentions. Overall, the authors conclude by discussing conceptual contributions, methodological implications, limitations, and future research directions of the study.