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A “state-driven” approach suggests that colonists use census categories to rule. However, a “society-driven” approach suggests that this state-driven perspective confers too much power upon states. A third approach views census-taking and official categorization as a product of state–society interaction that depends upon: (a) the population's lay categories, (b) information intellectuals' ability to take up and transform these lay categories, and (c) the balance of power between social and state actors. We evaluate the above positions by analyzing official records, key texts, travelogues, and statistical memoirs from three key periods in India: Indus Valley civilization through classical Gupta rule (ca. 3300 BCE–700 CE), the “medieval” period (ca. 700–1700 CE), and East India Company (EIC) rule (1757–1857 CE), using historical narrative. We show that information gathering early in the first period was society driven; however, over time, a strong interactive pattern emerged. Scribes (information intellectuals) increased their social status and power (thus, shifting the balance of power) by drawing on caste categories (lay categories) and incorporating them into official information gathering. This intensification of interactive information gathering allowed the Mughals, the EIC, and finally British direct rule officials to collect large quantities of information. Our evidence thus suggests that the intensification of state–society interactions over time laid the groundwork for the success of the direct rule British censuses. It also suggests that any transformative effect of these censuses lay in this interactive pattern, not in the strength of the British colonial state.

As a result of COVID-19 outbreak, the rapid digital transformation has drastically changed the way we work as individuals as well as the organisations. Our constant engagement online has become a natural phenomenon. Whenever we go online, we leave a trail of digital data behind us either actively or passively. For a common customer, employee or even an employer, issues regarding data protection and data security are challenging. For instance, who owns the Digital Footprint Data? Do the employees have the skills to protect customers' data online? How are small and medium enterprises (SMEs) handling the ethical issues around digital footprints? These are some primary questions SMEs are currently facing in the transition of digital transformation. These questions have profound ethical implications for SMEs' digital footprints during the COVID-19 outbreak and beyond, which have been explored further in this study.

This chapter examines the significant transformations brought about by the incorporation of service robots in the ever-changing retail industry. In the retail industry, advanced technologies, including artificial intelligence (AI), co-bots, robotics, and automation, are transforming the experiences of customers and employees in response to the surge in human–robot collaboration (HRC) and worldwide investments in innovative projects. The primary goal of the research is to examine the impact of incorporating service robots on employees’ willingness to work in a retail sector that fosters collaboration between humans and robots while improving the performance. The research highlights the key factors influencing employee perspectives and inclinations for collaborating with service robots in retail environments, as determined by an in-depth review of academic research and industrial insights. The results demonstrate the positive influence of service robots on improving HRC, optimising inventory management, and enhancing overall operational efficiency in the retail sector. The conclusion emphasises the need to adopt a holistic approach to successfully use the potential of service robots, with the aim of establishing a retail ecosystem that is both sustainable and harmonious. The presence of service robots in the retail industry has significant implications, offering a competitive advantage. The research results reveal stakeholders’ perspectives on the crucial role of service robots in driving future development and maintaining long-term benefits. This chapter offers a comprehensive review of innovative technology in the retail marketplace, offering significant insights into the transformative potential of service robots.

Sports advertisements such as the Super Bowl showcase products and brands that have invested increasingly large sums financially to gain viewers’ attention. However, how audio features in advertisements impact viewers' behavior remains unexplored.

Using the lens of signaling theory, this research uses advanced data analytics of voice and music audio in Super Bowl advertisements to examine its impacts on viewers.

Results show that advertisement viewers prefer more voiced frames and have a greater liking behavior of voiced frames with a low intensity (less loud) and a higher F1 frequency, which is typically associated with male vocal tracts. A fast music tempo works better for longer advertisements. The model controls for various types of ad appeals. The research underlines the importance of voice and music in signaling successful brand features that are likely to increase the ad-liking behavior of consumers (positive effect).

The current research implies that brands advertising through sports ads must carefully select voice actors and music in order to provide the most positive signals for a brand to have the most significant effect and, thus, a greater return on the high sums invested in the ads.

First, this research contributes in terms of a new research process for using audio analytics in advertising. The detailed research process outlined can be used for future research examining audio and music from advertisements. Second, our findings provide additional support to the important role of voice features (e.g. intensity and frequency) as signals in inducing responses from consumers (Biswas et al., 2019; Hagtvedt and Brasel, 2016). Third, the study surfaces a new theoretical association: the effect of tempo in moderating the relationship between duration and propensity to like an ad.

The study aims to investigate the impact of board gender diversity (BGD) on firm performance (FP) by testing two hypotheses – the existence of a positive relationship between BGD and FP, and the moderating role of a critical mass of female directors on FP. The study also explores whether the association varies across different industries.

The authors collect data using Bloomberg and CMIE Prowess, from the Bombay Stock Exchange (BSE) 500 index for the period 2008–2018 and employ a robust statistical methodology (Dynamic Panel Data Model).

A critical mass of female directors positively moderates and strengthens the relationship between BGD and FP. The study fails to find evidence of a direct association between BGD and FP. The study also finds evidence of industry effects.

Though we use a very robust statistical methodology, any modifications in the methodology or choice of a different methodology are likely to change the results. Moreover, some of the findings are statistically significant at the 10% level.

The findings of our study hold particular significance for emerging economies like India where regulatory initiatives aim to enhance gender diversity within boardrooms.

The study contributes to the critical mass literature by examining the association between a critical mass of female directors as a moderating variable of BGD and FP. Further, the study also identifies those industries which show a positive association between FP and BGD.

The emergence of widely available Generative Artificial Intelligence (GenAI) tools, such as ChatGPT, presents both opportunities and threats for higher education. This study aims to investigate the factors that influence students’ current use of GenAI and students’ perceptions of how GenAI can facilitate learning, as well as informs recommendations for institutional policies related to GenAI.

A mixed-method approach was used. A survey of undergraduate business students was followed by a case study that required students to use GenAI as part of a homework assignment and then reflect on their learning experience.

Students used GenAI more frequently when they perceived that it helped their learning outcomes and when it was perceived as a social norm. Conversely, the perception that GenAI was cheating reduced its usage. Male (vs female) students used GenAI more frequently. Students preferred institutional policies that allowed the use of GenAI but also set clear boundaries for its use. They reported that the assignment that required the use of GenAI enhanced their learning experience.

Results from the survey and case study imply that institutions should set policies establishing clear boundaries for the use of GenAI while encouraging and training faculty to incorporate GenAI into classroom assignments. Doing so can facilitate student learning and train students on an important technology that prepares them for the workforce.

This study provides insight into students’ usage of GenAI, explores factors that predict its usage, provides policy recommendations for educational institutions and offers a template for incorporating GenAI into classroom assignments.

Despite the growing interest in closed-loop manufacturing, there is a lack of comprehensive frameworks that integrate product development, production processes, people and policies (4Ps) to optimize sustainable manufacturing performance. This study investigates the influence of the four Ps of closed-loop manufacturing systems (product development, production processes, people and policies) on sustainable manufacturing performance (SMP).

To investigate the influence of the four Ps on SMP, a hybrid analytical model was employed, combining structural equation modeling (SEM) with artificial neural networks (ANN). Data were collected through a structured survey administered to 353 manufacturing firms in Malaysia. SEM was used to assess the relationships between the variables, while ANN was employed to capture nonlinear relationships and improve prediction accuracy.

The research findings demonstrate that product development practices, including eco-design, life cycle assessment and resource planning, exert the most significant influence on SMP. Furthermore, implementing green and lean manufacturing techniques, energy modeling and material utilization/toxicity planning significantly enhances sustainability outcomes. While the social setting (employee motivation, turnover and work–life quality) does not directly impact SMP, it plays a pivotal role in facilitating the implementation of internal environmental policies. Moreover, environmental management practices, both mandatory and voluntary, serve as intermediaries between the four Ps and SMP within closed-loop manufacturing systems.

The findings offer valuable insights for policymakers, industry leaders and manufacturing organizations. By prioritizing product development, implementing green and lean manufacturing practices and fostering a positive social setting, organizations can significantly enhance their sustainable performance. Additionally, the study highlights the importance of effective environmental management practices in mediating the relationship between other factors and SMP.

This study contributes to the literature by providing a comprehensive framework for understanding the factors that drive sustainable manufacturing performance. The hybrid SEM-ANN model offers a robust and innovative approach to analyzing the complex relationships between the four Ps and SMP.

The advancement of artificial intelligence (AI) has brought intelligent online shopping experiences to customers. AI-powered retail platforms deliver personalized shopping experiences through tailored recommendations, promotions and assistance. Given the increasing preference for online shopping, it is crucial to explore methods to optimize the adoption of AI-powered retail platforms. To address this, this study aims to examine the impact of technology readiness motivators (optimism and innovativeness) and inhibitors (discomfort and insecurity) on perceived trust, perceived usefulness and purchase intention toward AI-powered retail platforms.

Data were collected from 276 customers in Egypt, primarily from the millennial and Gen Z demographic segments. The collected data were then analyzed using the statistical package for social sciences (SPSS) and partial least squares structural equation modeling (PLS-SEM).

The findings revealed that optimism, innovativeness and discomfort significantly influence perceived trust, while optimism, insecurity and perceived trust significantly influence perceived usefulness. Both perceived trust and usefulness are significant predictors of purchase intention. Perceived trust mediates the effects of technology readiness motivators on perceived usefulness and purchase intention. Moreover, perceived usefulness mediates the effects of technology readiness motivators, insecurity and perceived trust on purchase intention.

To date, there are few investigations regarding the acceptance and adoption of AI-powered retail platforms in developing countries. Thus, this study offers valuable theoretical and practical implications in the context of smart retail technology adoption.

The purpose of this study is to investigate consumers' emotional responses to artificial intelligence (AI) defeating people. Meanwhile, the authors investigate the negative spillover effect of AI defeating people on consumers' attitudes toward AI companies. The authors also try to alleviate this spillover effect.

Using four studies to test the hypotheses. In Study 1, the authors use the fine-tuned Bidirectional Encoder Representations from the Transformers algorithm to run a sentiment analysis to investigate how AI defeating people influences consumers' emotions. In Studies 2 to 4, the authors test the effect of AI defeating people on consumers' attitudes, the mediating effect of negative emotions and the moderating effect of different intentions.

The authors find that AI defeating people increases consumers' negative emotions. In terms of downstream consequences, AI defeating people induces a spillover effect on consumers' unfavorable attitudes toward AI companies. Emphasizing the intention of helping people can effectively mitigate this negative spillover effect.

The authors' findings remind governments, policymakers and AI companies to pay attention to the negative effect of AI defeating people and take reasonable steps to alleviate this negative effect. The authors help consumers rationally understand this phenomenon and correctly control and reduce unnecessary negative emotions in the AI era.

This paper is the first study to examine the adverse effects of AI defeating humans. The authors contribute to research on the dark side of AI, the outcomes of competition matches and the method to analyze emotions in user-generated content (UGC).