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This study identifies and ranks the barriers to manufacturing integrated circuit (IC) chips in the Indian semiconductor manufacturing industry by examining the causal relationships between the different barriers using an advanced causal modeling technique named the grey influence analysis (GINA). Moreover, IC plays a crucial function in reducing the size, boosting the speed and improving the dependability of electronic devices. The widespread use of these technologies has led to advancements in sectors such as communications, healthcare, transportation and entertainment.

This study investigates 12 factors related to the barriers to manufacturing IC chips. In addition, experts from an Indian semiconductor manufacturing organization have validated the presence of these barriers. In addition, the research employs a novel GINA to investigate and comprehend the influence relationships between these barriers.

To successfully manufacture IC chips in India, the “Government Policies and Regulations (B11),” “Lack of Semiconductor Fabrication Facilities (B1)” and “Dependence on Imported Raw Materials and Components (B2),” were the critical barriers that must be addressed and overcome, as per the GINA result.

The author highlighted that a lack of fabrication plants could lead to limited production of the semiconductor chips and that a lack of local fabs can hinder the development of advanced technologies. The complex policy can significantly affect the set-up of new semiconductor fabs, hamper the overall growth of the semiconductor market and divert away foreign investors. This further creates restrictions on employment creation and undermines the economy.

Although semiconductor applications have several benefits, more research is needed to identify the barriers to manufacturing IC chips in India, making this study unique. In addition, the advanced GINA method is also used to examine the critical barriers and their interconnection based on an expert response-based study from an Indian semiconductor organization.

Due to the extreme competition in the e-commerce landscape, online companies resort to driving extra profits by manipulating user decisions via manipulative user interfaces and tricks, known as dark patterns. Although many online users are aware of such manipulative tactics, they struggle to combat dark patterns due to certain challenges. Therefore, this study identifies and ranks the most and least significant user challenges in resisting dark patterns in e-commerce.

Initially, the authors identified 11 user challenges against dark patterns in the existing literature. After collecting expert opinions about the challenges from 17 industry professionals, the authors used grey influence analysis (GINA) to identify the most significant challenges.

The study reveals that lack of user awareness, partial control over cognitive biases and user preference for short-term benefits are the most influential challenges. Conversely, the normalization of aggressive marketing, lack of collective user action and legal challenges were found to have minimal influence.

The study’s findings apply to the generic e-commerce industry and may differ in specialized sectors.

The study’s findings could encourage specialized consumer education regarding dark patterns to assist users in minimizing cognitive biases. Based on the insights, e-commerce companies might differentiate themselves by embracing ethical design methods and supporting consumer trust and transparency above manipulative techniques.

The study pioneered the use of GINA to model user challenges in combating dark patterns, providing fresh insights into how these challenges interact and influence user behavior. It is one of the first to show the cascading effects of user challenges in a manipulative e-commerce environment.

The study aims to identify the central paradoxical tensions existing in developing resilience in organisations. The main thrust of this study is to develop a thorough understanding of diverse conflicting tensions in building resilience and develop the possible strategies to surmount these tensions.

Using the case study approach, the study applied theory-elaboration strategy as this study is based on well-established literature from both digitalisation and resilience. The study uses the paradox theory lens in a case study to reconcile both theories with contextual idiosyncrasies.

The paradox theory lens provides perspectives to understand tensions during resilience development and the role of digital transformation in this process. It assesses the potential solutions for surmounting tensions in resilient operations. The mapping of workable solutions with different paradoxes and propositions has been proposed for future empirical research.

The study suggests that practitioners should not consider resilience and sustainability as mutually exclusive; instead, managers must embrace ongoing tensions to bring solutions to address these two essential organisational priorities.

To the best of the authors' knowledge, this is the first empirical study that applies paradox theory to understand how an organisation can build resilience while confronting several paradoxes. The study findings support that resilience practices can move in tandem with environmental sustainability goals rather than being usually mutually exclusive.

The evolving landscape of digital transformation, businesses are increasingly recognising the intrinsic link between technological innovation, sustainability and the critical role of photovoltaic (PV) cells in smart cities. This nexus represents a compelling proposition, not only for addressing the imperatives of business sustainability but also for achieving United Nations Sustainable Development Goal 11 – the aspiration for sustainable and resilient urban communities. The use of PV cells within smart city infrastructure serves as an exemplar of how digital transformation can be harnessed to drive sustainability and innovation concurrently. With harvesting solar energy through PV cells, smart cities can reduce their carbon footprint, enhance energy efficiency and offer cleaner, more sustainable living environments for their inhabitants. This chapter investigates how the integration of PV cells in smart city infrastructure not only aligns with SDG11 but also serves as a potent catalyst for turbocharging digital transformation endeavours, fostering business sustainability and fuelling innovation.

This study aims to identify the enablers of supply chain resilience (SCR) through a literature review and expert panel input in the context of Pakistan and the post-pandemic era. This study also aims to categorize and rank the identified enablers using expert panel input.

A review of the extant literature was conducted to investigate and identify the factors that contribute to SCR. The relative ranking of the enablers was carried out by a group of industry and academic experts. The expert panel was convened to compare the main categories and each enabler in pairs and to score the enablers using triangular fuzzy numbers.

This study identified 16 critical SCR enablers. Using the fuzzy analytic hierarchy process (AHP), these enablers were divided into three groups and analyzed. The results show that financial enablers, technology enablers and then social enablers are prioritized when it comes to SCR in emerging markets. The robustness of the ranking of enablers is tested through sensitivity analysis.

The results shall be helpful for policymakers and managers to understand the important enablers and also help allocate resources to important enablers. Managers will be able to formulate strategies to achieve SCR in an uncertain environment.

This is one of the first attempts to identify and rank the enablers of SCR in an emerging economy context.

This paper aims to explore the antecedents of service robots’ adoption at restaurants in India, specifically focusing on Gen Z customers. This study sheds light on customer preferences in getting served by service robots with the moderating role of human service preference and the perceived job lose orientation of dine-in customers.

Primary data was collected from Gen Z customers using purposive sampling from prime cities in South (n = 363) and North India (n = 303). The perceptions of respondents towards service robots were analysed in SmartPLS 3.3.9.

The findings suggest that performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivations and price value significantly determine service robots’ acceptance by Gen Z consumers. However, such direct associations are significantly moderated by Gen Z’s human service preference and perceived job loss. Furthermore, demographic factors like respondents’ location, i.e. north and south, affect their preferences.

This research provides more insights into human preferences in dining in terms of moderating variables like human service preference and job-saving orientation of customers based on which the managers can design people, processes and physical evidence strategies of restaurant service mix.

The growing prevalence of digitalisation in economies has brought attention to the significance of digital transformation and its potential to enhance the competitiveness of enterprises within the emerging market. Nevertheless, it is important to note that disruptive changes are not limited to the organisational level, as they also have broader implications for the environment, society and institutions. The incorporation of technology into the field of education, often known as educational technology (EdTech), has undergone a significant evolution in recent times, fundamentally transforming the methods and processes of teaching and learning. This chapter delves into the multifaceted landscape of digital transformation in the field of EdTech from the perspective of sustainable development, elucidating the wide range of opportunities and challenges that consumer, educators, institutions and technology providers and various stakeholders face when they embark on this journey. Further, this chapter also sheds light on how to overcome the challenges faced by the stakeholders in digital transformation of EdTech for quality education.

Agile new product development (ANPD) attracts researchers and practitioners by its ability to rapidly reconfigure products and related processes to meet the needs of emerging markets. To increase ANPD adoption, this study aims to identify ANPD enablers (ANPDEs) and create a structural framework that practitioners can use as a quick reference.

Initially, a comprehensive literature review is conducted to identify ANPDEs, and a structural framework is developed in consultation with an expert panel using a hybrid robust best–worst method interpretive structural modeling (ISM). During the ISM process, the interactions between the ANPDEs are investigated. The ISM result is used as input for fuzzy Matrice d’Impacts croises-multiplication appliqúean classment means cross-impact matrix multiplication applied to classification (MICMAC) analysis to investigate enablers that are both strong drivers and highly dependent.

The study’s findings show that four ANPDEs are in the low-intensity cluster and thus are excluded during the structural frame development. ISM output shows that “Strong commitment to NPD/top management support,” “Availability of resources,” “Supplier commitment/capability” and “Systematic project planning” are the important ANPDEs. Based on their driving and dependence power, the clusters formed during the fuzzy MICMAC approach show that 16 ANPDEs appear in the dependent zone, one ANPDE in the linkage zone and 14 ANPDEs in the driving zone.

This research has intense functional consequences for researchers and practitioners within the industry. Industry professionals require a conservative focus on the established ANPDEs during ANPD adoption. Management has to carefully prepare a course of action to avoid any flop during ANPD adoption.

The framework established is a one-of-a-kind study that provides an integrated impression of important ANPDEs. The authors hope that the suggested structural framework will serve as a blueprint for scholars working in the ANPD domain and will aid in its adoption.

We investigate how the COVID-19 pandemic affected the efficiency of exchange rate, stock and cryptocurrency returns by analyzing a well-known calendar anomaly, the turn-of-the-month (TOM) effect.

We define the TOM days as the final trading day of a month and the initial three trading days of the immediate next month. To understand the TOM effect, we estimate the typical ordinary least squares regression model using the Heteroskedasticity and Autocorrelation Consistent (HAC) standard errors and covariances.

Our findings indicate that the returns during TOM days are significantly higher relative to those of non-TOM days. Nonetheless, our findings further reveal that the COVID-19 pandemic intensifies the TOM effect for the equity markets but weakens it for the exchange rate and Ethereum markets. We then develop a trading strategy that is found to beat the typical buy-and-hold (BH) approach.

Based on these results, we create a trading strategy which is found to surpass the BH strategy. Our results provide useful implications for investors and policymakers, as the considered markets can be timed by taking positions, especially based on the behavior of the TOM effect.

First, this paper is the inaugural study to examine the TOM effect across equity, currency and cryptocurrency markets. Previous studies have not addressed the TOM effect in Ethereum markets. Second, our paper conducts a battery of validity tests to ensure that the studied anomaly is not confounded by erstwhile anomalies. Third, our paper explores the performance of studied anomalies both prior to and throughout the COVID-19 pandemic to gain a deeper understanding of market efficiency. Finally, we validate our findings using the Kruskal–Wallis test, free from the assumptions of normal distribution.

The study explores the potential barriers to data science (DS) implementation in organizations and identifies the key barriers. The identified barriers were explored for their interconnectedness and characteristics. This study aims to help organizations formulate apt DS strategies by providing a close-to-reality DS implementation framework of barriers, in conjunction with extant literature and practitioners' viewpoints.

The authors synthesized 100 distinct barriers through systematic literature review (SLR) under the individual, organizational and governmental taxonomies. In discussions with 48 industry experts through semi-structured interviews, 14 key barriers were identified. The selected barriers were explored for their pair-wise relationships using interpretive structural modeling (ISM) and fuzzy Matriced’ Impacts Croise's Multiplication Appliquée a UN Classement (MICMAC) analyses in formulating the hierarchical framework.

The lack of awareness and data-related challenges are identified as the most prominent barriers, followed by non-alignment with organizational strategy, lack of competency with vendors and premature governmental arrangements, and classified as independent variables. The non-commitment of top-management team (TMT), significant investment costs, lack of swiftness in change management and a low tolerance for complexity and initial failures are recognized as the linkage variables. Employee reluctance, mid-level managerial resistance, a dearth of adequate skills and knowledge and working in silos depend on the rest of the identified barriers. The perceived threat to society is classified as the autonomous variable.

The study augments theoretical understanding from the literature with the practical viewpoints of industry experts in enhancing the knowledge of the DS ecosystem. The research offers organizations a generic framework to combat hindrances to DS initiatives strategically.