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This study investigates the influence of augmented reality (AR) on consumer behavior and decision-making in the context of retail. By developing a conceptual framework that highlights AR’s distinct characteristics and its contribution to the theme of human-centricity and resilience, this research explores the cognitive and affective responses triggered by AR experiences and their subsequent impact on consumer choices.

The study employs a comprehensive literature review approach, delving into the intricate relationships between AR, consumer behavior and decision-making. Through the exploration of factors such as interactivity, vividness, body image perception, narcissism, information processing preference, privacy concerns and situational surroundings, the study develops a nuanced understanding of the mechanisms underlying AR’s effects.

The study aligns with the contemporary focus on human-centeredness and resilience in the digital age. It reveals that interactive and vivid AR experiences significantly enhance consumer enjoyment and influence decision-making processes. Furthermore, the study uncovers how individual traits and contextual factors shape consumer responses to AR, shedding light on the broader landscape of consumer behavior.

While acknowledging limitations in generalizability and potential publication bias, this study offers a fresh perspective by accounting for moderating effects and situational contexts. Future research directions might involve longitudinal studies to capture evolving trends in the dynamic realm of AR in retail.

Businesses and marketers have the opportunity to utilize the knowledge acquired from this study to effectively integrate AR technologies into their customer engagement strategies, thereby promoting a customer-centric approach in their operations. The results emphasize the utmost importance of creating AR experiences that align with user preferences and provide meaningful interaction, ultimately leading to the development of sustainable consumer decisions in the digital age.

Distinct from existing literature, this study contributes to the theme of human-centricity and resilience by examining the influence of AR on consumer behavior. This contribution enriches discussions on sustainable consumer choices in the digital era. Employing a comprehensive literature review approach, we explore key factors including interactivity, vividness, body image perception, narcissism, information processing preference, privacy concerns and situational surroundings. This approach deepens our understanding of AR’s influence on consumer behavior, presenting a valuable addition to the field.

People with disabilities (PwDs) encounter several impediments to employment prospects, contributing to India's huge employment gap. Entrepreneurship offers a different avenue for PwDs to overcome these constraints. Entrepreneurship Support Organizations (ESOs) play a significant role in assisting PwD entrepreneurs. The present study aims to explore challenges encountered by entrepreneurs with disabilities (EwDs) in starting or sustaining a business in an Indian ecosystem. The study further aims to examine the status of entrepreneurial outcomes while comprehending the intersection of the challenges of disability and entrepreneurship. The study draws on stakeholder and social identity theories to understand this critical connection.

A qualitative approach is applied to study the phenomenon. Semi-structured interviews were conducted with 13 EWDs and 5 ESOs to explore the challenges faced and the available support in the Indian context. The study explores the level of coordination and roles these organizations play in fostering an entrepreneurial culture for PWDs.

The research findings explore novel challenges and facilitators of entrepreneurial pursuits of EwDs. The study further offers implications for policymakers to foster inclusive infrastructure, financial institutions to provide accessible credit options, and ESOs to amplify tailored assistance. Importantly, these dynamics have implications for advancing relevant Sustainable Development Goals.

The intersectional lens of disability and entrepreneurial challenges offered to study PwD entrepreneurship highlights new directions for exploring the phenomenon in deeper detail. Further, the theoretical underpinnings of stakeholder and social identity theories provide practical and indispensable insights into the phenomenon.

This study aims to provide insights into different aspects of the extant literature on the effects of dividend announcements. Along with other outputs of a bibliometric study, this study provides deeper insights into the concentration of the extant literature and suggest future research agendas.

This study uses the bibliometric, network and content analysis of the dividend announcement literature indexed in Scopus. This study presents the temporal analysis, the network of authors, countries, author citations and the co-occurrence of author keywords. This study provides the concentration of the extant literature in three clusters and unearth some key future research areas. This study uses the latent Dirichlet allocation method for robustness.

A total of 54 documents examining the US sample have received 1,804 citations. Interestingly, the first article on emerging markets was published in 2002, when at least 34 articles on developed markets had already been published from 1982 to 2001. The content analysis of top-cited literature unveils diverse insights into dividend announcements’ effects on financial markets. Contagion effects negatively impact non-announcing banks, particularly larger ones. Dividend maintenance affects stock market momentum, influencing loser returns. While current dividend/earnings news may not predict future company performance, information content dominates bond market reactions to post-dividend announcements. Concomitantly, while financially constrained firms exhibit short-term gains but worse long-term performance following dividend increases, larger stock dividends send stronger market signals in China.

This study significantly contributes to the bibliometric and content analysis literature by analyzing the sample documents based on the sample examined. To the best of the authors’ knowledge, no previous bibliometric study in this domain has been conducted to explore the markets (developed and emerging) to which the samples examined belong and the quality of publications from developed and emerging markets.

This work provides a generic review of the existing data mining ontologies (DMOs) and also provides a base platform for ontology developers and researchers for gauging the ontologies for satisfactory coverage and usage.

The study uses a systematic literature review approach to identify 35 DMOs in the domain between the years 2003 and 2021. Various parameters, like purpose, design methodology, operations used, language representation, etc. are available in the literature to review ontologies. Accompanying the existing parameters, a few parameters, like semantic reasoner used, knowledge representation formalism was added and a list of 20 parameters was prepared. It was then segregated into two groups as generic parameters and core parameters to review DMOs.

It was observed that among the 35 papers under the study, 26 papers were published between the years 2006 and 2016. Larisa Soldatova, Saso Dzeroski and Pance Panov were the most productive authors of these DMO-related publications. The ontological review indicated that most of the DMOs were domain and task ontologies. Majority of ontologies were formal, modular and represented using web ontology language (OWL). The data revealed that Ontology development 101, METHONTOLOGY was the preferred design methodology, and application-based approaches were preferred for evaluation. It was also observed that around eight ontologies were accessible, and among them, three were available in ontology libraries as well. The most reused ontologies were OntoDM, BFO, OBO-RO, OBI, IAO, OntoDT, SWO and DMOP. The most preferred ontology editor was Protégé, whereas the most used semantic reasoner was Pellet. Even ontology metrics for 16 DMOs were also available.

This paper carries out a basic level review of DMOs employing a parametric approach, which makes this study the first of a kind for the review of DMOs.

The purpose of this paper is to investigate the effect of high-k material HfO₂ as a buffer layer for the fabrication of metal-ferroelectric-insulator-silicon (MFeIS) structures on Si (100) substrate.

RF-sputtered Pb[Zr0.35Ti0.65]O₃ or (PZT) and plasma-enhanced atomic layer deposited HfO₂ films were selected as the ferroelectric and high-k buffer layer, respectively, for the fabrication of metal-ferroelectric-insulator-silicon (MFeIS) structures on Si (100) substrate. Multiple angle ellipsometry and X-ray diffraction analysis was carried out to obtain the crystal orientation, refractive index and absorption coefficient parameters of the deposited/annealed films. In the different range of annealing temperature, the refractive index was observed in the range of 2.9 to 2 and 1.86 to 2.64 for the PZT and HfO₂ films, respectively

Electrical and ferroelectric properties of the dielectric and ferroelectric films and their stacks were obtained by fabricating the metal/ferroelectric/silicon (MFeS), metal/ferroelectric/metal, metal/insulator/silicon and MFeIS capacitor structures. A closed hysteresis loop with remnant polarization of 4.6 µC/cm² and coercive voltage of 2.1 V was observed in the PZT film annealed at 5000 C. Introduction of HfO₂ buffer layer (10 nm) improves the memory window from 5.12 V in MFeS to 6.4 V in MFeIS structure with one order reduction in the leakage current density. The same MFeS device was found having excellent fatigue resistance property for greater than 1010 read/write cycles and data retention time more than 3 h.

The MFeIS structure has been fabricated with constant PZT thickness and varied buffer layer (HfO₂) thickness. Electrical characteristics shows the improved leakage current and memory window in the MFeIS structures as compared to the MFeS structures. Optimized MFeIS structure with 10-nm buffer layer shows the excellent ferroelectric properties with endurance greater than E10 read/write cycles and data retention time higher than 3 h. The above properties indicate the MFe(100 nm)I(10 nm)S gate stack as a potential candidate for the FeFET-based nonvolatile memory applications.

Option pricing theory enables computation of the price of an option using different variables associated with the underlying security and options contract. The purpose of this study is to assess research trends that emerged in the field of option pricing. This study reviews existing literature of the option pricing domain, both qualitatively and quantitatively, and identifies potential themes for future research.

This study adopts bibliometric analysis method to explore literature published in the option pricing domain. As part of bibliometric analysis, this study considers both descriptive and network analysis to assess publication trends. For descriptive analysis, the “bibliometrix” package proposed by Aria and Cuccurullo (2017) is used and for network analysis, VOS viewer (Van Eck and Waltman, 2017) and Gephi (Bastian et al., 2009) are used.

This study identifies research trends, top researchers, articles, journals and contributions from institutions and countries in the option pricing domain. It identifies four clusters that show different directions and also focuses on past studies on the same subject. It explores research gaps by performing an in-depth analysis of existing literature on option pricing and suggests the way forward for research in this area.

To the best of the authors’ knowledge, no previous studies have attempted to analyze the literature published in the option pricing domain. This study fulfils this research gap by conducting a comprehensive analysis of studies in the option pricing area. This study identifies quality research work published in the domain, research trends, contribution by most relevant researchers, contributions across geographies and institutions and the connections among these aspects. This study also identifies important themes and provides directions for future research.

Contemporary firms are increasingly focusing on enhancing the customer experience (CX) to gain a sustainable competitive advantage. However, despite the rapid growth of CX research from both the customer's and the firm's perspectives, the intellectual structure of CX research remains tenuous, thus requiring further investigation. Addressing this gap, the authors review and map the existing corpus of CX literature, from which important implications are drawn.

After inventorizing the CX literature (1997–2021), the authors apply bibliometric (e.g. co-citation) analysis to identify key CX articles and pertinent CX-publishing journals, followed by the identification of key CX research themes through network analysis.

The authors first document chief CX-publishing journals and articles and identify their respective contributions. The authors, then, derive five core CX themes, including CX through the customer journey, S-D Logic-informed CX, interactive service–based CX, CX in the servicescape and CX and consumption. The authors conclude by developing an agenda for future CX research based on the study findings.

By offering a pioneering analysis of the corpus of CX research (1997–2021), these analyses offer a pertinent theoretical contribution.

Natural calamities like earthquakes, floods and epidemics/pandemics like COVID-19 significantly disrupt almost all the supply networks, ranging from medicines to numerous daily/emergency use items. Supply Chain Resilience is one such option to overcome the impact of the disruption, which is achieved by developing supply chain factors with Artificial Intelligence (AI) and Big Data Analytics (BDA).

This research examines how organizations using AI and BDA can bring resilience to supply chains. To achieve the objective, the authors developed the methodology to gather useful information from the literature studied and developed the Total Interpretive Structural Modeling (TISM) by consulting 44 supply chain professionals. The authors developed a quantitative questionnaire to collect 229 responses and further test the model. With the analysis, a conceptual and comprehensive framework is developed.

A major finding, this research advocates that supply chain resilience is contingent upon utilizing supply chain analytics. An empirical study provides further evidence that the utilization of supply chain analytics has a positive and favorable effect on the flexibility of demand forecasting to inventory management, resulting in increased efficiency.

Few studies demonstrate the impact of advanced technology in building resilient supply chains by enhancing their factors. To the best of the authors' knowledge, no earlier researcher has attempted to infuse AI and BDA into supply chain factors to make them resilient.

The purpose of this paper is to develop a nuanced framework for evaluating a channel partner’s performance in distribution channel relationships. Given a channel partner’s task environment characteristics (high/low munificence, dynamism and complexity), the study examines which performance metrics (output, activity or capability) are most relevant for evaluating its performance levels effectively.

The study adopts self-administered cross-sectional survey-based research design. Matched data were collected from 252 channel partners – manager relationship dyads. The latent change score (LCS) model within SEM framework provides mean paired-differences of the relevance ratings for each metrics. This was used to assess the empirical validity of the hypothesized relationships.

The study demonstrates the importance of calibrating performance evaluation metrics to a channel partner’s task environment state, made possible by its holistic approach to performance evaluation. Based on an extensive analysis, it shows that no single metric is relevant within all environmental states; rather, it could be dysfunctional, a result that differs from vast majority of the literature.

Investigates individual linkages between task environment dimensions and performance metrics to provide a fuller understanding of these relationships. Also provides a theoretical framework to support further research on the topic.

The study provides managerial guidelines (and extensive graphical analysis) for nuanced and dynamic evaluation of channel partners’ performance that can enable firms to identify and promote their most valuable channel partners and prevent the deterioration of others.

First one to develop and empirically validate a nuanced framework for evaluating performance of exchange partners that operate under diverse task environment states.