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This paper aims to examine the influence of sustainability reporting on bank performance. Furthermore, this study investigates the impact of the country’s economic development, financial system and crisis in moderating sustainability reporting and bank performance relationship.

The sample consists of 400 listed banks from 19 countries over the 2009–2022 period. Panel fixed-effect regression is applied, and System Generalized Method of Moments is used as robustness to address endogeneity concerns. The results are robust and survive several sensitivity tests.

The results, aligning with legitimacy and agency theories, suggest a negative relationship between sustainability reporting and bank performance. Based on further classifications, results suggest the negative (positive) impact of country’s financial system (economic development) in moderating the sustainability reporting and bank performance nexus. Finally, this study documents the positive influence of sustainability reporting on bank performance during the crisis period. Overall, the findings fail to support the reduced information asymmetry accruing from higher sustainability disclosures in developing and bank-based economies.

This study has important implications for regulators, policymakers and other stakeholders, especially in light of recent banking scandals that have deteriorated stakeholders' faith in financial institutions' reporting quality.

This study extends the scant literature on sustainability reporting in banking from a cost-benefit vantage point. Furthermore, to the best of the author’s knowledge, no previous research has examined the moderating role of the country’s financial structure and crisis in sustainability reporting and bank performance relationship.

The paper aims to provide business process designers a formal yet user friendly technique to evaluate the implications of a process design on process performance even before its implementation.

Based on practical experience, the paper has built on past research to hypothesize structural metrics for business processes that help assess the influence of process design on organizational goals.

This paper suggests a list of structural metrics that can be used to approximate common performance goals (i.e. soft goals) at the stage of process design. Distinct views for process depiction are discussed to explain how each metric can be calculated and what kind of performance goals it can approximate.

The paper has assumed an intuitive relationship between process structure and process performance which has to be validated empirically. There is scope for developing formal methods to translate changes in structural metrics to monetary value for business and also to refine the structural metrics further if required.

The suggested list of structural metrics and the corresponding process views can be used to compare process design alternatives to select a process design better aligned to organization goals.

A list of structural metrics based on practical experience can be easily applied by business process designers to create a formal yet user friendly approach for process design evaluation.

This paper aims to examine the impact of announcements related to 77 interventions by 46 listed Indian pharmaceutical firms during COVID-19 on the abnormal returns of the firms. The study also finds the variables which explain cumulative abnormal returns (CARs).

This study uses standard event methodology to compute the abnormal returns of firms announcing pharmaceutical interventions in 2020 and 2021. Besides this, the multilayer perceptron technique is applied to identify the variables that influence the CARs of the sample firms.

The results show the presence of abnormal returns of 0.64% one day before the announcement, indicating information leakage. The multilayer perceptron approach identifies five variables that explain the CARs of the sample companies, which are licensing_age, licensing_size, size, commercialization_age and approval_age.

The study contributes to the efficient market literature by revealing how firm-specific nonfinancial disclosures affect stock prices, especially in times of crisis like pandemics. Prior research focused on determining the effect of COVID-19 variables on abnormal returns. This is the first research to use artificial neural networks to determine which firm-specific variables and pharmaceutical interventions can influence CARs.

The COVID-19 pandemic has transformed consumer buying behavior across the world. COVID-19 crisis brought a behavioral change in consumers' attitudes toward health, financial and social well-being. The current research work highlights the factors influencing consumer buying behavior during the COVID-19 pandemic considering saving and safety perspectives.

This study attempts to understand the gap in buying behavior with reference to saving and safety. Survey-based study was conducted during the second phase of COVID-19, and the respondents were those who lived in highly affected COVID cities in India. Exploratory factor analysis and multiple regression analysis were carried out for testing the hypotheses.

Seven factors became the prominent factors in consumer buying patterns during the pandemic. Consumers in the times of COVID-19 pandemic spend only on essential items as compared to nice-to-have and non-essential items.

Respondents considered in the research were millennials aged 25–40. The current research is limited to specific geographic location.

The study assessed how savings and safety influence consumer buying behavior. The 2S framework model for consumer buying behavior during pandemic has been developed. The findings of the study provides a road map to the companies, policy makers, managers and consumers in understanding the consumer buying behavior during pandemic.

The current research work observe the changes in the behavioral patterns of consumers in the context of 2S framework, i.e. saving and safety. This study offer novel contribution as there is no available literature that examined the saving and safety aspects together for consumer buying behavior during crisis.

The purpose of this paper is to examine the impact of perceived social media marketing activities (SMMAs) on customer loyalty via customer equity drivers (CEDs) in an e-commerce context.

The study surveyed 371 students from a large university in India. The data were analyzed via confirmatory factor analysis and the research hypotheses were examined using SEM.

The study revealed three key findings. First, perceived SMMAs of e-commerce comprise five dimensions, namely, interactivity, informativeness, word-of-mouth, personalization and trendiness. Second, perceived SMMAs of e-commerce have significantly and positively influenced all the drivers of customer equity (CEDs). Third, the CEDs of e-commerce exhibit a significant and positive influence on customer loyalty toward the e-commerce sites.

This study will help e-commerce managers to boost customer loyalty toward the e-commerce sites through perceived SMMA.

The study is the first to identify five dimensions of e-commerce perceived SMMA. The current study also introduces the stimulus–organism–response model as a theoretical support to connect perceived SMMAs of e-commerce to customers’ loyalty via CEDs. This is supposed to be the first study to examine the impact of perceived SMMA on customer loyalty toward the e-commerce sites via CEDs in the e-commerce industry.

The purpose of this paper is to estimate the multicomponent reliability by assuming the unit-Gompertz (UG) distribution. Both stress and strength are assumed to have an UG distribution with common scale parameter.

The reliability of a multicomponent stress–strength system is obtained by the maximum likelihood (MLE) and Bayesian method of estimation. Bayes estimates of system reliability are obtained by using Lindley’s approximation and Metropolis–Hastings (M–H) algorithm methods when all the parameters are unknown. The highest posterior density credible interval is obtained by using M–H algorithm method. Besides, uniformly minimum variance unbiased estimator and exact Bayes estimates of system reliability have been obtained when the common scale parameter is known and the results are compared for both small and large samples.

Based on the simulation results, the authors observe that Bayes method provides better estimation results as compared to MLE. Proposed asymptotic and HPD intervals show satisfactory coverage probabilities. However, average length of HPD intervals tends to remain shorter than the corresponding asymptotic interval. Overall the authors have observed that better estimates of the reliability may be achieved when the common scale parameter is known.

Most of the lifetime distributions used in reliability analysis, such as exponential, Lindley, gamma, lognormal, Weibull and Chen, only exhibit constant, monotonically increasing, decreasing and bathtub-shaped hazard rates. However, in many applications in reliability and survival analysis, the most realistic hazard rates are upside-down bathtub and bathtub-shaped, which are found in the unit-Gompertz distribution. Furthermore, when reliability is measured as percentage or ratio, it is important to have models defined on the unit interval in order to have plausible results. Therefore, the authors have studied the multicomponent stress–strength reliability under the unit-Gompertz distribution by comparing the MLEs, Bayes estimators and UMVUEs.

The purpose of this paper is to present a comprehensive analysis of the current status and development of the digital journalism field from 1987 to 2021 using the Dimensions database.

Using the Dimensions.ai database, 1734 articles were identified through search strategies which were published from 1987 to 2021. The downloaded results were analysed using specific parameters with the help of bibliometric and science mapping tools: Biblioshiny, VOSviewer and CiteSpace. The key contributions of the present comprehensive bibliometric study of the digital journalism field can be seen in terms of the following aspects: (1) Publication analysis from the perspectives of publication growth, key journals, contributing authors, institutions and countries done through Biblioshiny package. (2) Citation network analysis from the perspective of co-citation structure of papers, authors, countries and institutions done through VOSviewer. (3) Timeline analysis and keywords burst detection to identify hotspots and research trends in digital journalism with the help of CiteSpace.

The first paper with the keyword digital journalism was published in the year 1989. From 2011 onwards, there has been growth in digital journalism literature. The most popular journal in digital journalism studies is Digital Journalism, Journalism, Journalism Practice, Journalism Studies. Lewis, S.C. has contributed the most number of papers in digital journalism. Further, authors from the countries the USA, Spain, Brazil and UK have contributed immensely. The citation network of authors, institutions and countries contributing to digital journalism studies has also been explored in the study. Through burst analysis, hot topics in digital journalism were identified.

The paper provides a complete overview of the growth of digital journalism literature published from 1987 to 2021. The originality of this work lies in the triangulation of Biblioshiny, VOSviewer and CiteSpace software to present various aspects of bibliometric study. Findings of the study can help the researchers to identify areas as well as journals, authors, institutions working actively in the field of digital journalism.

The prevailing conceptualization of information system (IS) capabilities, rooted in the resource-based view (RBV) framework, tends to focus on unique firm resources. In the digital age, as emphasized by dynamic capabilities (DC), resource reconfiguration is critical in maintaining strategic advantage. This paper focuses on big data analytics capabilities (BDAC) from a DC perspective to present a novel conceptualization of BDAC–DC. We examine its effects on product, business model and business process innovation, including the effects of enterprise architecture (EA) on the BDAC business model innovation relationship.

This research presents a novel DC-based BDAC conceptualization, operationalized as a hierarchical construct. A survey-based approach is used for data collection and data analysis is done using partial least squares structural equation modeling (PLS-SEM).

The novel conceptualization and the effects of BDAC DC on BDA sensing-seizing and reconfiguration capacities support BDAC’s functional and evolutionary roleplay. Empirical results confirm the positive effects of BDAC–DC on first-order value targets (innovation) and the moderating effects of EA.

The novel BDAC–DC conceptualization has several implications for BDAC, DC, EA and business value research. Practicing managers must adopt a multifaceted approach to BDAC development by considering non-technical and organizational factors, collaborate with their business counterparts to explore unique big data ideas, initiate proof-of-concept projects to secure support and allocate resources synchronously, considering a multidimensional view of the process, product and business model innovation.

Practicing managers must adopt a multifaceted approach to BDAC development by considering non-technical and organizational factors, collaborate with their business counterparts to explore unique big data ideas, initiate proof-of-concept projects to secure support and allocate resources synchronously, considering a multidimensional view of the process, product and business model innovation for synergistic outcomes.

To the best of our knowledge, this research is the first attempt toward DC-based BDAC conceptualization, empirical validation of first-order effects on various forms of innovation and the often-overlooked role of critical EA capability.

Adopting and implementing robotic technology applications in the library is a significant technological up-gradation today. The purpose of this study was to evaluate selected literature focused mainly on robotics technology applications in the field of libraries and to assess the online social attention to research publications.

The study employed Scientometric and altmetric tools to evaluate the research publications. The bibliographic data of research publications were downloaded from Scopus database and scrutinized one by one and 71 articles were selected which mainly focused on robotic technology in libraries. Altmetric data were collected from the Dimensions.ai database. The analysis was performed using MS Excel, Tableau, Biblioshiny, VOSviewer and SPSS software.

Research on robotic technology in the field of libraries has been experiencing a gradual increase, marked by an annual growth rate of 12.93%. The United States has prominently led the way as the most active participant and collaborator in this advancement. Among the various journals, Library Hi Tech has notably stood out as a significant contributor to this field. However, the research articles have garnered limited social attention and impact. Furthermore, the patterns of authorship collaboration have demonstrated relatively modest levels within the field, and a weak correlation has been observed between the social attention received and the Scopus citation metrics of the publications.

The research needs to be disseminated more through various social media platforms to increase its visibility. Sharing research information through social media can bridge the gap between academia and society. The findings of this study can serve as a valuable reference for researchers and policymakers.

This study presents a Scientometric analysis of the selected published literature on robotics technology applications in the field of libraries, highlighting the progress and development of worldwide research in this area.

This empirical study aims to measure the performance of electronic service quality and related infrastructure in a state university library and suggests the strategies for further improvement based on this study.

The data collection was done through a survey questionnaire based on the 22 attributes of four e-Service quality dimensions rated on five-point Likert scale. The sample population consisted of 239 respondents comprising of different categories of users of university library.

The results of this study indicated that all four dimensions rated by the users fell between mean score 3 to 4 (good), the highest score was received in dimension online public access catalogue (OPAC) and internet service with followed by Library website, Library electronic equipment’s and e-User education. Out of total 22 e-Service quality attributes, none of the attributes received score above 4 (excellent), which indicates that still university library need to improve the current performance of e-Service quality and to take remedial steps to enhance the existing e-services and infrastructure.

Findings from this study could assist university library authority to sustain and enhance performance of e-Services that could achieve the core journey of the modern university libraries.

This study was the first endeavour for measurement of the performance of e-Service quality and related infrastructure of federal university libraries in India.