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The case “Evaluating Business Value of IT Requirements” addresses a software/information technology (IT) project management concern of assessing value of specified project requirements upfront.

This newly designed case is suitable for the students of an undergraduate programme, an MBA programme and practitioners. Assignment questions are designed from the perspective of teaching this case to a business student audience. The case is ideally suited for the IT strategy course where approaches to identifying business value of software investments are discussed.

Set in November 2011, the case begins with the dilemmas facing Mr Suneel as he tries to come up with an approach that could facilitate evaluation of business value of software at a relatively early stage of its development. Based on a review of available literary sources, he proposed a six-step approach that incorporates responses of both the project representatives and business representatives. The final result is a quantitative representation of business value that is expected to facilitate both the business and the project organisation to finalise on software features that contribute significantly in realising the intended business objectives.

To introduce the concept of business value and its associated dimensions; to introduce the concept of project scenarios and its constituents; to highlight the benefits of an early assessment of the business value; and to demonstrate how one can link software requirements to business value and the difficulties associated with the estimation process.

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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.

The ratio between share price and current earnings per share, the Price Earning (PE) ratio, is widely considered to be an effective gauge of under/overvaluation of a corporation’s stock. Arguably, a more reliable indicator, the Cyclically Adjusted Price Earning ratio or CAPE, can be obtained by replacing current earnings with a measure of permanent earnings i.e. the profits that a corporation is able to earn, on average, over the medium to long run. In this study, we aim to understand the cross-sectional aspects of the dynamics of the valuation metrics across global stock markets including both developed and emerging markets. We use a time varying DCC model to exploit the dynamics in correlations, by introducing the notion of value spread between CAPE and the respective Market Index from 2002 to 2014 for 34 countries. Value spread is statistically significant during the 2008 crisis for asset allocation. The signal can be utilized for better asset allocation as it allows one to interpret the common movements in the stock market for under/overvaluation trends. These estimates clearly indicate periods of misvaluation in our sample. Furthermore, our simulations suggest that the model can provide early warning signs for asset mispricing in real time on a global scale and formation of asset bubbles.

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.

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.