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This study proposes that the relationship between the employee's moral identity and unethical behavior might sometimes differ from linearly negative, given certain contextual variations, such as unethical pro-organizational behavior (UPB). The pro-organizational motivation of UPB may appeal to individuals' morality to perform unethical behavior for their organization. Herein, using the social identity perspective, the present study hypothesizes a curvilinear relationship between moral identity and UPB with the moderation of organizational identification.

Data collected in two field studies, from 316 and 185 executives in Study 1 and 2, was analyzed using hierarchical regression analysis.

The results affirmed an inverted U-shaped relationship between moral identity and UPB. Further, organizational identification moderated the relationship such that an increase in organizational identification augmented the UPB tendency for low moral identity individuals while inhibiting the UPB tendency for high moral identity individuals.

The current study uniquely demonstrates how moral identity might promote unethical behavior in pro-organizational contexts. Further, this research shows how high moral identity might mitigate the negative outcomes of high organizational identification.

The purpose of this paper is to explore the support for multi-party collaboration in project construction provided by building information modeling (BIM). Based on the perspective of value co-creation, the research results can provide support for the collaborative application and contract design of BIM platform.

In this paper, an evolutionary game model involving the owner, designer and constructor is constructed by using prospect theory and evolutionary game theory. Through simulation analysis, the evolution law of the strategy choice of each party in the collaborative application of BIM platform is discussed and the key factors affecting the strategy choice of all parties are analyzed.

The results show that there is an ideal local equilibrium point with progressive stability in the evolutionary game between the three parties: “the construction party shares information, the designer receives the information and optimizes the project and the owner does not provide incentives”; in addition, the opportunistic behaviors of the design and construction parties, as well as the probability of such behaviors being detected and the subsequent punishment have a significant impact on the evolutionary outcome.

This method can provide support for the collaborative application and contract design of BIM platform.

This paper examines the evolution of massive open online courses (MOOCs) literature over the past 15 years and identifies its significant developments.

Utilizing main path analysis (MPA) on a dataset of 1,613 articles from the Web of Science (WoS) databases, the authors construct the main pathway in MOOC literature through a citation analysis. Pajek software is used to visualize the 34 influential articles identified in the field.

Three phases emerge in MOOC research: connectivism as a learning theory, facilitating education reform and breaking barriers to MOOCs adoption. Multiple-Global MPA highlights sub-themes including self-regulated learning (SRL), motivation, engagement, dropouts, student performance and the impact of COVID-19.

First, data limitations from the WoS core collection might not cover all research, but using reputable sources enhances data validity. Second, despite careful algorithm selection to enhance accuracy, there remains a limitation inherent in the nature of citations. Such biased citations may result in findings that do not fully align with scholars' perspectives.

The authors' findings contribute to the understanding of MOOCs literature development, enabling educators and researchers to grasp key trends and focus areas in the field. It can inform the design and implementation of MOOCs for more effective educational outcomes.

This study presents novel methodologies and important findings for advancing research and practice in MOOCs.

This research investigates the impact of strategic research and development (R&D) (one led by a firm’s innovation orientation) on stock market performance during the economic disruption caused by the 2016 demonetization of high-value currency notes in India. It shows how firms’ strategic focus on innovation and integrated R&D initiatives can help mitigate shareholders’ losses and protect market value during negative macroeconomic shocks.

We analyzed financial and administrative data from firms listed in the Bombay Stock Exchange (BSE) 500 index and used the Fama French market model with appropriate instruments accounting for possible endogeneity to identify the impact. To ensure the reliability of our findings, we conducted robustness checks with alternate event windows, estimation methods, and variable measurements.

Strategic R&D plays a crucial role in building resilience against macroeconomic shocks. It effectively mitigated shareholders’ losses in the immediate aftermath of the shock, with an elasticity of abnormal returns of 7.65% on day zero, 13.1% during the first five days and 10.5% after the first fortnight. We also find that firms that are business-to-business (B2B), as well as those that are older and less leveraged, are better able to combat such a shock.

The study looked at one shock, namely demonetization. Future research is needed to demonstrate the generalizability of results during other macroeconomic shocks, like the COVID-19 pandemic. The study focuses on relatively near-term impacts, leaving the long-term value-creation effects of strategic R&D unexplored.

Innovation orientation acts as a structural enabler, allowing firms to make strategic R&D investments that mitigate losses during macroeconomic shocks. It explains that managers should avoid myopically managing R&D investments and align them with the firm’s innovation focus to enhance value creation.

While the currency demonetization was widely considered to be detrimental for firms as an unannounced negative monetary shock, our research shows that firms with high levels of strategic R&D were successfully able to counteract such a shock.

This is the first study to examine the short-term loss mitigation impact of firms’ focus on innovation and strategic R&D. It emphasizes the role of innovation-focused strategies during economic crises.

This study aims to examine living preference and tenure among millennials, with a particular focus on the impact of ethnic and cultural diversity on housing outcomes including observed homeownership inequalities.

Using the individual panel data from three waves in American Housing Survey, 2015–2019, this study compares the likelihood of co-residing among Asian and Hispanic millennials with non-Hispanic white millennial peers. Furthermore, this study estimates the effect of co-residence on homeownership across generational and ethnic backgrounds.

This study finds a preference for coresident adult familial households among foreign-born Asian and Hispanic millennials, and US-born Hispanic millennials when compared to their non-Hispanic white millennial peers. The results are robust after considering neighborhood selection bias, affordability and education. The effect of co-residence on ownership is significant and positive, suggesting this living arrangement contributes to homeownership across all generational and ethnic groups.

Housebuilders should be aware of Asian and Hispanic millennials’ increased appetite for extended family living arrangements and consider increasing the physical size of affordable or workforce-oriented rental housing and new single family construction to accommodate more adult co-living arrangements.

This study provides a more comprehensive understanding of the role ethnic and cultural diversity has on millennial adult living preferences and its generational differences, which is not just “boomeranging” as identified by previous literature, contributing to the growing interest in the housing research on the effect of ethnic diversity and culture on millennials’ homeownership rates.

This paper aims to explore the impact of the data observation period on the computation of altmetric measures like velocity index (VI) and half-life. Furthermore, it also attempts to determine whether article-level computations are better than computations on the whole of the data for computing such measures.

The complete publication records for the year 2016 indexed in Web of Science and their altmetric data (original tweets) obtained from PlumX are obtained and analysed. The creation date of articles is taken from Crossref. Two time-dependent variables, namely, half-life and VI are computed. The altmetric measures are computed for all articles at different observation points, and by using whole group as well as article-level averaging.

The results show that use of longer observation period significantly changes the values of different altmetric measures computed. Furthermore, use of article-level delineation is advocated for computing different measures for a more accurate representation of the true values for the article distribution.

The analytical results show that using different observation periods change the measured values of the time-related altmetric measures. It is suggested that longer observation period should be used for appropriate measurement of altmetric measures. Furthermore, the use of article-level delineation for computing the measures is advocated as a more accurate method to capture the true values of such measures.

The research work suggests that altmetric mentions accrue for a longer period than the commonly believed short life span and therefore the altmetric measurements should not be limited to observation of early accrued data only.

The present study indicates that use of altmetric measures for research evaluation or other purposes should be based on data for a longer observation period and article-level delineation may be preferred. It contradicts the common belief that tweet accumulation about scholarly articles decay quickly.

Several studies have shown that altmetric data correlate well with citations and hence early altmetric counts can be used to predict future citations. Inspired by these findings, majority of such monitoring and measuring exercises have focused mainly on capturing immediate altmetric event data for articles just after the publication of the paper. This paper demonstrates the impact of the observation period and article-level aggregation on such computations and suggests to use a longer observation period and article-level delineation. To the best of the authors’ knowledge, this is the first such study of its kind and presents novel findings.

The purpose of this study was to develop a suitable module for digital conservation of traditional knowledge of medicinal plants (MPs) used by tribal communities living in the Anuppur district of Madhya Pradesh, Central India.

The research used a qualitative approach to gather the data of MPs through the use of literature review and field survey. Based on the acquired data, a prototype digital learning system was constructed and assessed. This study used digital learning technologies to assess the requirements for transmitting traditional knowledge of important MPs used by tribal communities so that people can absorb and conserve them.

Over time, the focus on the digital conservation of traditional MP’s knowledge has progressively increased globally. Despite the rise in this field of study, information technology methods to preserve and distribute traditional knowledge of MPs have remained a few. When adopting digital learning to maintain traditional knowledge of MPs, it was discovered that it would be necessary to engage with relevant knowledge keepers, use multimedia, and provide content in local languages.

This study helps in conservation of important MP species that are having biologically important therapeutic compounds meant for treating various ailments. Older generations of various tribal communities mainly hold traditional knowledge of important MPs, and unless it is preserved, it will perish along with its caretakers.

It is worth looking at a digital platform that can help future generations to maintain traditional knowledge of MPs, as it is a dynamic and ever-changing, it must involve a digital tool for its future conservation. Current methods for maintaining traditional knowledge of MPs were ineffective and constrained by space and time.

The COVID-19 pandemic has had far-reaching effects on society and will continue to be a subject of study for researchers in the years to come. Businesses have implemented technologies that reduce reliance on physical currencies, such as e-commerce sites and contactless payments. This study aims to examine the users’ attitudes and behaviors toward mobile payments. The focus is on identifying the most effective techniques and approaches that businesses can use to encourage user adoption of mobile payments.

This study uses survey data from 396 active mobile payment users across the mid-west region of the USA to test the proposed hypothesis. The snowball sampling approach is used to sample the participants for the data collection. This study uses partial least squares structural equation modeling to test the ten hypotheses proposed in this study.

This study finds that organizational commitment and privacy customization can significantly overcome users’ protective attitudes toward mobile payments during the pandemic. In addition, providing users with privacy customization options can significantly encourage self-disclosure, which is crucial for transaction authentication and fraud detection.

Envisioned in the backdrop of the COVID pandemic, this is one of the earliest studies investigating the role of privacy customization, self-disclosure and organizational commitment on mobile payment adoption.

The aim of the paper is to rank and analyze the key strategies to sustainable finance adoption in the manufacturing sector using Fuzzy Delphi method (FDM), Interpretive Structural Modeling (ISM) and MICMAC (impact matrix cross-reference multiplication applied to a classification) analysis.

The study develops a novel framework to identify and analyze the mutual relationships among set of sustainable policies using extensive literature survey and experts opinion. Initially, the study found 14 strategies to implement sustainable finance with the help of vast literature. Then, the list of identified factors were screened through Fuzzy Delphi Method (FDM). Based on driving and dependence power, the final list of factors are divided into three categories.

The study findings reveal that “environmental rules and practices”, “financial incentives, tax reduction and subsidy”, have strongest driving power for promoting sustainable financial system in Pakistani manufacturing sector. Furthermore, “environmental awareness” and “long term vision” are found to be highly influenced by other corresponding elements in a system.

The ISM approach assists professionals, academics, and managers in identifying and ranking policies in implementing green business techniques. The hierarchical representation of ISM results provides a roadmap for decision-makers to navigate and prioritize factors effectively, facilitating the implementation of strategies that contribute to sustainable growth within organizations.

The study results provide interesting clues regarding green finance policies that provide the foundations, incentives, protections or other provisions that support the ecological conservancy’s mission. Specifically, the findings guide that government must offer research grants to private enterprises, research and development institutions, and universities to promote environmental protection and develop transformative technologies such as waste recycling, renewable energy, carbon capture, and power consumption.

The exploration of strategies for sustainable finance adoption with the help of mixed methodological approach and classification of these strategies on the basis of importance level is a new attempt in the field of manufacturing sector.

There is an ongoing challenge to map the efficacy of e-retailing strategies in building both value co-creation opportunities for online customers and customer value for companies. Based on the service-dominant (S-D) logic, an integrative model is provided that connects the impact of convenience and personalisation strategies (CPSs) on an e-retailer's performance – by offering co-creation opportunities and customer engagement.

The survey instrument is validated and the model is tested with data from active online customers using a novel methodology that blends artificial neural network (ANN) analysis with partial least squares (PLS) in both the measurement model and the path analysis.

The findings robustly support the model and yield evidence of the contribution of CPSs in effective value propositions, the interface between the S-D logic and customer engagement, and the direct effect of customer engagement on tangible forms of value for companies.

This study is the first scholarly effort to provide a comprehensive understanding of how and why CPSs can maximise customer value for the e-retailer, while simultaneously testing the customer value/engagement interface with a new blended ANN-PLS method.