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Blockchain is a developing technology that affects numerous industries, including facility management (FM). Many barriers are associated with adopting blockchain-enabled building information modeling (BEBIM) in FM. This research aims to identify and prioritize the barriers to adopting BEBIM in FM.

To address the knowledge gap, this study employs a two-phase methodology for evaluating the barriers to adopting BEBIM in FM. The first phase involves a comprehensive literature review identifying 14 barriers to BEBIM adoption. Using a Delphi approach, the identified barriers were categorized into 6 groups and finalized by 11 experts, adding 3 more barriers to the list. The best-worst method (BWM) determines the priority weights of identified barriers and sub-barriers in the second phase.

This study reveals that adopting BEBIM for FM in India faces significant hurdles. The most critical barriers are “limited collaboration” and “communication among stakeholders,” “legal constraints in certain jurisdictions” and “challenges in establishing trust and governance models.” To mitigate these barriers, stakeholders should foster collaboration and communication, develop efficient blockchain technology (BT) and establish a trust and governance model.

This work underscores the importance of formulating effective strategies to overcome the identified barriers and emphasizes implications that can assist policymakers and industry stakeholders in achieving successful BEBIM adoption for improved FM practice.

The study provides valuable insights for policymakers, construction industry stakeholders and facility managers interested in leveraging this technology to improve the efficiency and effectiveness of FM practice in India.

The purpose of this paper is to design novel tunnel field effect transistor (TFET) using graphene nanoribbons (GNRs).

To design the proposed TFET, the bilayer GNRs (BLGNRs) have been used as the channel material. The BLGNR-TFET is designed in QuantumATK, depending on 2-D Poisson’s equation and non-equilibrium Green’s function (NEGF) formalism.

The performance of the proposed BLGNR-TFET is investigated in terms of current and voltage (I-V) characteristics and transconductance. Moreover, the proposed device performance is compared with the monolayer GNR-TFET (MLGNR-TFET). From the simulation results, it is investigated that the BLGNR-TFET shows high current and gain over the MLGNR-TFET.

This paper presents a new technique to design GNR-based TFET for future low power very large-scale integration (VLSI) devices.

This study aims to assess the impact of participation in self-help group (SHG) on the psychological empowerment of rural tribal women.

Primary data was collected using multistage random sampling from the rural women of Jharkhand, India. The propensity score matching method was adopted using the psmatch2 command in STATA.

The results show a significant positive change in women’s self-esteem, self-confidence, self-efficacy, autonomy, knowledge and skills, reduction in agony and quality of life after participation in SHG. This reveals that participation in SHG has a significant positive impact on the psychological empowerment of rural tribal women.

Despite the numerous studies on rural women’s empowerment, there is little evidence of literature focusing on the impact of participation in SHG on psychological empowerment, specifically in the tribal context. This study primarily focuses on women belonging to the scheduled tribe category of Jharkhand, one of the poorest states of India.

This study aimed to examine the efficient market hypothesis (EMH) for the stock markets of 12 member countries of the Organization of Islamic Cooperation (OIC), such as Egypt, Indonesia, Jordan, Kuwait, Malaysia, Morocco, Pakistan, Saudi Arabia, Tunisia, Turkey and the United Arab Emirates (UAE), during the global financial crisis (GFC) and the COVID-19 (CV-19) epidemic. The objective was to classify the effects on individual indices.

The study employed the multifractal detrended fluctuation analysis (MF-DFA) on daily returns. After calculation and analysis, the data were then divided into two significant events: the GFC and the CV-19 pandemic. Additionally, the market deficiency measure (MDM) was utilized to assess and rank market efficiency.

The findings indicate that the average returns series exhibited persistent and non-persistent patterns during the GFC and the CV-19 pandemic, respectively. The study employed MF-DFA to analyze the sequence of normal returns. The results suggest that the average returns series displayed persistent and non-persistent patterns during the GFC and the CV-19 pandemic, respectively. Furthermore, all markets demonstrated efficiency during the two crisis periods, with Turkey and Tunisia exhibiting the highest and deepest levels of efficiency, respectively. The multifractal properties were influenced by long-range correlations and fat-tailed distributions, with the latter being the primary contributor. Moreover, the impact of the fat-tailed distribution on multifractality was found to be more pronounced for indices with lower market efficiency. In conclusion, this study categorizes indices with low market efficiency during both crisis periods, which subsequently affect the distribution of assets among shareholders in the stock markets of OIC member countries.

Multifractal patterns, especially the long memory property observed in stock markets, can assist investors in formulating profitable investment strategies. Additionally, this study will contribute to a better understanding of market trends during similar events should they occur in the future.

This research marks the initial effort to assess the impact of the GFC and the CV19 pandemic on the efficiency of stock markets in OIC countries. This undertaking is of paramount importance due to the potential destabilizing and harmful effects of these events on global financial markets and societal well-being. Furthermore, to the best of the authors’ knowledge, this study represents the first investigation utilizing the MFDFA method to analyze the primary stock markets of OIC countries, encompassing both the GFC and CV19 crises.

The tea plantation industry is characterized by the large-scale deployment of cheap women laborers and gender-blind practices that make the social positions of women workers vulnerable. This paper considers women temporary workers in tea gardens to study the exacerbated impact of Covid-19 on their lives. The impact of the pandemic on marginal tea garden women laborers has not received enough attention from researchers; hence, the authors have studied the gendered implications of the pandemic on Adivasi temporary women workers in tea gardens in India. “Adivasi” is an umbrella term to refer to all indigenous tribes in India.

The authors conducted a qualitative study with 26 in-depth interviews with women temporary workers who identify themselves as Adivasis. For the discussion, the authors have mainly borrowed from intersectionality and subalternity literature.

The analysis explored the intersectional experiences of the women temporary workers (1) as members of Tea Tribes who are compelled to continue working at tea gardens as wage laborers, (2) job insecurities at work due to their temporary worker status, (3) disadvantages faced by women workers for their gender identity and (4) the gendered impact of the pandemic on their lives.

This study has explored the gendered impact of the Covid-19 pandemic on the lives of temporary women workers who belong to ethnic minority groups in the global south. The exploitation of labor rights in the tea industry during the pandemic has not been discussed enough by researchers earlier.

Consumers have pervasively relied on mobile reviews in digital economy. However, little knowledge exists regarding how customers adopt several mobile reviews to make purchasing decisions. With the assistance of reader-response theory, this study investigates how the consistency of product reviews, in terms of their adherence to both other reviews and the prior experience of the customer, affect perceived quality, confirmation of the customer's expectations, the customer's level of trust in the seller and the consequent purchase intention.

Based on a scenario simulation and an online experiment to collect data, the authors employed AMOS to test the proposed hypotheses using survey data collected from 314 customers in Study 1 and 420 consumers in Study 2.

The results indicate that global consistency positively and significantly contributes to confirmation, perceived quality and trust in sellers while sequential inconsistency positively and significantly influences perceived quality. Meanwhile, purchase intention is positively and significantly promoted by confirmation, perceived quality and trust in sellers, and initial valence has some moderating effects on these relationships.

This study contributes to the understanding of how customers apply product reviews to make purchasing decisions from a new angle. It also elucidates the way in which the perceived consistency of product reviews affects how reviewers are perceived and the consequent effect of these perceptions on a customer's purchase intentions.

The stock market generates massive databases of various financial companies that are highly volatile and complex. To forecast daily stock values of these companies, investors frequently use technical analysis or fundamental analysis. Data mining techniques coupled with fundamental and technical analysis types have the potential to give satisfactory results for stock market prediction. In the current paper, an effort is made to investigate the accuracy of stock market predictions by using the combined approach of variables from technical and fundamental analysis for the creation of a data mining predictive model.

We chose 381 companies from the National Stock Exchange of India's CNX 500 index and conducted a two-stage data analysis. The first stage is identifying key fundamental variables and constructing a portfolio based on that study. Artificial neural network (ANN), support vector machines (SVM) and decision tree J48 were used to build the models. The second stage entails applying technical analysis to forecast price movements in the companies included in the portfolios. ANN and SVM techniques were used to create predictive models for all companies in the portfolios. We also estimated returns using trading decisions based on the model's output and then compared them to buy-and-hold returns and the return of the NIFTY 50 index, which served as a benchmark.

The results show that the returns of both the portfolios are higher than the benchmark buy-and-hold strategy return. It can be concluded that data mining techniques give better results, irrespective of the type of stock, and have the ability to make up for poor stocks. The comparison of returns of portfolios with the return of NIFTY as a benchmark also indicates that both the portfolios are generating higher returns as compared to the return generated by NIFTY.

As stock prices are influenced by both technical and fundamental indicators, the current paper explored the combined effect of technical analysis and fundamental analysis variables for Indian stock market prediction. Further, the results obtained by individual analysis have also been compared. The proposed method under study can also be utilized to determine whether to hold stocks for the long or short term using trend-based research.

The aim of this paper is to explore and analyse the challenges in effective implementation of blockchain by human resource management (HRM) functions. This paper also aims to assess the interplay between the barriers in causing the challenges during blockchain execution.

Ten barriers are discovered from the past studies. Based on the expert views on the identified barriers interpretive structural modelling (ISM) is administered to understand the interplay of these 10 challenges resulting in ineffective or non-implementation of HR blockchain.

The application of ISM has helped in categorizing the variables into strategic, operational and performance outcomes. Results of ISM indicate key barriers like lack of expertise, data privacy, technical infeasibility, complexity in implantation and lack of used cases.

The research is limited to 10 barriers. There can be other barriers that can also be studied. Second, the research is proposing a conceptual model that needs further validation.

This paper has significant implications for the theoretical and practical body of knowledge. So far, most studies are exploring and describing HRM from a digital perspective. Most HR studies are on artificial intelligence, the Internet of Things and smart HRM. Previous studies on blockchain for HRM are mostly describing the advantages of going for it.

Based on the findings, it can also be suggested that policy formulators must advance the technical regulatory framework. Blockchain technology can be effectively implemented only if the top management is committed to it because they can only frame the rules and right control framework, affirm the governance process and strategize improvement.

The study offers insights into the organization's decision makers for effectively implementing blockchain into their HR systems. Some specific recommendations based on the results are also made. The paper is an innovative attempt to analyse the barriers to HR blockchain.

Artificial intelligence (AI) is the most progressive commodity among current information system applications. In-house development and sales of beneficial products are difficult for many software development and service companies (SDSCs). SDSCs have some implicit concerns about implementing AI software development due to the complexity of AI technology; they require an evaluation framework to avoid development failure. To fill the void, this study identified the factors influencing SDSCs when developing AI software development.

Based on complex adaptive systems theory, three aspects were developed as the main factors of hierarchy, namely, employees' capabilities, environmental resources and team capabilities. Fuzzy analytic hierarchy process (FAHP) was used to assess the SDSCs' attitude. Based on SDSCs, attitudes toward implementing AI software projects were collected to calculate the hierarchy of factors.

The outcome of FAHP is used as understanding the key factors of SDSCs for selecting an AI software project, toward the improvement of overall project planning. Employees' stress resistance was considered as a priority for the project, although professional AI skills and resources were also important.

This study suggested three variables developed using complex adaptive systems. This study contributes to a better understanding of the critical aspects of developing AI software projects in SDSCs. The study's findings have practical and academic implications for SDSCs and subsequent academic development, broadening the scope of AI software development research.

This study examines how compliance with government and firm's own policy and reverse logistics practices relate with firm environmental performance.

This study draws on insights from stakeholder theory, and follows a two-phase research approach. The first phase utilized an extended literature review that seeks to provide a qualitative and comprehensive understanding of the research problem. The 2001–2023 data was collected from the Web of Science and Scopus databases, complemented with Google Scholar. The second phase involved an empirical study—adopting a quantitative cross-sectional survey design with a self-administered questionnaire to validate the theoretical conceptualizations deriving from the literature review. The empirical data were collected from 203 food and beverages manufacturing firms in Uganda and analysed using the partial least squares structural equation modelling (PLS-SEM) approach.

The study findings suggest that compliance with government policy positively influences firm environmental performance, both directly, and indirectly through fostering reverse logistics practices, and that the relationship between compliance with government policy and reverse logistics practices is contingent upon compliance with the focal firm's own policy.

The study findings will enhance the theoretical and conceptual development of the ideas that underpin stakeholder theory and applications. The Ugandan government will come up with better mechanisms for enforcing compliance with policy regulating the application of reverse logistics practices. In addition, the study advances the use of multi-method approaches in investigating interesting research aspects requiring in-depth examination. However, considering the fact that the empirical study was conducted in a single country context, and focused on firms more or less from the same sub-sector, the findings of the study might not be generalizable globally.

This study provides useful insights to logistics and supply chain managers involved in reverse logistics activities in food and beverages manufacturing firms. These managers can know how to leverage reverse logistics practices to enhance environmental performance of firms amidst environmental policies in the industry where they operate.

This study contributes to the built body of knowledge in operations, logistics and supply chain management literature; understanding about reverse logistics practices as a mechanism through which compliance with government policy influences environmental performance of firms. The interaction between compliance with government policy and compliance with firm policy is essential in explaining the performance effects of reverse logistics practices. In addition, the study advances the use of multi-method approaches in investigating interesting research aspects requiring in-depth examination. Complementing extended literature review with and empirical research to investigate reverse logistics practices influences on firm environmental performance, and incorporating the role of policy in explaining this relationship should make considerable contribution. Besides, the study highlights important areas for future research.