Search results

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.

Work–family conflict is one of the most challenging stressors for construction industry professionals to cope with emotional problems. This study aims to propose a model linking work–family conflict and high-quality relationships among project team members and explore mediating effects of life and job satisfactions.

A theoretical framework was established based on the spillover theory and social exchange theory. Data were obtained by a questionnaire survey conducted with 328 respondents working in construction projects. Structural equation modeling (SEM) was used to test interrelationships among work–family conflict, job satisfaction, life satisfaction and high-quality relationship.

The results indicate that work–family conflict is negatively associated with both job and life satisfactions of construction professionals. In addition, there were direct and positive relationships between satisfaction domains and high-quality relationship capacity. The findings further support that job satisfaction plays a mediating role between work–family conflict and high-quality relationship among construction professionals, while life satisfaction does not mediate the relationship.

The effects of interrole conflicts on the context of satisfaction have been investigated previously; however, there is a lack of knowledge regarding its influence on high-quality relationship among project team members. This study extends the body of knowledge on high-quality relationships among project team members to understand how conflict and satisfaction factors influence interpersonal relationships in construction project management.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

This paper aims to measure the severity, frequency and importance of the factors causing delays in the United Arab Emirates (UAE) construction industry following the Covid-19 pandemic onset. The study also measures the likelihood of the effects caused by these delays.

A mixed approach of both qualitative and quantitative. Literature review was conducted to extract 40 factors of delays and 10 effects of delays. A survey was then administered to construction professionals in the UAE to collect the perceptions on the severity and frequency of factors of the causes of delays using a Likert Scale of 1–5 where 1 represented very low and 5 represented very high. Similarly, the respondents were also asked to rate the likelihood of the occurrence of the effects of the delays based on a Likert scale of 1–5 as well. Furthermore, Spearman’s rank correlation was also conducted to compute the level of agreement between the different parties; owner, consultants and contractors.

The results revealed that the top five factors of delays include: award the project for the lowest bidder, delay in progress payment, change orders by the owner, poor subcontractor performance and inadequate planning and scheduling by the contractor. The findings of this study emphasize the financial challenges and economic crisis brought upon the construction industry due to the pandemic. Furthermore, the pandemic also shifted the perceptions of construction professionals, who are now more aware of the delays caused by awarding the project to the lowest bidder who would not have the required qualifications to conduct efficient planning and scheduling that are relevant in the case of extraordinary events such as Covid-19. Moreover, a high level of agreement between the consultants and contractors was observed, with a Spearman’s rank correlation of 0.804. Additionally, the most likely effects of delays concluded from this study were time overrun/extension and poor quality of work.

Literature review is very rich in the field of construction projects delays. However, there is very limited research on the impact of Covid-19 in the context of construction projects delays, and insights from construction professionals regarding this matter are particularly lacking in literature. Therefore, this paper bridges the gap in literature by providing perceptions of construction professionals on the impact of Covid-19 on the factors causing delays in the UAE construction industry. The findings of this research are expected to be an invaluable resource for future to help the construction industry heal faster when encountering similar epidemics or extraordinary events.

The purpose of this study is to look at the factors that drive people to participate in the sharing economy (SE). Based on the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), the study proposes a mathematical model. The study’s ultimate objective is to help businesses attract more involved customers and promote collaborative consumption as a sustainable alternative to typical consumption patterns. The study offers a conceptual framework established via a thorough literature review to examine Indian customers’ use behavior toward SE platforms. A one-sample two-tailed t-test is used to assess the framework’s efficacy. The research fills gap in the literature on the SE by investigating the factors that determine subjective norms (SN), attitudes (A), and perceived behavioral control (PBC). A framework is provided that takes behavioral intention (BI) contemplated as a mediating variable. The research improves TAM and TPB by including new factors such as technical characteristics. This research adds to the body of knowledge on the digital SE by underlining the relevance of usage behavior in comprehending Indian customers, where A, SN, and PBC are important aspects. The research presents a paradigm for better understanding customers’ attitudes and behaviors toward various SE platforms, which might help academics, practitioners, and policy makers situate their initiatives within the larger field of sharing. The study’s categorizations of Indian consumers’ A, SN, PBC, and BI toward the SE might potentially advise on future research and government policies.

This research sought to examine the differential effect of chatbot banking artificial intelligence (AI) on consumer experience in the banking industry. A positivist paradigm was adopted to sample 389 consumers who were previously exposed to chatbot banking in Zimbabwe. A causal research design was employed whilst a quantitative approach was followed. In analysing data, the research study applied the structural equation modelling (SEM) technique. The authors found that chatbot banking significantly improves customer experience (CX) in the banking industry. Reliability and responsiveness of the chatbot need to be enhanced for effective improvements in CX. A need was also identified to enhance CX through the development of an ease-to-use chatbot which is embedded in everyday messaging applications of consumers. A significant association was also found between perceived benefits of chatbot banking and CX. This study informs the development of competitive advantage by banks and other related companies through AI-based CX management strategies. In times of pandemics and beyond, chatbot banking can be very instrumental in improving CX.

The coronavirus disease 2019 (COVID-19) pandemic has had a big impact on organisations globally, leaving organisations with no choice but to adapt to the new reality of remote work to ensure business continuity. Such an unexpected reality created the conditions for testing new applications of smart home technology whilst working from home. Given the potential implications of such applications to improve the working environment, and a lack of research on that front, this paper pursued two objectives. First, the paper explored the impact of smart home applications by examining the factors that could contribute to perceived productivity and well-being whilst working from home. Second, the study investigated the role of productivity and well-being in motivating the intention of remote workers to use smart home technologies in a home-work environment in the future.

The study adopted a cross-sectional research design. For data collection, 528 smart home users working from home during the pandemic were recruited. Collected data were analysed using a structural equation modelling approach.

The results of the research confirmed that perceived productivity is dependent on service relevance, perceived usefulness, innovativeness, hedonic beliefs and control over environmental conditions. Perceived well-being correlates with task-technology fit, service relevance, perceived usefulness, perceived ease of use, attitude to smart homes, innovativeness, hedonic beliefs and control over environmental conditions. Intention to work from a smart home-office in the future is dependent on perceived well-being.

The findings of the research contribute to the organisational and smart home literature, by providing missing evidence about the implications of the application of smart home technologies for employees' perceived productivity and well-being. The paper considers the conditions that facilitate better outcomes during remote work and could potentially be used to improve the work environment in offices after the pandemic. Also, the findings inform smart home developers about the features of technology which could improve the developers' application in contexts beyond home settings.

Purpose: The study aims to extract insights into five significant industries, pharmaceutical, space, defence, renewal energy, and information technology (IT), which have huge potential to make India achieving a five trillion-dollar economy in the future.

Design/methodology/approach: The authors focus on future-driven industries which are not only making India a third highest gross domestic product (GDP) producer country but also reviewing the different aspects of these industries and how they can assist India in achieving a five trillion-dollar economies along with determining India’s self-reliance through different governments initiatives in this direction.

Findings: The findings highlight the importance of inclusiveness of policymakers, stakeholders, private players, foreign investors, and the masses. Their significant contributions especially in the pharmaceutical, space, defence, renewal energy, and IT sectors in terms of creativities, innovations, intellect, executions, implementations, and improvements can assist India in achieving its five trillion-dollars economy soon.

Practical implications: This study offers (1) convincing insights into five key industries, pharmaceutical, space, defence, renewal energy, and IT, which have huge potential to increase total GDP volume shortly and (2) the investment areas for the masses where they can see their world not only self-reliant but also will see huge growth in their invested amount in these industries in future.

Originality/value: The insights of five key industries, pharmaceutical, space, defence, renewal energy, and IT, highlight that India has the potential to achieve a five trillion-dollar economy in the future; however, it does not ignore the significant contribution of other industries in making of total GDP.

Purpose: This chapter aims to unveil the challenges of adopting and using banking chatbots in India and identify the challenges of Chat Generative Pre-trained Transformer (ChatGPT) for future banking.

Need for the study: Unveiling the challenges of chatbots and ChatGPT in the banking industry in India is crucial to understand the limitations and areas of improvement to enhance customer experience, ensure data security, and maintain regulatory compliance.

Methodology: The researchers conducted a narrative review systematically summarising and analysing existing literature on chatbots and ChatGPT, providing a comprehensive overview of the challenges faced in the industry.

Findings: The authors identify perceived risk, platform quality, connectivity and infrastructure, data privacy and security, user education and acceptance, existing legacy systems, and regulatory guidelines as the challenges of adopting chatbots. Additionally, the findings reveal that the challenges posed by ChatGPT in future banking include the potential reduction of traditional banking jobs, linguistic diversity, data privacy and security, ethical considerations and bias mitigation, explainability and accountability, integration with existing banking systems, and user trust and acceptance. However, implementing these new technologies also presents opportunities for individuals with unique human skills, such as critical thinking, empathy, and creativity, which are difficult to replace with technology.

Practical implications: By minimising the challenges of ChatGPT and chatbots, the banking industry could achieve improved customer service, cost efficiency, automation of routine tasks, and 24/7 availability, leading to enhanced customer satisfaction and operational efficiency in the banking industry. Additionally, these artificial intelligence (AI) tools enable data-driven insights, personalised experiences, scalability, and efficient handling of large customer volumes, contributing to better decision-making and enhanced customer engagement.

Social commerce has brought about a significant transformation in consumer experience due to diverse factors. As a result, users often find themselves prone to impulsive buying behaviour when exposed to such an environment. Prior research was limited to demonstrating the expanding influence of celebrities on social media and the linkage between social engagement and impulse buying context. Furthermore, the impulse buying tendency of consumers on social media in the context of celebrity posts has yet to be validated. This paper aims to assess the influence of consumer awareness, consumer trust and observational learning on the latent state-trait (LST) theory regarding celebrity posts on impulse buying tendencies.

The empirical research builds on a sample survey involving 750 students from the “Big Four” economics universities in Hanoi. The proposed model was analysed using a partial least squares structural equation modelling technique.

The authors find that consumer trust and observational learning from celebrity’ posts positively affect impulse buying tendency. Yet celebrity influence awareness directly impacts trust in celebrity’ posts rather than directly impacting impulse buying tendency. Perceiving the importance of interactive and authentic posts by a celebrity in influencing consumers’ purchase behaviour on social media, this research offers valuable insights for stakeholders in the digital celebrity sphere of communication and marketing.

Perceiving the importance of interactive and authentic posts by a celebrity in influencing consumers’ purchase behaviour on social media, this research offers valuable insights for stakeholders in the digital celebrity sphere of communication and marketing.

From a theoretical perspective, this expands the applicability of the LST theory in social commerce to promote impulse buying tendencies. Second, this contributes to the literature on the emerging phenomenon of social media celebrities, as existing literature does not clarify their influence on impulse buying behaviour. Third, this research applies the concept of observational learning in online shopping through key features of social media platforms, namely, likes, shares and comments, to investigate their influence on the impulse buying tendency of consumers. Concerning managerial implications, the authors propose practical recommendations for practitioners, particularly those involved or interested in the commercial services industry and social media marketing (namely, celebrities and partner companies).