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Despite the deployment of state-of-the-art methodologies for project management, employee turnover in projects remains high. Such turnover has significant costs in terms of replacing personnel, potential deadline overruns and financial expenditure. Employee turnover in project contexts may stem from time-related issues associated with multiple parallel projects and short deadlines. Using person–environment fit and time congruence theories, this research examines the relationship between employee turnover intentions and individual–organizational (I–O) polychronicity fit, which captures the degree of match between individuals’ and organizational preferences for focusing on multiple tasks simultaneously.

Survey data were collected from 309 software project employees in Pakistan. Hypotheses were tested using polynomial regressions and response surface modeling.

I–O polychronicity fit is related to turnover intentions. Turnover intentions are lower when I–O polychronicity fit occurs on the lower end of the polychronicity continuum, whereas turnover intentions are higher when fit is observed on the higher end of the polychronicity continuum. The relationship between I–O polychronicity fit and turnover intentions is significantly explained by exhaustion and perceptions of work overload.

The study’s insights provide recommendations for organizations to optimally manage multitasking to help retain project employees.

These findings extend our understanding of the underlying mechanisms between I–O polychronicity fit and turnover intentions. Furthermore, this research expounds on how employee exhaustion and perceptions of work overload explain the relationship between I–O polychronicity fit and turnover intentions.

The purpose of this study was to fill the gap in understanding the impact of Drug Recognition Expert (DRE) evidence and testimony in driving under the influence (DUI) trials. This was accomplished by documenting and analyzing the perceptions of DREs and the DRE program across different stakeholders to understand how and when this type of evidence is used in DUI trials.

The methodology is a qualitative case study of the DRE program in one police agency in Washington. Data were collected using semi-structured interviews with criminal justice actors and state-level experts on their perceptions of the DRE program for the agency. Themes were developed from these interviews to analyze their perceptions of the efficacy and utility of DREs in trials.

While the courts in Washington accept DRE evidence in criminal trials, DRE evidence is largely absent in the adjudication process. Participants noted multiple reasons for this, including the lack of trials, the primacy of blood evidence and the expansion of the Advanced Roadside Impaired Driving Enforcement (ARIDE) program.

Although the DRE program has been around for decades, there is a lack of peer-reviewed studies regarding DRE evidence, and no studies regarding how court actors perceive and use DRE evidence. Understanding when and how DRE evidence is utilized in DUI trials can increase its value and utility by prosecutors and the national DRE program.

The purpose of this study is to investigate the negative spillover effects of family incivility on employees’ ability to meet their work-related deadlines. Furthermore, we examine emotional exhaustion as a mediator and emotional intelligence as a moderator in this process.

We test our predictions through a multisource, time-separated data collection involving 161 employee-supervisor dyads working in various hospitality sector organizations in Pakistan – a relatively understudied context where the hospitality industry is struggling.

The findings of our study indicate that exposure to rude family behavior hinders employees’ ability to complete work tasks on time, as mediated by their emotional exhaustion. However, this harmful process can be curtailed if employees can rely on their emotional intelligence to regulate their own emotions and those of their family members.

The findings of this study provide pertinent insights for HR managers and organizations to help their employees deal with family issues. Moreover, organizations can benefit from introducing training programs that improve employees’ emotional intelligence and increase the likelihood that they do not default on their work deadlines.

This study extends the limited existing family incivility research by investigating its spillover effects on employees’ ability to meet work deadlines, a previously unexplored relationship. In addition, we highlight the roles of two pertinent aspects: emotional intelligence and emotional exhaustion.

This study aims to evaluate sustainable investment policies for housing in Qatar.

This paper proposes a new model for analyzing sustainable investment policies for housing demand in Qatar via a hybrid quantum fuzzy decision-making model. The study processed the criteria with the facial expression-based Quantum Spherical fuzzy DEMATEL and ranked the alternatives with the facial expressions-based quantum spherical fuzzy TOPSIS. Four factors were determined due to a comprehensive literature review (Environment, Housing Design, Building Design, and Surrounding the building), with five sustainable investment policy alternatives (Electricity production with renewable energies, Recycling systems and materials in construction, Transport with less carbon emission, Biodiversity for residents, and Resilience to natural disasters).

The analysis indicates that the design of the building is the most important factor (0.254), while the environment is the most influencing factor (0.253) regarding housing demand in Qatar. Transport with less carbon emission and electricity production with renewable energies are the most critical alternative investment policies.

This study provides useful insights for regulators, policymakers, and stakeholders in Qatar’s sustainable investment policies for housing demand. The main motivation of this study is that there is a need for a novel model to evaluate the sustainable investment policies for housing demand. The main reason is that existing models in the literature are criticized due to some issues. In most of these models, emotions of the experts are not taken into consideration. However, this situation has a negative impact on the appropriateness of the findings. Because of this situation, in this proposed model, facial expressions of the experts are considered. With the help of this issue, uncertainties in the decision-making process can be handled more effectively.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

In the vibrant world of social commerce (SC), where information flows freely, interactions thrive and online purchases abound, there is an escalating challenge. Users are uninstalling and disengaging due to approach and avoidance stimuli, a trend mirroring the approach-avoidance motivation model (AAMM). Our study, anchored in AAMM and the stimulus-organism-response (SOR) model, aims to dive into the complex dynamics of these factors that shape users' SC continuance intentions.

Our findings, drawn from 472 SC users in Malaysia, paint an intriguing research framework via PLS-SEM analysis by testing the proposed hypotheses. A purposive sampling technique was utilized, deliberately selecting respondents based on specific criteria. Subsequently, data were gathered through the distribution of face-to-face questionnaires at selected shopping malls, facilitating a focused and comprehensive exploration of consumer perspectives.

The empirical results demonstrate the following: (1) Users' determination to stay engaged on SC platforms hinges on approach factors, like emotional support, surveillance gratification and multisensory gratification. (2) Simultaneously, avoidance factors such as technostress and perceived deception exert their negative influence. (3) Flow experience, rooted in flow theory, emerges as the underlying mechanism connecting these duality stimuli, influencing the continuance intention.

In a departure from conventional research, our study pioneers a comprehensive approach and boldly confronts the research gap by introducing a rich tapestry of antecedents, embracing both the appeal of approach factors and the deterrence of avoidance ones, using the AAMM that sheds light on how individuals navigate between embracing opportunities and avoiding pitfalls based on perceived gains and losses. This holistic approach enables us to redefine our understanding of digital engagement dynamics, offering a captivating journey into the realm of user experience and intention that transcends the ordinary.

The current work aims to understand the consumers’ adoption of electric vehicles (two-wheelers) from their value perspective by utilizing the value-based adoption model.

The study considered data from 302 potential electric two-wheeler customers and tested the hypotheses using structural equation modeling.

The outcomes showed that perceived economic benefits, social image, enjoyable acceleration and enhanced fun and perceived environment (positively) and perceived physical safety risk, perceived cost of ownership and range and charging risk (negatively) influenced the customers’ perceived value linked with electric two-wheeler (ETW) adoption. Only low engine noise emission and infrastructure issues did not affect perceived value.

Most of the respondents considered in the study were less than 35 years old. Hence, the model can be tested for other age groups.

The study’s outcomes will help ETW marketers, manufacturing companies and governments (state and central) to provide a more convenient environment for electric two-wheelers' adoption and help them curate appropriate strategies.

The current work offers a better understanding of potential customers' ETW adoption by employing a value-based trade-off.

With the advancement of digital transformation, it is important for e-retailers to use artificial intelligence (AI) for customer engagement (CE), as CE enables e-retail brands to succeed. Essentially, AI e-marketing (AIeMktg) is the use of AI technological approaches in e-marketing by blending customer data, and Retail 4.0 is the digitisation of the physical shopping experience. Therefore, in the era of Retail 4.0, this study investigates the factors influencing the use of AIeMktg for transforming CE.

The primary data were collected from 305 e-retailer customers, and the analysis was performed using a quantitative methodology.

The results reveal that AIeMktg has tremendous applications in Retail 4.0 for CE. First, it enables marketers to swiftly and responsibly use data to anticipate and predict customer demands and to provide relevant personalised messages and offers with location-based e-marketing. Second, through a continuous feedback loop, AIeMktg improves offerings by analysing and incorporating insights from a 360-degree view of CE.

The main contribution of this study is to provide theoretical underpinnings of CE, AIeMktg, factors influencing the use of AIeMktg, and customer commitment in the era of Retail 4.0. Subsequently, it builds and validates structural relationships among such theoretical underpinning variables in transforming CE with AIeMktg, which is important for customers to expect a different type of shopping experience across digital channels.

By integrating the Uses and Gratifications Theory and Flow Theory, this research seeks to untwine the veiled effects of interactive commerce enhancements (ICEs), specifically haptic imagery and social presence, in promoting user immersion and sustaining social commerce (SC) users' usage intention, considering the moderating role of autotelic personality.

The research utilized purposive sampling of Malaysian SC app users with recent transactions. A dual-source data collection approach, encompassing offline and online channels, was employed to ensure a broad and diverse respondent pool. Partial least squares–structural equation modeling was chosen for its adeptness in analyzing complex relationships in predictive studies.

The findings revealed the significant positive effects of haptic imagery and social presence on user immersion and continuance intention within SC apps. Social presence and immersion were found to mediate the proposed paths. Additionally, autotelic personality traits were identified as moderators, influencing the strength of these relationships.

This research makes a unique contribution by addressing critical gaps in SC environments, extending the concept of ICEs, understanding the impacts of underlying mediators and pioneering the examination of autotelic personality traits' moderating effects. It introduces a fresh perspective on how individual differences impact user engagement. This groundbreaking study benefits social media and interactive marketing literature by comprehensively understanding how ICEs elevate SC, fostering innovation and heightened engagement.

The purpose of this study is to explore how past and future temporal focus of CEOs in the hospitality industry influence their intention to invest in metaverse technology and the underlying mechanism under boundary conditions of perceived competitive pressure.

This multi-informant study collected data over three waves from a sample of 235 CEOs and their subordinates in India’s hospitality industry. A PLS-SEM was applied to the study data. Further, the study also used phenomenological interviews to capture CEOs’ perspectives on the study’s conceptual model.

Findings suggest that the past temporal focus of CEOs decreases technology orientation, and future temporal focus increases the technology orientation of firms, consequently impacting the intention to invest in the metaverse. CEOs’ perceived competitive pressure moderates the mediating relationship, such that the negative impact of past temporal focus on technology orientation is decreased and that of future temporal focus on the CEO is increased.

By exploring the role of a CEO’s past and future temporal focus on influencing technology orientation and, hence, adoption of new technology, the study extends upper-echelon theory to the field of metaverse adoption in the hospitality industry and responds to scholars’ calls to explore the industry’s technology adoption from the lens of the upper echelon.

The study has significant implications for the success of the adoption of metaverse technology in the hospitality industry. Findings imply that the board members should encourage CEOs to have future temporal focus.

The study provides novel insights into the adoption of metaverse technology by the hospitality industry, where CEO attributes such as their temporal focus influence intention to invest in metaverse.