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The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.

To investigate the influence of logistics and transportation workers’ perceptions of their management’s simultaneous safety and operations focus (or lack thereof) on related worker safety and operational perceptions and behaviors.

This multi-method research consisted of two studies. Study 1 aimed to establish correlational relationships by evaluating the impact of individual-level worker perceptions of operationally focused routines (as a moderator) on the relationship between worker perceptions of safety-related routines and workers’ self-reported safety and in-role operational behaviors using a survey. Study 2 aimed to establish causal relationships by evaluating the same conceptual relationships in a behavioral-type experiment utilizing vehicle simulators. After receiving one of four pre-task briefings, participants completed a driving task scenario in a driving simulator.

In Study 1, the relationship between perceived safety focus and safety behavior/in-role operational behavior was strengthened at higher levels of perceived operations focus. In Study 2, participants who received the balanced pre-task briefing committed significantly fewer safety violations than the other 3 treatment groups. However, in-role driving deviations were not impacted as hypothesized.

This research is conducted at the individual (worker) level of analysis to capture the little-known perspectives of logistics and transportation workers and explore the influence of balanced safety and operational routines from a more micro perspective, thus contributing to a deeper understanding of how balanced routines might influence worker behavior when conducting dynamic tasks to ensure safe, effective outcomes.

This paper aims to construct a new turnover theory to explain and predict employee voluntary turnover in a more in-depth and comprehensive way.

Based on the literature review and theoretical analysis, this paper constructs a new turnover theory called the psychological goal system driving theory of employee turnover.

The psychological goal system driving theory of employee turnover advocates that there are psychological goals in the individual psychological world that point to the future and seek self-realization, and that there is a synergistic or competitive relationship among different psychological goals, and thus forming a psychological goal system and the dominant goals (including single goal or goal group) that exist in it; the individual’s dominant goals are the source of motivation, which initiate and organize the individual’s cognition and behavior; when the dominant psychological goals are difficult to achieve or destroyed in the original organization, they will produce continuous negative emotions and drive the individual to choose new and suitable job opportunities to realize themselves. Therefore, the dominant psychological goal is the organizer and driver of the employee turnover behavior, and when they are threatened, they will drive individuals to actively terminate the employment relationship with the current organization to better promote or protect their own realization process and sustainable growth.

This paper constructs a new turnover theory based on the self-organization goal system theory of motivation and personality.

Building on social exchange theory (SET), the main aim of this paper is to empirically study the impact of high-commitment work practices (HCWPs) systems on radical innovation. Additionally, the paper examines the mediating roles of employee innovative work behaviour (IWB) and knowledge sharing (KS) in the relationship between HCWPs and radical innovation.

Using a survey questionnaire, data were collected from employees working in pharmaceutical, manufacturing and technological industries in Jordan. A total of 408 employees participated in the study. Structural equation modelling (SEM) using AMOS v28 was employed to test the research hypotheses.

This research found that HCWPs in the form of a bundle of human resource management (HRM) practices are significant for employee IWB and KS. However, similar to previous studies, this paper failed to find a direct significant impact for HCWPs on radical innovation. Rather, the impact was mediated by employee IWB. Additionally, this paper found that HCWPs are significant for KS and that KS is significant for employee IWB.

Distinctively, this paper considered the mediating effect of employee IWB on radical innovation. Extant research treated IWB as a consequence of organisational arrangements such as HRM practices; this paper considered IWB as a foundation and source for other significant organisational outcomes, namely radical innovation. Additionally, the paper considered employees' perspectives in studying the relationship between HRM, KS, IWB and radical innovation.

This study provide an in-depth review on the knowledge structure of green information technology (GIT) adoption and behavior. Environmental degradation has escalated even further with information and digital technology development. Researchers have come up with a new concept of GIT to dampen the carbon emission due to the excessive use of IT in today’s everyday usage. A similar terminology, green information system (GIS), is a rather broad understanding of GIT, which relates to the environmental management system to improve operations in the organization and will be included in the scope of the study.

This study presents a science mapping analysis through a bibliometric review to explore emerging trends and predict future trends based on 293 publications in the Web of Science.

The bibliographic coupling analysis discovered five themes related to the theoretical foundation of GIT and the determinants of their adoption. The five themes are (1) theoretical foundation in GIT, (2) determinants of green IT and IS adoption, (3) fundamental of GIT and information science, (4) green technologies and green computing and (5) determinants of managers green IT adoption behavior. While co-word analysis presents the impact of GIT, driving performance and energy efficiency through the adoption of GIT producing four themes, (1) GIT acceptance through the theory of planned behavior, (2) impact of GIT’s: strategies for sustainable implementation, (3) driving sustainable performance through green innovation in information systems and technology and (4) energy efficiency and sustainability in green computing and cloud computing.

The finding is relevant to managers, researchers and stakeholders bounded by environmental responsibilities to mitigate its impact on the socioeconomic and environment through GIT adoption.

The contribution of this study is presenting an in-depth analysis of the knowledge structure through bibliometric analysis by providing network visualization on one of the crucial pro-environmental behavior.

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

For start-ups or growing firms, to effectively navigate the unpredictable nature of digital development and achieve superior innovative performance, it is crucial to have a workforce comprised of creative and innovative employees. Drawing upon the principles of social information processing theory, this study aims to investigate whether specific combinations of organizational internal and external environments, as well as work characteristics in the digital age, can foster a high level of employee innovative behavior.

By collecting a multilevel and multisource data set comprising 693 employees and 88 CEOs from 88 start-ups or growing firms, this study used fuzzy-set qualitative comparative analysis to examine the distinctive configurations associated with achieving a high level of employee innovative behavior.

The study found that six solutions enabled employees to innovate more effectively, but six solutions led to the absence of employee innovative behavior.

The findings of this study offer important theoretical and practical implications to motivate employee innovative behavior in Chinese enterprises.

First, this study contributes to the literature on employee innovative behavior by addressing the need to explore the impact of the digital context on promoting innovation among employees. Second, this study adds to the existing literature on employee innovation and entrepreneurship by examining multiple organizational contexts and their influence on innovative behavior. Third, this study makes a significant contribution to the field of employee innovative behavior by examining the macroenvironment surrounding digital transformation within enterprises and integrating both internal and external organizational factors.

The different dimensions of the online engagement behaviors exhibited by omnichannel shoppers, who mainly rely on the online channel for information search, are still understudied. This study aims to investigate how service journey quality (SJQ) has an impact on the overall omnichannel customer experience leading to customer identification (CI) with the store, subsequently leading to their exhibition of online engagement behaviors (writing online reviews, blogging, rating products and service online and indulging in customer-to-customer online interactions.

The research is cross-sectional, quantitative and descriptive. Purposive sampling was used to choose the research's participants. Data were collected from 591 Indian omnichannel customers who had previously made an omnichannel purchase that included the concurrent usage of various channels of a retailer using a verified self-administered survey. Using the Smart PLS 4.0 software, the proposed conceptual model has been evaluated.

The results indicate that omnichannel customer experience mediates the relationship between SJQ and CI with the store, subsequently leading to their exhibition of online engagement behaviors (writing online reviews, blogging, rating products and service online and indulging in customer-to-customer online interactions). The perceived customer gratitude toward the store significantly and positively moderated the direct relationship between SJQ and different online engagement behaviors (writing online reviews, blogging, rating products and service online and indulging in customer-to-customer online interactions).

The study relied upon the omnichannel shoppers of only Indian population and relied on a cross-sectional data collection procedure for this research.

Post-pandemic, with highly dynamic shifts in customer preferences, the need for channel-agnostic shopping leading to the unpredictability of purchase patterns has made SJQ the only dimension to achieve sustainable loyalty intentions through value co-creation in an omnichannel retail context. Emphasizing post-purchase behaviors like different online engagement behaviors (writing online reviews, blogging, rating products and services online and indulging in customer-to-customer online interactions), this study is the first to show that SJQ might affect four different online customer engagement behaviors through omnichannel shopping experience and CI with the store. The moderating effect of customer-perceived gratitude toward the retailer on a few proposed hypotheses was also tested to give managerial recommendations. The study also answers the call to investigate the moderating role of customer gratitude in determining service quality-driven engagement behaviors.

This study aims to test the market anomaly of herding behavior driven by the response to supply chain disruptions in extreme market conditions such as those observed during COVID-19. The authors empirically test the response of the capital market participants for B2B firms, resulting in herding behavior.

Using the event study approach based on the market model, the authors test the impact of supply chain disruptions and resultant herding behavior across six sectors and among different B2B firms. The authors used cumulative average abnormal returns (CAAR) and cross-sectional absolute deviation (CSAD) to examine the significance of herding behavior across sectors.

The event study results show a significant effect of COVID-19 due to supply chain disruptions across specific sectors. Herding was detected across the automotive and pharmaceutical sectors. The authors also provide evidence of sector-specific disruption impact and herding behavior based on the black swan event and social learning theory.

The authors examine the impact of COVID-19 on herding in the stock market of an emerging economy due to extreme market conditions. This is one of the first studies analyzing lockdown-driven supply chain disruptions and subsequent sector-specific herding behavior. Investors and regulators should take sector-specific responses that are sophisticated during extreme market conditions, such as a pandemic, and update their responses as the situation unfolds.

This study investigates the relationship between leaders’ collectivist orientation and employees’ innovative behavior, as well as the mediating effects of employees’ collectivist orientation and servant leadership.

Based on a survey of 40 leaders and 219 employees in 12 technologically innovative enterprises in Jiangsu Province, China, a hierarchical linear modeling is used.

The results show that leaders’ collectivist orientation significantly positively affects employees’ innovative behavior. Moreover, leaders’ collectivist orientation significantly positively affects employees’ collectivist orientation/servant leadership, employees’ collectivist orientation/servant leadership significantly positively affects employees’ innovative behavior, and employees’ collectivist orientation/servant leadership partially mediates the relationship between leaders’ collectivist orientation and employees’ innovative behavior.

In response to the lack of research on the relationship between leadership cultural orientation and employees’ innovative behavior, this study sheds light on the effectiveness and mechanism of the influence of leaders’ collectivist orientation on employees’ innovative behavior, thus expanding and deepening the boundaries of theoretical research on leadership, culture and innovation management.