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The purpose of this study was to analyze, through a sequential model, the underlying mechanisms connecting transformational leadership with employee task performance. Specifically, it examined the causal chain of transformational leadership-job characteristics (task variety and task significance)-job engagement-task performance.

The hypotheses were tested on a sample of 320 employees and their supervisors from Spanish young technology and knowledge-intensive small and medium-sized enterprises (SMEs) using structural equations.

The results show that the effect of transformational leadership on task performance (assessed by supervisors) occurs sequentially via task significance and job engagement. Additionally, job engagement mediates the relationship between task variety and task performance.

Findings highlight the relevance for supervisors to employ a transformational leadership style that leads employees directly and indirectly, through task significance, to be more engaged and achieve higher task performance. They also emphasize the importance of proper job design that allows employees to be fully invested in their job performance.

Despite the importance of leadership and employee performance for the survival and growth of these firms, the study of these relationships is largely unexplored. This study proposes and tests a serial model in which supervisor transformational leadership is linked to employee task performance through two sequential mediators: job characteristics (task variety and task significance) and job engagement.

With the outbreak of the COVID-19 pandemic, online assessment has become the dominant mode of examination in higher education institutions. However, there are contradictory findings on how students perceive online assessment and its impact on their academic performance. Thus, the purpose of this study is to evaluate the potential impact of online assessment on students' academic performance.

This study proposes a research model based on the task–technology fit theory and empirically validates the model using a survey from students in the UK. In addition, the study conducted four experiments based on paper-based and online assessments and analysed the data using paired sample t test and structural equation modelling.

The findings show that the use of online assessment has a positive impact on students' academic performance. Similarly, the results from the experiment also indicate that students perform better using online assessments than paper-based assessments.

The findings provide crucial evidence needed to shape policy towards institutionalising online assessment. In addition, the findings provide assurance to students, academics, administrators and policymakers that carefully designed online assessments can improve students' academic performance. Moreover, the study also provides important insights for curriculum redesign towards transitioning to online assessment in higher education institutions.

This study advances research by offering a more nuanced understanding of online assessment on students' academic performance since the majority of previous studies have offered contradictory findings. In addition, the study moves beyond existing research by complementing assessment results with the views of students in evaluating the impact of online assessment on their academic performance. Second, the study develops and validates a research model that explains how the fits between technology and assessment tasks influence students' academic performance. Lastly, the study provides evidence to support the wide use of online assessment in higher education.

In the aftermath of disasters, humanitarian actors need to coordinate their activities based on accurate information about the disaster site, its surrounding environment, the victims and survivors and the supply of and demand for relief supplies. In this study, the authors examine the characteristics of radio frequency identification (RFID) technology and those of disaster relief operations to achieve information visibility and actor coordination for effective and efficient humanitarian relief operations.

Building on the contingent resource-based view (CRBV), the authors present a model of task-technology fit (TTF) that explains how the use of RFID can improve visibility and coordination. Survey data were collected from humanitarian practitioners in India, and partial least squares (PLS) analysis was used to analyze the model.

The characteristics of both RFID technology and disaster relief operations significantly influence TTF, and TTF predicts RFID usage in disaster relief operations, providing visibility and coordination. TTF is also a mediator between the characteristics of RFID technology and disaster relief operations and between visibility and coordination.

The many recent humanitarian disasters have demonstrated the critical importance of effective and efficient humanitarian supply chain and logistics strategies and operations in assisting disaster-affected populations. The active and appropriate use of technology, including RFID, can help make disaster response more effective and efficient.

Humanitarian actors value RFID technology because of its ability to improve the visibility and coordination of relief operations. This study brings a new perspective to the benefits of RFID technology and sheds light on its antecedents. The study thus expands the understanding of technology in humanitarian operations.

Based on the principles of positive psychology, this study aims to investigate the mediating role of job characteristics (task, knowledge, social and work context) in the connection between lean manufacturing (LM) practices and employee well-being.

Employee well-being, encompassing job satisfaction, organizational affective commitment and work involvement, is conceptualized as a second-order construct. Using a proposed model, an online survey was administered to 520 participants across 23 operational sectors categories of the Brazilian manufacturing industry. The data is processed using structural equation analysis.

The research shows that the relationship between LM practices and employee well-being is partially mediated through three out of the four work characteristics (task, knowledge and social). In addition, LM exerts a direct and significant influence on employee well-being.

This study contributes to practical insights by encouraging industrial managers to refine their managerial work design. It highlights the importance of appropriately dimensioning tasks, nurturing social skills to enhance interactions and task execution and optimizing physical facilities to counteract potential initial-stage work intensification during LM implementation.

Previous research about organizational behavior extensively examines happiness at work, focusing on constructs such as well-being, satisfaction, commitment, engagement and motivation, but there is a lack of studies assessing employee well-being in the lean context, particularly from a positive perspective.

Artificial intelligence (AI) is a growing paradigm and has made considerable changes in many fields of study, including medical education. However, more investigations are needed to successfully adopt AI in medical education. The purpose of this study was identify the determinant factors in adopting AI-driven technology in medical education.

This was a descriptive-analytical study in which 163 faculty members from Tabriz University of Medical Sciences were randomly selected by nonprobability sampling technique method. The faculty members’ intention concerning the adoption of AI was assessed by the conceptual path model of task-technology fit (TTF).

According to the findings, “technology characteristics,” “task characteristics” and “TTF” showed direct and significant effects on AI adoption in medical education. Moreover, the results showed that the TTF was an appropriate model to explain faculty members’ intentions for adopting AI. The valid proposed model explained 37% of the variance in faulty members’ intentions to adopt AI.

By presenting a conceptual model, the authors were able to examine faculty members’ intentions and identify the key determining factors in adopting AI in education. The model can help the authorities and policymakers facilitate the adoption of AI in medical education. The findings contribute to the design and implementation of AI-driven technology in education.

The finding of this study should be considered when successful implementation of AI in education is in progress.

Task complexity is one of the significant factors that influences and is used for forecasting employee performance and determining labor cost. However, the complexity level of tasks is unstructured, dynamic and complicated to perform. This paper develops a new method for evaluating the complexity level of tasks in the production process to support production managers to control their manufacturing systems in terms of flexibility, reliability to production planning and labor cost.

The complexity level of tasks will be analyzed based on the structuralist concept. Using the structure of task, the factors that significantly affect the task complexity in an assembly line will be defined, and the complexity level of the task will be evaluated by measuring the number of task components. Using the proportional 2-tuples linguistic values, the difference between the complexity levels of tasks can be compared and described clearly.

Based on the structure of the task, three contributory factors including input factors, process-operation factors and output factors that significantly affect the task complexity in an assembly line are identified in the present study. The complexity level of the task is quantified through analyzing the details of the three factors according to two criteria and six sub-criteria within the textile case study.

The proposed approach provides a new insight about the factors that have an effect on the complexity of tasks in production and remedies some of limitations of previous methods. The combination of experts' experience and scientific knowledge will improve the accuracy in determining the complexity level of tasks.

Although humanoid robots are increasingly adopted in many business settings, the dynamic effects of anthropomorphism and the functional perceptions of service robots on consumers’ responses remain unclear. This paper aims to examine the impacts of robot anthropomorphism on consumers’ trust, receptivity and the downstream effect on satisfaction. Furthermore, it examines the mediating effects of perceived ease of use (PEOU) and perceived usefulness (PU) in the relationship between anthropomorphism and consumer responses.

After conducting two separate pilot studies to help design the research materials, this research involves three sequential studies. In studies 1A and 1B, the authors used two distinct humanoid robots (i.e. Connie and Pepper) to test the direct effects of anthropomorphism on trust and receptivity and the mediated effects via PEOU and PU. Study 2 conducted a 2 (robot appearance: machine-like vs. human-like) × 2 (task complexity: low vs. high) between-subjects experimental design to further explore the boundary effects of task complexity on trust and customer satisfaction.

This research theorizes and empirically examines the mediating effects of PEOU and PU in the relationship between anthropomorphism and consumers’ responses (i.e. trust and receptivity) to service robots. Results also demonstrate a moderating role of task complexity, whereby only when the task was complex did anthropomorphism affect consumer responses and customer satisfaction. The parallel mediations of PEOU and PU were also confirmed. However, when task complexity was low, the authors observed no differences between human- and machine-like robots.

First, this research used a scenario-based method by exposing participants to different pictures or videos of service robots and measuring individuals’ responses. Consumers may respond differently upon interacting with robots in actual service contexts. Second, future research could investigate the effects of other aspects of anthropomorphism, such as robots’ voice characteristics (gender, high/low pitch), verbal communication styles and emotional expression. Finally, future research could explore other service contexts to test the generalizability of the findings.

Findings of this study also provide useful insight for companies interested in adopting service robots. First, the authors unearthed several positive outcomes of using human-like versus machine-like robots in service settings. Despite concerns about the perceived creepiness and discomfort associated with human-like robots, managers should not worry about these service agents’ potential negative effects. Second, it shows that human-like robots’ competitive advantage over machine-like robots stands out when task complexity is high. Managers should therefore carefully consider relevant service characteristics and task requirements when deciding whether to adopt robots.

This study provides original and valuable contributions to the growing literature on service robots by addressing scholarly incongruencies regarding the impact of anthropomorphism and disentangling its positive influence on consumers’ perceptions and acceptance of service robots. This study also contributes to research on technology acceptance and service robot receptivity by empirically demonstrating the mediating role of PEOU and PU. Furthermore, this research enriches the body of knowledge on task-technology fit by providing evidence that task complexity is a crucial factor to consider in service robot design.

The purpose of the study is to identify the soft skills and abilities that are crucial to success in the fields of operations management (OM) and supply chain management (SCM), using the O*NET database and the classification of a set of professional figures integrating values for task skills and abilities needed to operate successfully in these professions.

The study used the O*NET database to identify the soft skills and abilities required for success in OM and SCM industries. Correlation analysis was conducted to determine the tasks required for the job roles and their characteristics in terms of abilities and soft skills. ANOVA analysis was used to validate the findings. The study aims to help companies define specific assessments and tests for OM and SCM roles to measure individual attitudes and correlate them with the job position.

As a result of the work, a set of soft skills and abilities was defined that allow, through correlation analysis, to explain a large number of activities required to work in the operations and SCM (OSCM) environment.

The work is inherently affected by the database used for the professional figures mapped and the scores that are attributed within O*NET to the analyzed elements.

The information resulting from this study can help companies develop specific assessments and tests for the roles of OM and SCM to measure individual attitudes and correlate them with the requirements of the job position. The study aims to address the need to identify soft skills in the human sphere and determine which of them have the most significant impact on the OM and SCM professions.

The originality of this study lies in its approach to identify the set of soft skills and abilities that determine success in the OM and SCM industries. The study used the O*NET database to correlate the tasks required for specific job roles with their corresponding soft skills and abilities. Furthermore, the study used ANOVA analysis to validate the findings in other sectors mapped by the same database. The identified soft skills and abilities can help companies develop specific assessments and tests for OM and SCM roles to measure individual attitudes and correlate them with the requirements of the job position. In addressing the necessity for enhanced clarity in the domain of human factor, this study contributes to identifying key success factors. Subsequent research can further investigate their practical application within companies to formulate targeted growth strategies and make appropriate resource selections for vacant positions.

Seekers who visit professional virtual communities (PVCs) are usually motivated by knowledge-seeking, which is a complex cognitive process. How do seekers search for knowledge, and how is their search linked to prior knowledge or PVC situation factors? From the cognitive process and interactional psychology perspectives, this study investigated the three-way interactions between seekers’ expertise, task complexity, and perceptions of PVC features (i.e. knowledge quality and system quality) on knowledge-seeking strategies and resultant outcomes.

A field experiment was conducted with 119 seekers in a PVC using a 2 × 2 factorial design of seekers’ expertise (i.e. expert versus novice) and task complexity (i.e. low versus high).

The study reveals three significant insights: (1) For a high-complexity task, experts adopt an ask-directed searching strategy compared to novices, whereas novices adopt a browsing strategy; (2) For a high-complexity task, experts who perceive a high system quality are more likely than novices to adopt an ask-directed searching strategy; and (3) Task completion time and task quality are associated with the adoption of ask-directed searching strategies, whereas knowledge seekers’ satisfaction is more associated with the adoption of browsing strategy.

We draw on the perspectives of cognitive process and interactional psychology to explore potential two- and three-way interactions of seekers’ expertise, task complexity, and PVC features on the adoption of knowledge-seeking strategies in a PVC context. Our findings provide deep insights into seekers’ behavior in a PVC, given the popularity of the search for knowledge in PVCs.

Innovative work behaviour (IWB) has long been advocated as a crucial resource for organisations. Evidence that work characteristics stimulate the adoption of IWB is widespread. Yet, the relationship between knowledge characteristics and IWB has often been overlooked. This study aims to address this gap by examining this relationship.

Building on an integrative vision of innovation, this study analyses the effects of combinations in work characteristics on IWB through a configurational approach. Job autonomy, complexity, problem solving, specialisation and demand for constant learning were examined as determinants of IWB using fuzzy-set qualitative comparative analysis.

Based on a sample of 214 Belgium employees, the results highlight seven configurations of work characteristics to elicit high levels of IWB. For six of them, problem solving appears as a needed condition.

Presented findings offer insights for organisations aiming at evolving in a competitive context to generate optimal conditions for promoting employee innovation.

While most studies have tested the influence of work characteristics independently, this research investigates the joint influence of work characteristics and identifies how combinations of multiple variables lead to IWB.