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In the ever-evolving digital landscape, this study aims to explore which specific personality traits contribute to the innovativeness of startups, with a particular emphasis on understanding how technology adoption mediates this relationship. By doing so, the authors strive to unveil the nuanced dynamics of personality, technology adoption and startup innovativeness in the digital era.

The authors conduct a quantitative empirical analysis using a sample of 1,314 German startups. This study utilizes a mediation analysis to examine the effects of personality traits on the innovativeness of startups, taking technology adoption as a mediator into account.

The empirical results show certain personality traits have direct effects on innovativeness. Also, the results show that technology adoption is a driver of startup innovativeness. In addition, these traits are (partially) mediated by technology adoption.

The results shed new light on the interplay of entrepreneurs' personality and technology adoption in relation to startup innovativeness and therefore underline the importance of technology in this triangular relationship. The authors employ secondary data from startups in Germany, which complicates generalization of the results to other geographical and cultural contexts.

This study contributes to the scientific debate on the role of personality traits in entrepreneurship by providing empirical evidence on the mediating effect of technology adoption in the relationship between personality traits and startup innovativeness. The findings offer valuable insights for researchers, entrepreneurs and policymakers interested in understanding and promoting innovativeness in the context of startups.

This research examines the impact of the National Rural E-commerce Comprehensive Demonstration Project (NRECDP) on poverty reduction and income growth in rural China.

The study develops a theoretical framework, which considers the role of geographical, technological, institutional and cultural factors for the e-commerce poverty alleviation (e-CPA) model. Empirically, this study applies the difference-in-differences (DID) model and the event study approach to evaluate the effectiveness of NRECDP on the basis of large-scale county-level and household-level panel data spanning 2010 to 2020.

The study found that the NRECDP, as a government-led, information and communication technology (ICT)-enabled, market-based program, has led to a significant increase in per capita output of primary industry employees, as well as in the disposable income of rural residents, especially those in national-level poverty-stricken (NP) counties. The interventions of the NRECDP achieved these positive outcomes through transportation and Internet infrastructure improvement, ICT adoption and human capital accumulation in impoverished towns and villages in remote rural areas. These effects are larger in the eastern region of China, followed by the central region, whereas the weakest effects were found in the western region. However, we found little evidence of the NRECDP increasing household developmental expenditure.

The study findings have important practical and policy implications for rural e-commerce development and self-sustained poverty alleviation solutions. The research revealed the significance of government NRECDP interventions for increasing rural income, reducing living costs, and empowering the rural population in its multiple social roles, namely, as consumers, producers, employees and microentrepreneurs. The local cultural context may also play a role in ICT adoption and entrepreneurship cultivation with a downstream effect on the effectiveness of e-CPA practices. Policymakers would need to ensure a supportive entrepreneur-friendly environment for rural e-commerce development and continue implementing progressive policies for poverty alleviation.

This study explores poverty alleviation issues in China by developing for the first time a multi-faceted framework that is subsequently tested by both county-level and household-level large-scale observations. Also, it is the first study to provide nationwide empirical evidence on the effectiveness of e-CPA in narrowing down the spatial and digital divides in China. In addition to the impact of geography, technology and governmental support, this study also sheds light on the role of culture in the adoption and diffusion of digital technologies and as a source of local entrepreneurial opportunities.

The increasing urgency to address climate change in construction has made green construction (GC) and sustainability critical topics for academia and industry professionals. However, the volume of literature in this field has made it impractical to rely solely on traditional systematic evidence mapping methodologies.

This study employs machine learning (ML) techniques to analyze the extensive evidence-base on GC. Using both supervised and unsupervised ML, 5,462 relevant papers were filtered from 10,739 studies published from 2010 to 2022, retrieved from the Scopus and Web of Science databases.

Key themes in GC encompass green building materials, construction techniques, assessment methodologies and management practices. GC assessment and techniques were prominent, while management requires more research. The results from prevalence of topics and heatmaps revealed important patterns and interconnections, emphasizing the prominent role of materials as major contributors to the construction sector. Consistency of the results with VOSviewer analysis further validated the findings, demonstrating the robustness of the review approach.

Unlike other reviews focusing only on specific aspects of GC, use of ML techniques to review a large pool of literature provided a holistic understanding of the research landscape. It sets a precedent by demonstrating the effectiveness of ML techniques in addressing the challenge of analyzing a large body of literature. By showcasing the connections between various facets of GC and identifying research gaps, this research aids in guiding future initiatives in the field.

This study aimed to understand better what makes esports fans engage with streamers' live-streaming of esports gameplay. This study used the Theory of Planned Behavior (TPB) and additionally adopted streamer identification and esports game identification as moderating variables.

Data were collected from streamers' esports content streaming viewers over 18 years of age using an online survey in Amazon M-Turk (N = 307). Based on past esports live-streaming weekly watching hours, which range from 1 to 45 h, the participants were divided into lower (n = 152) and higher (n = 155) frequency groups. PLS-SEM and bootstrapping techniques were used to test the moderated mediation relationships among the constructs.

This study found a negative moderating effect of past watching experience on the relationship between attitudes and behavioral intention, and it positively moderated the path between perceived behavioral control and behavioral intention. Also, it was found statistically significant direct impacts of streamer identification (STI) and esports game identification (EGI) on attitude and subjective norms. While the indirect impact of STI on behavioral intention through attitude was statistically significant, there were no significant indirect impacts of EGI on attitude and behavioral intention through subjective norms.

Theoretically, this study extends the TPB model by exploring the two identifications (i.e. streamers and esports games) as antecedents of the focal TPB factors (i.e. attitudes, subjective norms and perceived behavioral control) and the moderating effect of prior experience based on high/low weekly watching frequencies. Practically, content creators of esports live-streaming and live-streaming platform managers can use the study’s findings to develop strategies to nurture their current and future viewership.

This study assesses the relationship between job satisfaction, perceived organizational support and workplace factors on officer turnover intention within an urban, municipal police organization.

Using data from an online survey of New York City Police Officers (n = 1,823), both bivariate analysis and logistic regression models were utilized to assess the salience of police officer job satisfaction, perceived organizational support and perception of six workplace domains, including financial compensation, environmental factors, professional fulfillment, work/quality of life balance, treatment from management and occupational risk, on predicting turnover intention.

The cross-sectional study finds that job satisfaction, financial factors (salary, benefits and retirement benefits) and fulfillment predict lower levels of turnover intention (i.e. higher levels of organizational commitment). Work–life balance and environmental factors (cleanliness of work environment and condition of equipment) predict higher levels of turnover intention. Both perceptions of organizational support and occupational risk, while significant in the bivariate models, were not significantly associated after accounting for other factors. There is no evidence that officer perception of public support or the risk of being injured/killed at work were related to officer turnover intention.

The current study is limited by its focus on only one police department and its use of cross-sectional data, which may limit the generalizability of the results to agencies that differ in size and type and do not allow for assessment of causality.

Officer turnover intention may be reduced by increasing financial compensation, improving the work environment and promoting a healthy work–life balance.

The study contributes to a growing body of research on police officer voluntary turnover by evaluating established predictors along with workplace factors in an urban police department: the setting where officer turnover intention is hypothesized to be the greatest.

This study aims to understand the learner behaviour of millennials for Massive Open Online Courses (MOOCs) in the post-adoption stage by extending the theory of Unified Theory of Acceptance and User Technology 2 (UTAUT2) with expectancy confirmation model (ECM) along with personal innovativeness as the exogenous, satisfaction as a mediating and continued intention as an endogenous construct.

This study applied a cross-sectional research design by using a survey method to collect primary data with a structured questionnaire. Convenience sampling was used to collect data from millennial MOOC users, and partial least square structural equation modelling method was applied for data analysis.

The results indicate that performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation influence satisfaction. Similarly, performance expectancy, hedonic motivation, personal innovativeness and satisfaction influence the continued intention for MOOCs.

In terms of limitations, the study applied a cross-sectional research design that could lead to data collection bias. Similarly, the study used convenience sampling as the authors did not have access to the participant list of users from MOOC platforms.

The research highlights various insights to all the stakeholders on improving MOOC satisfaction and enhance the continued intention for millennial learners.

The findings of this research bridge this gap by examining the post-adoption usage behaviour of MOOCs by extending the baseline model of UTAUT2 with personal innovativeness and integrating it with ECM.

As an emerging video comment feature, danmaku is gaining more traction and increasing user interaction, thereby altering user engagement. However, existing research seldom explores how the effectiveness of danmaku on user engagement varies over time. To address this research gap, this study proposes a comprehensive framework drawing on social presence theory and information overload theory. The framework aims to explain how the effectiveness of danmaku in increasing user engagement changes over shorter time intervals.

A research model was proposed and empirically tested using data collected from 1,019 movies via Bilibili.com, one of China’s most popular danmaku video platforms. A time-varying effect model (TVEM) was used to examine the proposed research model.

The study finds that the volume of danmaku and its valence exert a time-varying influence on user engagement. Notably, the study shows that danmaku volume plays a more substantial role in determining user engagement than danmaku valence.

This research offers theoretical insights into the dynamic impact of danmaku on user engagement. The innovative conceptualization and measurement of user engagement advance research on pseudo-synchronous communication engagement. Furthermore, this study offers practical guidelines for effectively managing danmaku comments on online video platforms.

This study aims to improve a construction company's overall project delivery by utilising lean six sigma (LSS) methods combined with building information modelling (BIM) to design, modularise and manufacture various building elements in a controlled factory environment off-site.

A case study in a construction company utilised lean six sigma (LSS) methodology and BIM to identify non-value add waste in the construction process and improve sustainability.

An Irish-based construction company manufacturing modular pipe racks for the pharmaceutical industry utilised LSS to optimise and standardise their off-site manufacturing (OSM) partners process and leverage BIM to design skids which could be manufactured offsite and transported easily with minimal on-site installation and rework required. Productivity was improved, waste was reduced, less energy was consumed, defects were reduced and the project schedule for completion was reduced.

The case study was carried out on one construction company and one construction product type. Further case studies would ensure more generalisability. However, the implementation was tested on a modular construction company, and the methods used indicate that the generic framework could be applied and customized to any offsite company.

This is one of the few studies on implementing offsite manufacturing (OSM) utilising LSS and BIM in an Irish construction company. The detailed quantitative benefits and cost savings calculations presented as well as the use of the LSM methods and BIM in designing an OSM process can be leveraged by other construction organisations to understand the benefits of OSM. This study can help demonstrate how LSS and BIM can aid the construction industry to be more environmentally friendly.

This paper explores the engagement of architecture and design-related disciplines within the built environment with social media. The study aims to identify key themes, assess the methodologies used and compare the approaches across different disciplines.

The research employs a semi-systematic literature review coupled with thematic analysis. This approach allows for a comprehensive examination of existing studies, identifying key themes, trends and gaps in the current knowledge base. Through this methodology, the paper synthesises diverse perspectives and insights from a wide range of sources.

Social media data is a valuable tool for understanding architecture and the built environment, but there is a significant divergence in methodologies across disciplines. There is a growing advocacy for mixed-method approaches that would benefit from critical engagement with data that’s underpinned by theoretical frameworks. Such approaches enhance research robustness, support multidisciplinary collaboration and improve the applicability of social media data in design practices.

Despite the proliferation of literature on social media and the built environment, there remains a lack of nuanced understanding regarding thematic variations within individual design disciplines and the specific research methodologies they employ. This paper critically reviews existing research to offer current insights, research gaps and potential future research directions on social media and architecture/design-related disciplines in the built environment.

Although the prior literature has identified the relevance of dealer participation for multinational enterprises (MNEs), it is unclear whether such participation could also be an important means for local dealers to learn from MNEs. By adopting local firms’ viewpoint, our study draws on organizational learning theory to examine how local dealers benefit from their participation with foreign suppliers in Africa.

The empirical setting is a combinative dataset of secondary data and primary survey of 164 small- and medium-sized local dealers with nine subsidiaries of a Chinese motorcycle company in six countries of Sub-Saharan Africa.

This research shows that dealer participation is positively associated with dealer performance, and this positive effect is stronger when local dealers operate in regions with low government corruption and high government support. However, the positive relationship is weaker when local dealers use the local tongue extensively but becomes stronger when their foreign suppliers have a high dealer coverage.

By taking a local-participant perspective, our study extends the participation literature to show how firms from a resource-constrained region may benefit from their proactive participation with foreign counterparts. Additionally, we identify the boundary conditions of institutional factors and strategic choices of local dealers and foreign suppliers, providing a nuanced understanding of firm behaviors in complex and uncertain markets.