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This study aimed to understand the agri-entrepreneurial traits of undergraduate university students using machine learning (ML) algorithms.

This study used a conceptual framework of individual-level determinants of entrepreneurship and ML. The Google Survey instrument was prepared on a 5-point scale and administered to 656 students in different sections of the same class during regular virtual classrooms in 2021. The datasets were analyzed and compared using ML.

Entrepreneurial traits existed among students before attending undergraduate entrepreneurship courses. Establishing strong partnerships (0.359), learning (0.347) and people-organizing ability (0.341) were promising correlated entrepreneurial traits. Female students exhibited fewer entrepreneurial traits than male students. The random forest model exhibited 60% accuracy in trait prediction against gradient boosting (58.4%), linear regression (56.8%), ridge (56.7%) and lasso regression (56.0%). Thus, the ML model appeared to be unsuitable to predict entrepreneurial traits. Quality data are important for accurate trait predictions.

Further studies can validate K-nearest neighbors (KNN) and support vector machine (SVM) models against random forest to support the statement that the ML model cannot be used for entrepreneurial trait prediction.

This research is unique because ML models, such as random forest, gradient boosting and lasso regression, are used for entrepreneurial trait prediction by agricultural domain students.

The purpose of this study is to address a critical gap in enterprise resource planning (ERP) implementation process for small and medium-sized enterprises (SMEs) by acknowledging and countering cognitive biases through a cognitive bias awareness matrix model. Cognitive biases such as temporal discounting and optimism bias often skew decision-making, leading SMEs to prioritize short-term benefits over long-term sustainability or underestimate the challenges involved in ERP implementation. These biases can result in costly missteps, underutilizing ERP systems and project failure. This study enhances decision-making processes in ERP adoption by introducing a matrix that allows SMEs to self-assess their level of awareness and proactivity when addressing cognitive biases in decision-making.

The design and methodology of this research involves a structured approach using the problem-intervention-comparison-outcome-context (PICOC) framework to systematically explore the influence of cognitive biases on ERP decision-making in SMEs. The study integrates a comprehensive literature review, empirical data analysis and case studies to develop the Cognitive Bias Awareness Matrix. This matrix enables SMEs to self-assess their susceptibility to biases like temporal discounting and optimism bias, promoting proactive strategies for more informed ERP decision-making. The approach is designed to enhance SMEs’ awareness and management of cognitive biases, aiming to improve ERP implementation success rates and operational efficiency.

The findings underscore the profound impact of cognitive biases and information asymmetry on ERP system selection and implementation in SMEs. Temporal discounting often leads decision-makers to favor immediate cost-saving solutions, potentially resulting in higher long-term expenses due to the lack of scalability. Optimism bias tends to cause underestimating risks and overestimating benefits, leading to insufficient planning and resource allocation. Furthermore, information asymmetry between ERP vendors and SME decision-makers exacerbates these biases, steering choices toward options that may not fully align with the SME’s long-term interests.

The study’s primary limitation is its concentrated focus on temporal discounting and optimism bias, potentially overlooking other cognitive biases that could impact ERP decision-making in SMEs. The PICOC framework, while structuring the research effectively, may restrict the exploration of broader organizational and technological factors influencing ERP success. Future research should expand the range of cognitive biases and explore additional variables within the ERP implementation process. Incorporating a broader array of behavioral economic principles and conducting longitudinal studies could provide a more comprehensive understanding of the challenges and dynamics in ERP adoption and utilization in SMEs.

The practical implications of this study are significant for SMEs implementing ERP systems. By adopting the Cognitive Bias Awareness Matrix, SMEs can identify and mitigate cognitive biases like temporal discounting and optimism bias, leading to more rational and effective decision-making. This tool enables SMEs to shift focus from short-term gains to long-term strategic benefits, improving ERP system selection, implementation and utilization. Regular use of the matrix can help prevent costly implementation errors and enhance operational efficiency. Additionally, training programs designed around the matrix can equip SME personnel with the skills to recognize and address biases, fostering a culture of informed decision-making.

The study underscores significant social implications by enhancing decision-making within SMEs through cognitive bias awareness. By mitigating biases like temporal discounting and optimism bias, SMEs can make more socially responsible decisions, aligning their business practices with long-term sustainability and ethical standards. This shift improves operational outcomes and promotes a culture of accountability and transparency. The widespread adoption of the Cognitive Bias Awareness Matrix can lead to a more ethical business environment, where decisions are made with a deeper understanding of their long-term impacts on employees, customers and the broader community, fostering trust and sustainability in the business ecosystem.

This research introduces the original concept of the Cognitive Bias Awareness Matrix, a novel tool designed specifically for SMEs to evaluate and mitigate cognitive biases in ERP decision-making. This matrix fills a critical gap in the existing literature by providing a structured, actionable framework that effectively empowers SMEs to recognize and address biases such as temporal discounting and optimism bias. Its practical application promises to enhance decision-making processes and increase the success rates of ERP implementations. This contribution is valuable to behavioral economics and information systems, offering a unique approach to integrating cognitive insights into business technology strategies.

Whenever social injustice tackled by social movements receives heightened media attention, charitable crowdfunding platforms offer an opportunity to proactively advocate for equality by donating money to affected people. This research examines how the Black Lives Matter movement and the associated social protest cycle after the death of George Floyd have influenced donation behavior for campaigns with a personal goal and those with a societal goal supporting the black community.

This paper follows a quantitative research approach by applying a quasi-experimental research design on a GoFundMe dataset. In total, 67,905 campaigns and 1,362,499 individual donations were analyzed.

We uncover a rise in donations for campaigns supporting the black community, which lasts substantially longer for campaigns with a societal than with a personal funding goal. Informed by construal level theory, we attribute this heterogeneity to changes in the level of abstractness of the problems that social movements aim to tackle.

This research advances the knowledge of individual donation behavior in charitable crowdfunding. Our results highlight the important role that charitable crowdfunding campaigns play in promoting social justice and anti-discrimination as part of social protest cycles.

The number of university students seeking mental health support is rapidly increasing. To provide additional psychological support to students accessing well-being services, this study aimed to pilot a mental health Web application (app) called Orpheus®.

Guided by student consultations, a multi-methods approach was adopted, including an examination of in-app data, chart reviews of routinely collected student information and interviews with mental health practitioners. Usage data were analysed descriptively. Changes in mental health outcomes were examined using means, standard deviations and reliable change indices for anxiety and depression scores. Inductive and deductive thematic analysis was conducted on qualitative data from staff interviews and student feedback.

A total of 26 students registered an account with 39 completed app visits. On 37 of the 39 (94.9%) occasions, students reported reductions in the intensity of unwanted negative emotions. Statistically significant reductions in the average pooled anxiety and depression scores were observed. Of the 15 students who completed pre- and post-routine outcome measures, between 20% and 60% showed reliable and meaningful symptom improvements. Students reported that the app was helpful and easy to set up and use, with no adverse events. Practitioners highlighted barriers and facilitators related to the technology features, situational contexts and individual differences.

Integrating Orpheus in real-world settings resulted in promising implementation processes, potential for future uptake and positive outcomes. However, ongoing research, staff training and app testing are needed to further improve the implementation processes for digital mental health interventions.

Given that previous research examining students’ perceptions of the legitimacy of the campus police (CP) has largely taken place on a single campus and utilized quantitative methods, the purpose of this qualitative study was to understand how community context shapes students’ interactions with and perceptions of the CP in two distinct campus settings.

This study draws on in-depth interviews with 70 undergraduates, including 36 attending a university in a relatively safe, affluent suburban area and 34 attending a university in a high crime, disadvantaged urban area.

Although participants at both universities appreciated the presence and visibility of the CP, some Suburban U students felt that rather than being protected from outside threats, they were instead policed for engaging in behaviors that they deemed typical of the college experience. Alternatively, Urban U students expressed the opposite of their CP, providing evidence that brief and positive officer-initiated encounters can enhance perceptions of legitimacy, particularly in settings characterized by higher crime rates.

These findings have implications for how the CP can build trust and legitimacy among students to enhance campus safety and foster future compliance and mutual respect.

This study extends the findings of previous research in two important ways. First, it compares two starkly different campus contexts to understand how community setting and local crime rates influence students’ perceptions of the CP, and second, it utilizes both a comparative and qualitative research design.

Effective information security management (ISM) contributes to building a healthy organizational digital ecology. However, few studies have built an analysis framework for critical influencing factors to discuss the combined influence mechanism of multiple factors on ISM performance (ISMP). This study aims to explore the critical success factors and understand how these factors contribute to ISMP.

This study used a mixed-method approach to achieve this study’s research goals. In Study 1, the authors conducted a qualitative analysis to take a series of International Organization for Standardization/International Electrotechnical Commission standard documents as the basis to refine the critical factors that may influence organizations’ ISMP. In Study 2, the authors built a research model based on the organizational control perspective and used the survey-based partial least squares-based structural equation modeling (PLS-SEM) approach to understand the relationships between these factors in promoting ISMP. In Study 3, the authors used the fuzzy set qualitative comparative analysis (fsQCA) method to empirically analyze the complex mechanisms of how the combinations of the factors affect ISMP.

The following three research findings are obtained. First, based on the text-based qualitative analysis, the authors refined the critical success factors that may increase ISMP, including information security policies (ISP), top management support (TMS), alignment (ALI), information security risk assessment (IRA), information security awareness (ISA) and information security culture (ISC). Second, the PLS-SEM testing results confirmed TMS is the antecedent variable motivating organization’s formation (ISP) and information control (ISC) approaches; these two types of organization control approaches increase IRA, ISA and ALI and then promote ISMP directly and indirectly. Third, the fsQCA testing results found two configurations that can achieve high ISMP and one driving path that leads to non-high ISMP.

This study extends knowledge by exploring configuration factors to improve or impede the performances of organizations’ ISM. To the best of the authors’ knowledge, this study is one of the first to explore the use of the fsQCA approach in information security studies, and the results not only revealed causal associations between single factors but also highlighted the critical role of configuration factors in developing organizational ISMP. This study calls attention to information security managers of an organization should highlight the combined effect between the factors and reasonably allocate organizational resources to achieve high ISMP.

Educational institutions are investing heavily in the internationalization of their campuses to attract global talent. Yet, highly skilled immigrants face persistent labor market challenges. We investigate how immigrant academics experience and mitigate their double precarity (migrant and academic) as they seek employment in higher education in Canada.

We take a phenomenological approach and draw on reflective interviews with nine immigrant academics, encouraging participants to elaborate on symbols and metaphors to describe their experiences.

We found that immigrant academics constitute a unique highly skilled precariat: a group of professionals with strong professional identities and attachments who face the dilemma of securing highly precarious employment (temporary, part-time and insecure) in a new academic environment or forgoing their professional attachment to seek stable employment in an alternate occupational sector. Long-term, stable and commensurate employment in Canadian higher education is out of reach due to credentialism. Those who stay the course risk deepening their precarity through multiple temporary engagements. Purposeful deskilling toward more stable employment that is disconnected from their previous educational and career accomplishments is a costly alternative in a situation of limited information and high uncertainty.

We bring into the conversation discussions of migrant precarity and academic precarity and draw on immigrant academics’ unique experiences and strategies to understand how this double precarization shapes their professional identities, mobility and work integration in Canadian higher education.