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This paper aims to close the gap between the generic concept of knowledge activities (KAs) and implementing them in the context of software engineering organisations concentrating on the non-technical aspects, such as team organisation and practices.

This qualitative research used a questionnaire with practitioners such as software developers and team leads who were asked to provide feedback on a set of team practices and measures typically used in software engineering projects and assess their relation to the activities of acquiring, codifying, storing, maintaining, transferring and creating knowledge. The obtained results were analysed using frequency analysis and further descriptive statistics yielding a matrix linking the investigated team practices and measures to KAs.

Team practices and measures commonly applied in software engineering can be facilitated to trigger particular KAs. While most of these team practices and measures originate from agile methods, they are not restricted to these. A purposeful composition can help in assembling a balanced set of KAs aimed at fostering given knowledge goals in software engineering organisations.

By bridging the communication and terminology gap between knowledge management research and software engineering practitioners, this work lays the foundation for assessing software teams’ knowledge profiles more easily and creating prerequisites for implementing knowledge management by facilitating common practices and measures often already part of their daily work. Hence, overhead can be avoided when implementing knowledge management.

To the best of the authors’ knowledge, this is the first study investigating application and relevance of KAs in the software industry by linking them to practices and measures well-accepted in software engineering, thus providing the necessary vocabulary for the implementation of knowledge management in software development teams.

Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

The substantial expansion of technology and the efficacy of digital platforms in reaching young audiences have led to enhanced targeting and customization of promotional communications. Notwithstanding the expansion and efficacy of contemporary advertising platforms, scholarly attention has not kept pace with this domain of inquiry. This study aims to assess the antecedents of Google Shopping Ads (GSA) on intention to purchase behavior among the Generation Y and Z cohorts.

The current study used a quantitative approach and snowball sampling technique to gather primary data via a questionnaire and Google Forms, which resulted in the collection of 5,808 questionnaires among the cohort members. A principal component analysis and multigroup confirmatory multigroup structural equation modeling (between Generation Y and Z) were used to assess the research data and model.

The results show positive trust and perceived value associations with intention to purchase, particularly among Generation Y and Z consumers. The findings also show negative irritation, product risk and time risk associations with intention to purchase, especially among the Generation Y cohort, which indicates that young consumers generally do not observe perceived risk due to the usage of GSA.

GSA will continue to grow and become an increasingly important integrated marketing communications tool as the digital landscape develops. It can be concluded that young consumers show a high degree of perceived value and low levels of perceived risk due to the use of GSA. This study, therefore, promotes improved understanding among academics, marketers and businesses of search engine advertising among young cohorts of consumers (Generation Y and Z) in a developing country context.

Despite the important role of entrepreneurial implementation intentions for closing the intention-behavior gap, empirical evidence on their drivers and mechanisms is scant and inconclusive. In the case of college students’ technology-driven entrepreneurship, the objective of the present study is to examine whether implementation intentions are contingent on the university environment in which the progression from entrepreneurial intentions to subsequent actions unfolds. The sample for this study is composed of 299 Bulgarian STEM students, who reported technology-based entrepreneurial intentions. A binary logistic regression is applied to examine four specific mechanisms that facilitate or impede the students’ actual implementation intentions. Findings suggest that students enrolled in universities that provide greater concept development support are more likely to have formed specific implementation intentions, while students in more research-intensive universities are less likely to do so. Practitioner implications and recommendations for future research are provided.

Though prosocial attributes are linked to positive outcomes among justice-involved adolescents and are a mainstay of numerous interventions, few measures have been specifically designed to evaluate prosocial functioning within this population. Although multiple instruments measuring aspects of prosocial behavior exist, these instruments were not designed to measure prosocial behaviors among youth in juvenile justice settings. This study aims to provide a preliminary validation of a new measure of prosocial attributes (the Prosocial Status Inventory – PSI), which was designed to comprehensively evaluate in greater depth the prosocial functioning of urban, justice-involved youth.

Youth (n = 51) were recruited as part of a larger study and were participants in a community-based mentoring program in a large, urban county in the Southern USA. Youth completed the PSI at baseline prior to their participation in the community-based mentoring program. The authors obtained follow-up data on recidivism from the county juvenile justice department.

PSI scores were positively related to a lower rate of recidivism and a decrease in offending frequency over a 12-month follow-up period.

The current findings complement previous work, suggesting that prosocial attributes are measurable and related to important outcomes among justice-involved youth and support the utility of strengths-based treatment approaches. Moreover, it provides preliminary evidence of the utility of a new self-report measure to assess these traits within a juvenile justice population.

This chapter begins by covering four parenting styles and their effects on college students’ wellbeing. Next, the effects of parents’ divorce and parental death on college students are explored. In addition to relationships with their parents, the chapter also examines college students’ relationships with their friends. Friendships based on utility or pleasure are contrasted with those based on virtue. Next, the chapter explores the effects of college students’ friendships on various aspects of their academic performance (e.g., dropping out of school) and social lives (e.g., drinking behaviors). The role of social media in college students’ relationships is considered as well. The chapter concludes with a focus on college students’ romantic relationships.

This paper aims to improve the delimitation of plant growth stages in the context of weather index insurance design. We propose a data-driven phase division that minimizes estimation errors in the weather-yield relationship and investigate whether it can substitute an expert-based determination of plant growth phases. We combine this procedure with various statistical and machine learning estimation methods and compare their performance.

Using the example of winter barley, we divide the complete growth cycle into four sub-phases based on phenology reports and expert instructions and evaluate all combinations of start and end points of the various growth stages by their estimation errors of the respective yield models. Some of the most commonly used statistical and machine learning methods are employed to model the weather-yield relationship with each selected method we applied.

Our results confirm that the fit of crop-yield models can be improved by disaggregation of the vegetation period. Moreover, we find that the data-driven approach leads to similar division points as the expert-based approach. Regarding the statistical model, in terms of yield model prediction accuracy, Support Vector Machine ranks first and Polynomial Regression last; however, the performance across different methods exhibits only minor differences.

This research addresses the challenge of separating plant growth stages when phenology information is unavailable. Moreover, it evaluates the performance of statistical and machine learning methods in the context of crop yield prediction. The suggested phase-division in conjunction with advanced statistical methods offers promising avenues for improving weather index insurance design.

This review identifies low self-concept clarity (SCC) as a source of consumer vulnerabilities and explains how the uncertainty associated with low SCC leads to processes that result in materialistic behaviors and overspending, product dissatisfaction, and potential self-harm. Processes include uncertainty reduction efforts through symbolic self-completion and social comparison, responses to everyday self-concept threats that result in feelings of deficiency and reduced consumption constraints, and susceptibility to interpersonal and marketer influences. In addition, the negative association between SCC and materialism is explained, risk factors for low SCC are described, and the need for research to help low SCC consumers deal with their vulnerabilities is explored.