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This paper aims to capture the challenges faced by large industrial firms in implementing employee green behaviour. It uses the gamification-based Octalysis framework for identifying motivational drives and entwins it to self-efficacy theory seeking to motivate and engage the employees through game techniques.

This paper uses qualitative approach where semi-structured interviews were conducted through snowball sampling technique with managers in senior positions in power sector holding significant decision-making authority. The interviews were transcribed and were analysed thematically.

This study offers compelling evidence that industrial firms are grappling to inculcate pro-environmental behaviour largely losing on incentivising motivation. Gamification can provide an enjoyable framework balancing intrinsic and extrinsic motivational drives.

This study offers a framework applicable to organisations across sectors, addressing challenges in implementing green behaviour by leveraging four phases of game mechanics. It tackles issues related to motivation and demand for incentives by striking a balance between intrinsic and extrinsic motivations.

This research stands out by incorporating game mechanics, specifically designed through Octalysis, to boost self-efficacy and encourage green behaviour among employees. Furthermore, it is in harmony with Sustainable Development Goals and circular principles.

This paper seeks to understand users' perceptions of their experiences in mobile applications (apps) with gamified loyalty programs (GLPs) that use rewards as the primary engagement vehicle. The research focuses particularly on the motivations to further interact with GLPs and the motivational changes occurring after successive interactions.

The authors conducted weekly open qualitative interviews over a month (four rounds of interviews) with five Portuguese active users of the mobile app Yorn Shake It, from Vodafone, which is a relevant case study to illustrate GLPs in mobile apps.

Participants' motivations to interact with the mentioned GLP are shaped by the reward incentive and users' perceptions of the gamified interactive experience. Motivational changes occur regardless of the presence of external contingencies and depend on contextual changes or perceived results of the gamified experience. This means that rewards also satisfy intrinsic needs, but users may remain connected to the system as long as fun experiences are provided without exhausting perceptions. Also, motivation may turn to reward contingencies when the challenge becomes boring.

This is the first qualitative study explaining the perceptions of gamified experiences after continued participation, extending knowledge about the importance of a fair balance between the value and achievement of rewards and the entertainment of the challenge provided after continued exposure. The findings provide insights to GLP marketing managers and developers to better engage target audiences according to their needs and past experience, creating levels of challenges and fair rewards to maintain motivation and prevent abandonment after continued exposure.

This study aims to explore the potential benefits of integrating Artificial Intelligence (AI) with Social Emotional Learning (SEL) in educational settings.

A systematic review of emerging AI technologies such as virtual reality, chatbots, sentiment analysis tools, gamification and wearable devices is conducted to assess their applicability in enhancing SEL.

AI technologies present opportunities for personalized support, increased engagement, empathy development and promotion of well-being within SEL frameworks.

Future research should focus on addressing ethical concerns, fostering interdisciplinary collaborations, conducting longitudinal studies, promoting cultural sensitivity and developing robust ecosystems for AI in SEL.

This study contributes by outlining pathways for leveraging AI to create inclusive and supportive learning environments that nurture students' socio-emotional competencies, preparing them for success in a globally connected world.

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.

Preservice teacher (PST) professional development programs are crucial for cultivating high-quality STEAM teachers of the future, significantly impacting the quality of regional STEAM education. The Guangdong-Hong Kong-Macao Greater Bay Area, as a region of cross-border cooperation, integrates the resources and advantages of Guangdong, Hong Kong, and Macao, possessing rich cultural heritage and innovative capabilities. Transdisciplinary Education for Cultural Inheritance (C-STEAM) is an effective approach to promoting educational collaboration within the Greater Bay Area, facilitating the integration of both technological and humanities education. This study aims to develop a Technology-Enabled University-School-Enterprise (T-USE) collaborative education model and implement it in the Greater Bay Area, to explore its role as a support mechanism in professional development and its impact on C-STEAM PSTs' professional capital.

Adopting a qualitative methodology, the study interviewed PSTs who participated in a C-STEAM teacher education course under the T-USE model. Thematic coding is used to analyze their knowledge acquisition, interaction benefits with community members, and autonomous thinking and decision-making in theoretical learning and teaching practice.

The findings show that the T-USE model significantly enhanced the PSTs' human capital, including teaching beliefs, knowledge, and skills. In terms of social capital, PSTs benefited from collaboration with PST groups, university teaching teams, in-service teachers, and enterprises, though challenges such as varying levels of expertise among in-service teachers and occasional technical instability emerged. For decisional capital, the T-USE model provided opportunities for autonomous thinking and promoted teaching judgment skills through real teaching challenges and scenarios. Reflective practice activities also supported PSTs' professional growth.

This study reveals the effectiveness and internal mechanism of the T-USE model in C-STEAM PST training, offering significant theoretical and practical references for future PST education.

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.

This paper intervenes in the consequences of a myth propagated in academic discourse about the dancesport world, according to which half of the men in Latin dancesport are gay. I challenge two assumptions that surround this myth: that cisgender gay men do not contribute to the reification of the heteronormative gender binary, and that the dancesport scene is inclusive of gay people. These assumptions are based on a blatant lack of understanding of the position of gay men within the dancesport world – that is, the ways in which subjects are constituted through the effects of power.

This work is based on empirical research I conducted in the dancesport community, including ethnographic and autoethnographic fieldwork, extant documents (e.g. books, blogs, Judging Regulations) and interviews with experts and participants of the dancesport scene (2021/2022). To analyse the data, I relied on the principles of dispositive analysis, grounded theory and dance analysis.

I show that gay dancers have turned to assimilation as their only available strategy. I discuss the negative consequences of assimilation as a political strategy and how it impacted queer dancers – between invisibilisation, residual shame and a failure to challenge the heteronormative gender binary. This led gay dancers to rationalise and perpetrate harm based on the systems of oppression they had internalised.

I conclude the paper by highlighting a way beyond assimilation for queer dancers.

This paper addresses a critical gap in research on LGBT + inclusion in dancesport.

The authors investigate how foreign investment in securities market informs about the future firm performance in emerging markets.

The authors define the independent variable abnormal foreign investment (AFI) as the residuals of the foreign ownership equation. The authors regress foreign ownership on its first lag and factors and define the residuals as the AFI. The AFI is the over- or under-investment reflecting foreign conscious (clear-purpose) investment, thus better indicating how foreign investment affects firm performance. The dependent variable is Tobin’s q (Q), which represents the firm performance. Then, the authors regress the Tobin’s q next quarters (Qt + k) on the AFI current quarter (AFIt). The authors use a two-step generalized method of moments (GMM) and check endogeneity with the D-GMM model for the regression.

The results show that the current AFI is positively correlated with the firm performance in each of the next four quarters (the following one year). This positive relationship is pronounced for large firms, firms with no large foreign investors, liquid firms and firms listed in the active market. The results suggest that foreign investment might choose well-productive firms already. Also, the current AFI is significantly positively correlated with stock returns in each of the next three quarters. These results suggest that the AFI is informative up to one-year period.

The results suggest that foreign investors (most of them are small) in the Vietnamese market might choose well-productive firms already. However, if the large investors have long-term investment in tangible, intangible, human capital and so on, and lead to a significant increase in firms’ performance is still the limitation of this paper.

The results of this paper may guide investors whose portfolios are composed of stocks with foreign investment.

This paper adds to the literature to enrich the conclusion of a positive relationship between foreign ownership and firm performance.

The United Nations Convention on the Rights of Persons with Disabilities (2006) recognizes the right of disabled people to access work. Against this legislative backdrop, this study explores the strengths and weaknesses of the Italian system of targeted placement for disabled people, based on Law 68/1999, which delegates to regional authorities the management of the labor market. The examination centers on the perspective of companies, the primary stakeholders in the inclusion of persons with disabilities within organizational structures.

The article discusses the results of focus groups conducted with 28 managers of large, medium and small enterprises in Lombardy (Italy). Qualitative analysis was employed, and the results were structured using a simplified strengths, weaknesses, opportunities and threats (SWOT) analysis, incorporating practical recommendations.

The analysis leads to practical suggestions to improve the entire targeted placement process at the regional level, from selection and accompaniment to evaluation, such as improving the networking of local stakeholders who deal with the inclusion of disabled people, homogeneity of the procedures in different regions, making all employees aware of diversity management, etc. The territorial network and the welfare environment are particularly important in achieving a successful targeted placement and to promote an inclusive corporate culture.

This study is not representative of Italy as a whole, as it remains a qualitative investigation focused on a single region.

This contribution accomplishes an in-depth study of the law of labor inclusion of people with disabilities observed from the point of view of companies, which are still usually reluctant to integrate people with disabilities into their organizations or encounter difficulties in doing so.

How does the interaction between time and knowledge affect the evolution of organizations? Past research in organizational evolution has mostly investigated time and knowledge as two separate variables. In contrast, theoretical perspectives integrating these variables are still seemingly scant. The authors believe that filling this literature gap needs attention. Thus, this study aims to contribute by developing a conceptual framework.

This is a conceptual study. The framework is centred on the concept of “co-evolutionary time”, which the authors explain through a business example from the tourism industry. Supported by a narrative-based style, from a methodological point of view the framework is featured by the attempt to synthesize specific, extant literature into new theoretical development.

As its main theoretical contribution, the co-evolutionary time suggests how firms can adapt in a way that, from an evolutionary perspective, proves fitting both in terms of contents and methods, thus opening possibilities for new long-term social construction and reconstruction. As its main practical contribution, co-evolutionary time can constitute not only a temporary source of organizational success and competitive advantage but also an agent of enduring change and long-term business survival.

As its main novelty, the framework is developed through merging two literature streams. In particular, the authors first consider the literature about time, with a focus on its objective and subjective dimensions. The authors then consider the literature about organizational evolution, with a focus on the co-evolutionary nature of the firm/environment relationship.