Purpose: The purpose of this study is to explore the pivotal role that collective teaching plays in knowledge transfer between new product development teams. This study develops a theoretical model of collective teaching, where team intelligence is its consequence and learning orientation cognitive skills are moderators.Design/methodology/approach: Based on a questionnaire survey of 156 pairs of new product development project teams of information technology firms, the authors used partial least squares to test the study’s hypotheses.Findings: The findings reveal that the use of collective teaching is positively related to team intelligence of recipient teams. In addition, T-shaped skills of source teams exert positive moderating influence on this relationship and so does a learning orientation of recipient teams.Research limitations/implications: First, the sample firms used in the study are from the IT industry, which is characterized by extremely short product life cycles, thereby limiting the generalizability of the study’s findings. Second, the authors did not examine whether the effect of T-shaped skills is different at various NPD stages; the contributions of each functional expertise may vary depending on the NPD stage (e.g. the idea generation or pre-launch stage). Third, the use of cross-sectional design precludes a causal inference. The role of focal constructs and moderators and their consequent effects would benefit from more stringent, longitudinal research. Finally, the authors controlled for only a limited set of factors of team intelligence because other potential antecedents of this variable still await identification by future studies.Practical implications: This study suggests that the implementation of collective teaching can enhance the capacity of a project team as a whole to manage and innovate information, namely, team intelligence. The study’s findings also suggest that the management must recognize the significance of teams’ learning orientation and thereby proactively develop teams’ learning culture by redesigning work, reward systems or performance evaluation to promote learning. Additionally, it is prudent for managers to reconsider their recruitment criteria to incorporate T-shaped skills.Originality/value: This study represents the first step in developing an empirically grounded framework linking collective teaching with team intelligence. Additionally, the authors confirm that team intelligence is a four-dimensional construct.

Drawing on motivation and behavioural theories, this study develops a three-way interactive model to examine the antecedents and moderators regarding chief executive officer (CEO) risk-taking. Our sample consists of 136 observations of Taiwanese firms from primary and secondary sources. Using hierarchical regression analysis, we confirm that the relationship between CEO shareholdings and CEO risk-taking is positively moderated by approach orientation but negatively moderated by avoidance orientation. Furthermore, firm complexity strengthens the positive moderating effect of approach orientation.

In recent years, users have increasingly focused on the privacy of social networking sites (SNS); users have reduced their self-disclosure intention. To attract users, SNS rely on active platforms that collect accurate user information, even though that information is supposed to be private. SNS marketers must understand the key elements for sustainable operation. This study aims to understand the influence of motivation (extrinsic and intrinsic) and self-disclosure on SNS through soft computing theories. First, based on a survey of 1108 users of SNS, this study used a dominance-based rough set approach to determine decision rules for self-disclosure intention on SNS. In addition, based on 11 social networking industry experts’ perspectives, this study validated the influence between the motivation attributes by using Decision-Making Trial and Evaluation Laboratory (DEMATEL). In this paper, the decision rules of users’ self-disclosure preference are presented, and the influences between motivation attributes are graphically depicted as a flow network graph. These findings can assist in addressing real-world decision problems, and can aid SNS marketers in anticipating, evaluating, and acting in accord with the self-disclosure motivations of SNS users. In this paper, practical and research implications are offered.

Higher education institutions are expected to cultivate graduates capable of addressing sustainability challenges through innovation, collaboration, and digital competence. However, many business programs struggle to integrate experiential authenticity, intelligent technologies, and collaborative learning into coherent instructional models, limiting students’ intrinsic motivation and sustainability-oriented competence development. This study aims to examine how experiential learning, artificial intelligence-assisted collaborative learning, and team-based learning operate within the Conceive–Design–Implement–Operate instructional framework to influence learning interest and learning satisfaction in a sustainability-oriented business course. Survey data from 217 undergraduate students were analyzed using confirmatory factor analysis, structural equation modeling, and moderated regression analysis. The results indicate that both experiential and AI-supported collaborative learning positively enhance students’ learning interest, which partially mediates their effects on learning satisfaction. Team-based learning strengthens the experiential pathway but does not significantly moderate the AI-assisted pathway. These findings clarify differentiated motivational mechanisms within structured instructional systems and provide theoretical support for designing digitally enhanced sustainability education.

The European Union Carbon Border Adjustment Mechanism (CBAM) and volatile electricity markets create linked operating and investment risks for energy-intensive multinational firms. This study develops a six-dimensional stochastic dynamic programming model with dual real options for plant entry, green transformation, production allocation, and abandonment. Electricity prices follow a mean-reverting jump process, carbon prices follow Geometric Brownian Motion, exchange rates follow a mean-reverting diffusion, and financing costs are represented by a carbon-adjusted Cox–Ingersoll–Ross process. The numerical analysis combines 2026 public market benchmarks with scenario inputs identified as model assumptions and solves the allocation problem using Monte Carlo paths and Sequential Least Squares Programming. Under the stated calibration, higher energy and financing uncertainty widens the entry abandonment region, while green capital investment and looser capital constraints narrow it. Production also shifts across plants when relative marginal costs change. These results are model-based and apply to the representative Texas–EU setting rather than to observed firm behavior.