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This study aims to investigate a mediation model in which generative learning positively affects marketing innovation and both organizational control and relationship openness mediate the relationship between learning intent and generative learning of international joint ventures (IJVs) in emerging markets. We also decipher the degree of necessity of these factors for generative learning and of generative learning for marketing innovation.

A sample of 181 marketing managers of IJVs in Vietnam, an emerging market, was surveyed to collect data. Partial least squares structural equation modeling (PLS-SEM) was employed to test the net effect, and necessary condition analysis (NCA) was used to decipher the degree of necessity.

The PLS-SEM results demonstrate that the effect of learning intent on generative learning is fully mediated by organizational control and relationship openness, which in turn leads to marketing innovation. The NCA findings reveal that all three factors, namely learning intent, organizational control and relationship openness, serve as necessary conditions for generative learning. However, generative learning does not play the role of a necessary condition for marketing innovation.

The study findings suggest that IJVs in emerging markets should pay attention not only to the net effects of those factors but also to their degrees of necessity for generative learning in order to achieve marketing innovation.

The study contributes to the literature by confirming the mediating roles of organizational control and relationship openness in the relationship between learning intent and generative learning. Furthermore, it is among the first to decipher the degrees of necessity of these factors for generative learning and of generative learning for the marketing innovation of IJVs in emerging markets.

The present paper aims to focus on the role which German controllers play so far in the process of sustainable transformation in for-profit organizations, the current obstacles to a wider engagement here and ways to overcome these obstacles.

The analysis combines two qualitative study designs. Empirical data is generated via a job advertisement analysis and an explorative survey with 107 subjects from management accounting/controlling and sustainability management. The generated data is interpreted against the background of the theory of institutional logics and Abbott’s (1988) theory of professional jurisdiction.

We find that controllers are in a state of tension. On the one hand, the pressure to integrate sustainability into companies is increasing. On the other hand, they seem to be rather reluctant to get involved. The institutional logics that shape their profession play an important role here, as does an unclear relationship with the sustainability department, which has its own claims here. Based on these observations, we identify the core obstacles to the transformation of the controllers’ profession and discuss solutions which can guide the transformation of this profession.

The present paper provides insights from a unique combination of different quantitative study designs and different perspectives on the possible role that controllers can play in advancing sustainable transformation in companies.

This study examines how assurors make sense of sustainability assurance (SA) work and how interactions with assurance team members and clients shape assurors’ sensemaking and their actual SA work.

To obtain detailed accounts of how SA work occurs on the ground, this study explores three SA engagements by interviewing the main actors involved, both at the client firms and at their Big Four assurance providers.

Individual assurors’ (i.e. partners and other team members) sensemaking of SA work results in the crafting of their logics of action (LoAs), that is, their meanings about the objectives of SA work and how to conduct it. Without organizational socialization, team members may not arrive at shared meanings and deviate from the team-wide assurance approach. To fulfill their objectives for SA work, assurors may engage in socialization with clients or assume a temporary role. Yet, the role negotiations taking place in the shadows of the scope negotiations determine their default role during the engagement.

Two options are available to help SA statement users gauge the relevance of SA work: either displaying the SA work performed or making it more uniform.

This study theoretically grounds how assurors make sense of SA work and documents how (the lack of) professional socialization, organizational socialization and socialization of frequent interaction partners at the client shape actual SA work. Thereby, it unravels the SA work concealed behind SA statements.

Ensuring the safety of structures is important. However, when a structure possesses both an implicit performance function and an extremely small failure probability, traditional methods struggle to conduct a reliability analysis. Therefore, this paper proposes a reliability analysis method aimed at enhancing the efficiency of rare event analysis, using the widely recognized Relevant Vector Machine (RVM).

Drawing from the principles of importance sampling (IS), this paper employs Harris Hawks Optimization (HHO) to ascertain the optimal design point. This approach not only guarantees precision but also facilitates the RVM in approximating the limit state surface. When the U learning function, designed for Kriging, is applied to RVM, it results in sample clustering in the design of experiment (DoE). Therefore, this paper proposes a FU learning function, which is more suitable for RVM.

Three numerical examples and two engineering problem demonstrate the effectiveness of the proposed method.

By employing the HHO algorithm, this paper innovatively applies RVM in IS reliability analysis, proposing a novel method termed RVM-HIS. The RVM-HIS demonstrates exceptional computational efficiency, making it eminently suitable for rare events reliability analysis with implicit performance function. Moreover, the computational efficiency of RVM-HIS has been significantly enhanced through the improvement of the U learning function.