Zoekresultaten

The Effects of Internationalization on Innovation: Firm-Level Evidence for Transition Economies

It is well-documented that international enterprises are more productive. Only few studies have explored the effect of internationalization on productivity and innovation at the firm-level. Using propensity score matching we analyze the causal effects of internationalization on innovation in 10 transition economies. We distinguish between three types of internationalization: exporting, FDI, and international outsourcing. We find that internationalization causes higher levels of innovation. More specifically, we show that (i) exporting results in more R&D, higher sales from product innovation, and an increase in the number of international patents (ii) outward FDI increases R&D and international patents (iii) international outsourcing leads to higher sales from product innovation. The paper provides empirical support to the theoretical literature on heterogeneous firms in international trade that argues that middle income countries gain from trade liberalization through increases in firm productivity and innovative capabilities.

Relaxing or exciting? Mobile emotion recording in natural settings.

Transfering Targeted Maximum Likelihood Estimation for Causal Inference into Sports Science

Although causal inference has shown great value in estimating effect sizes in, for instance, physics, medical studies, and economics, it is rarely used in sports science. Targeted Maximum Likelihood Estimation (TMLE) is a modern method for performing causal inference. TMLE is forgiving in the misspecification of the causal model and improves the estimation of effect sizes using machine-learning methods. We demonstrate the advantage of TMLE in sports science by comparing the calculated effect size with a Generalized Linear Model (GLM). In this study, we introduce TMLE and provide a roadmap for making causal inference and apply the roadmap along with the methods mentioned above in a simulation study and case study investigating the influence of substitutions on the physical performance of the entire soccer team (i.e., the effect size of substitutions on the total physical performance). We construct a causal model, a misspecified causal model, a simulation dataset, and an observed tracking dataset of individual players from 302 elite soccer matches. The simulation dataset results show that TMLE outperforms GLM in estimating the effect size of the substitutions on the total physical performance. Furthermore, TMLE is most robust against model misspecification in both the simulation and the tracking dataset. However, independent of the method used in the tracking dataset, it was found that substitutes increase the physical performance of the entire soccer team.

The Personal Touch in Customer Contact: The Influence of a Personal Approach on Customer Relationships and Customer Experience

Customer-supplier relationships are becoming more digital. However, a personal approach still seems to be a key success factor in the service journey, creating an optimal customer experience. In this research, we investigated the effects of a personal communication approach on customer experience and customer relationship. The personal touch was operationalised in two studies focusing on written forms of communication (Study 1) and spoken forms of communication (Study 2) amongst customers of an energy company. Both studies show that a personal tone of voice in customer contact results in a more positive customer experience (in terms of consumption emotions, customer satisfaction and recommendation intention). However, it does not impact the long term relationships between service provider and customers. Customers do not adjust previously built relationship norms when they are approached in a more or less personal way, as long as the chosen approach does not violate relationship norms. The research is relevant for organisations interested in the effects of a more personal approach in customer contact. The paper combines existing theories on customer experience and customer communication with the existing theories on relational models.

Human-centered evaluation of explainable AI applications

Explainable Artificial Intelligence (XAI) aims to provide insights into the inner workings and the outputs of AI systems. Recently, there’s been growing recognition that explainability is inherently human-centric, tied to how people perceive explanations. Despite this, there is no consensus in the research community on whether user evaluation is crucial in XAI, and if so, what exactly needs to be evaluated and how. This systematic literature review addresses this gap by providing a detailed overview of the current state of affairs in human-centered XAI evaluation. We reviewed 73 papers across various domains where XAI was evaluated with users. These studies assessed what makes an explanation “good” from a user’s perspective, i.e., what makes an explanation meaningful to a user of an AI system. We identified 30 components of meaningful explanations that were evaluated in the reviewed papers and categorized them into a taxonomy of human-centered XAI evaluation, based on: (a) the contextualized quality of the explanation, (b) the contribution of the explanation to human-AI interaction, and (c) the contribution of the explanation to human- AI performance. Our analysis also revealed a lack of standardization in the methodologies applied in XAI user studies, with only 19 of the 73 papers applying an evaluation framework used by at least one other study in the sample. These inconsistencies hinder cross-study comparisons and broader insights. Our findings contribute to understanding what makes explanations meaningful to users and how to measure this, guiding the XAI community toward a more unified approach in human-centered explainability.

Towards a sustainable model of innovative work behaviors’ enhancement

In this mixed methods study, a moderated mediation model predicting effects of leader-member exchange (LMX) and organizational citizenship behaviors (OCB) on innovative work behaviors, with employability as a mediator, has been tested. Multi-source data from 487 pairs of employees and supervisors working in 151 small and medium-sized enterprises (SMEs) supported our hypothesized model. The results of structural equation modelling provide support for our model. In particular, the benefits of close relationships and high-quality exchanges between employee and supervisor (LMX), and fostering individual development as a result of employees’ OCB have an indirect effect on innovative work behaviors through positive effects on workers’ employability. Innovative work behaviors depend on employees’ knowledge, skills, and expertise. In other words, enhancing workers’ employability nurtures innovative work behaviors. In addition, we found a moderation effect of organizational politics on the relationship between employability and innovative work behaviors. Secondly, qualitative methods focusing on experiences of the antecedents and outcomes of employability were used to complement our quantitative results. All in all, this study has important consequences for managerial strategies and practices in SMEs and call for an awareness of the dysfunctional effect of perceived organizational politics.