Journal of Engineering and Technology Management最新文献

Accelerating new product development (NPD) to gain a first-mover advantage is crucial in today's competitive business landscape. This paper aims to examine the effective power structure in NPD projects through the lens of two divergent theoretical perspectives: Information Processing Theory (IPT) and Resource Dependence Theory (RDT). While IPT suggests the benefit of a balanced decision-making power among various subunits, RDT advocates for a concentrated decision power for faster consensus and efficiency. Our research specifically investigates whether a balanced or imbalanced R&D/marketing power structure is more conducive to accelerating NPD time. This focus stems from the recognition that the interplay between R&D and marketing is critical in shaping NPD outcomes, though we acknowledge the broader context of multifunctional collaboration in NPD, including aspects like manufacturing. The paper tests two competing hypotheses: one advocating a balanced R&D/marketing power structure as per IPT, and the other supporting a dominant R&D/marketing power structure in line with RDT. We surveyed project leaders, R&D, and marketing personnel from 126 NPD projects across 87 Taiwanese firms. The empirical results significantly endorse the balanced R&D/marketing power structure hypothesis, suggesting that a harmonious collaboration between these two key subunits can expedite NPD processes. Our findings contribute to both academic research and managerial practice by offering nuanced insights into optimizing power structures within NPD teams, particularly highlighting the efficacy of a balanced R&D/marketing approach in the context of the prevalent theories.

Progress in automation has resulted in a growing number of autonomous vehicles (AVs). However, demographic and social differences behind the acceptance of AV technology are an emerging topic in the East-Central European region. These countries (e.g., Poland, Slovakia, Romania) move on a similar technological development path, and the social and economic conditions are alike; thus, Hungary represents this region well. This study contributes to fill this niche. We used quantitative research methods (factor analysis, Kruskal-Wallis H test, Pearson-correlation) to analyze with a snowball (non-probability) sampling. The Hungarian respondents (N=949) selected in the sample were interviewed through a face-to-face and online quantitative questionnaire. The results show that gender and age influence mostly the acceptance; residence and occupation have only partial influence. The propensity to take risks is significantly differentiated in almost all demographic segments. The results facilitate differentiation of users based on their demographic characteristics in AV adoption. Furthermore, the integration of risk propensity into the analysis helps to identify which potential user groups are more likely to overcome any fears of novelty or which clusters are more likely to adopt the current framework of safe transport without driver control. The outcomes are of interest to engineers, manufacturers and policy-makers who can adapt their products, services and taking measures to meet the mobility needs of potential users and introduce effective incentives to increase public acceptance of AVs.

Due to rapid digital advancements, established firms are actively pursuing business model innovation to seize emerging opportunities. By effectively managing digital technologies and related resources, digital capabilities play a crucial role in enabling such innovation. Through an exploratory case study of two established Chinese firms, we propose a process model that reveals a hierarchical structure of digital capabilities and two distinct approaches for utilizing these capabilities to enable exploitative and exploratory business model innovation, respectively. This study contributes to the emerging business model innovation literature by showcasing how digital capabilities are developed and utilized to facilitate business model innovation.

The aim of this paper is to investigate the impact of learning ambidexterity on firms’ technological innovation, especially in the context of varying degrees of modularity in firms’ knowledge networks. By combining temporal and cognitive aspects, this study focuses on the micro-level utilization of knowledge and redefines exploratory and exploitative learning as learning unfamiliar and familiar knowledge, respectively. Building upon previous research, we classify learning ambidexterity into combined and balanced forms, further investigate their influence on innovation performance. Utilizing a panel data set spanning 10 years of 63 semiconductor firms listed in the US market, our findings reveal that combined learning ambidexterity effectively harnesses the complementary effects of both learning patterns, resulting in a positive influence on technological innovation. Conversely, balanced learning ambidexterity hampers knowledge transferability between these two patterns leading to a negative impact on technological innovation. Additionally, we employ patent data to measure the degree of modularity within firms’ knowledge networks. Our results indicate that higher levels of modularity enhance the positive effect of combined learning ambidexterity on innovation while mitigating its negative impact for balanced learning ambidexterity. These findings suggest the importance for firms to strategically manage and dynamically orchestrate learning ambidexterity alongside double-loop learning practices, while continuously structuring and facilitating reuse within organizational knowledge networks to create favorable circumstances for the effective implementation of learning ambidexterity.

This paper explores the mechanism of dynamic capabilities of platform firms on value co-creation in a complex environment. Based on previous studies, a theoretical model of the relationship between dynamic capabilities, indirect network effects and value co-creation is constructed, and data are collected by distributing questionnaires and empirically tested using structural equation modeling. The results show that the dynamic capability of platform enterprises positively affects the value co-creation of ecosystem, and the indirect network effect plays a mediating role between the dynamic capability of platform enterprises and value co-creation. Finally, relevant management recommendations are proposed based on the research results.

This research proposes a competence-based role model as a methodological basis for guiding the conformation of social innovation lab (SI lab) teams. Based on a comprehensive literature review including the comparison of 7 innovation team frameworks and a set of 14 competences for SI lab teams, a model consisting of the roles of facilitator, maker, visionary and manager is proposed. Applied to 10 nascent SI lab teams from Latin American universities, the self-assessment methodology provides an interpretative approach for SI lab teams to position themselves with regards to their competence profile and proposed roles, while identifying strategies for improvement.

Previous studies indicate that absorptive capacity (ACAP) is a key factor for companies to achieve dynamic capability and competitive advantage. This construct is usually operationalized from two dimensions, namely the potential absorptive capacity (PACAP) and the realized absorptive capacity (RACAP). This study investigates the influence of both ACAP dimensions concerning project performance and project innovation, in addition to analyzing the mediating role played by the learning culture in these relations. Moreover, these relations are investigated based on the structural equation modeling (SEM) method, using the partial least square (PLS) approach, applied to a sample of 128 companies in the automotive industry that manufacture auto parts. According to the results, project performance and project innovation are differently impacted by PACAP and RACAP. Furthermore, the study points out that learning culture only influences relations between RACAP and project performance and project innovation.

Building on corporate governance life cycle theory, this study aims to enrich the understanding of the influence of corporate governance on post-IPO performance of R&D intensive newly public firms. The findings of this study reveal that corporate governance is an essential factor for determining the long run post-IPO performance. Promoters who retained controlling ownership in the post-IPO period and board independence are positively associated with the firm’s performance. Yet, a promoter’s involvement in management can have adverse influence on the firm’s performance. The outcome of the hypotheses draws support at the interplay of agency, stewardship, and resource dependence theory. The novelty of this study prevails in understanding the impact of corporate governance at the nexus of industrial and institutional contexts together.

Curiously, there are few contributions in the scientific literature on the subject of artificial intelligence (AI) and its impact on aeronautics. However, many communications and reports have been published by aeronautic companies about their applications of AI technologies. This article makes an industry-wide review of AI in aeronautics using a three-step sequential approach: (i) a review of AI and its concepts to define and develop a conceptual map; (ii) a selection of 100 use cases from aeronautics companies that use AI technologies (e.g., Airbus, Boeing, Air France, Safran, EasyJet, Dassault Aviation, Altair); and (iii) an analysis of the use cases using the topics defined in the conceptual map. The main results describe a rising interest in the integration of AI technologies by entities in the aeronautic sector. Moreover, the results from the use cases show that the most recurrent technologies are big data analytics, autonomous intelligent systems, predictive analytics, machine learning, and robotics. Another finding is related to the several benefits that motivate companies to integrate AI technologies into their industrial and operational processes. The most frequent benefits include customer satisfaction, saving time, safety and security, cost reduction, better decision making, solving complex problems, and ensuring optimisation and efficiency. It also appears that the performance of companies is positively impacted by using these AI technologies. Such impacts span all operational departments including marketing, where these technologies help satisfy customer needs; the industrial and operational area, which is provided with quality products; and where productivity and economic performance are optimised for more efficiency.

The automotive industry is undergoing a profound transformation characterized by the transition from hardware-centric to software-driven vehicles. This results in a variety of challenges, such as the increasing complexity of components, shortened development cycles, and evolving regulatory requirements that need to be met. In this context, our article explores the impact of UNECE regulations R.155 and R.156 on the technical prototyping phase in the development of mechatronic systems. Furthermore, we use the findings as an objective and examine the implications for the current landscape of prototyping research. Using a comprehensive case study in the German automotive industry with four OEMs, we investigate the current state of prototyping in the German automotive industry. Our analysis shows that although previous research suggests prototyping processes, they reach their limits in complex systems and cannot cope with the challenges of the industry. At the center of the conclusions is the need to develop a prototyping framework that includes a taxonomy of projects and forms the basis for a testing strategy to shorten time-to-market. As part of this, we propose the introduction of the novel term "phygital prototype" to bridge the gap between physical and virtual prototypes. Furthermore, we argue that product compliance should not only be integrated methodically but also propose concrete procedural steps within the prototyping framework.