Search results

The purpose of this paper is to examine whether modular product design is an appropriate practice to improve manufacturers’ flexibility performance and cost performance as well as to evaluate whether combined effects of modular product design and delivery performance on flexibility performance and cost performance exist.

Structural equation modeling with moderating effects is used. Moderating effects allow an evaluation whether combined effects of modular product design and delivery performance exist. For the analysis, data from the international high-performance manufacturing survey are used.

Analysis results show a positive relationship between modular product design and cost performance, but do not show a significant moderating effect. Thus, no combined effect of modular product design and delivery performance exists in the data at hand.

A potential limitation of this study is the cross-sectional nature of the analysis. In order to test for causal relationships or chronological sequences, longitudinal data are deemed more suitable.

The findings make improvement processes more predictable and help managers to overcome traditional trade-off situations, especially in terms of flexibility performance and cost performance. Manufacturers are still neglecting the implementation of complementary methods for achieving an increase in flexibility while maintaining efficiency.

This paper complements prior research on the effect of improvement practices on operational performance dimensions. It also takes an alternative approach to examine whether a beneficial implementation sequence of improvement practices can be assumed.

This study aims to investigate the impact of cross-functional coordination (cross-functional system, process and team coordination) on customer coordination (customer strategic and operational coordination) and operational performance. Following the lens of information processing theory (IPT), this study examines the diverse mechanisms of cross-functional coordination practices in enhancing firms’ information processing capabilities (IPCs) to cope with the higher information processing demands resulting from customer coordination, finally improving operational performance.

Based on data collected from 410 Chinese manufacturers, the authors use the structural equation modeling method to test the theoretical model.

The authors found that cross-functional system coordination is positively associated with customer operational coordination (COC) but not customer strategic coordination (CSC). Cross-functional process coordination increases both customer strategic and operational coordination. Cross-functional team coordination significantly promotes CSC but not COC. Both customer operational and strategic coordination facilitate operational performance.

This research pioneers in identifying three dimensions of cross-functional coordination based on IPT and examine their distinct impacts on various customer coordination activities. The authors distinguish two customer coordination dimensions and reveal their effects on operational performance. This research contributes to the development of IPT. Additionally, this study provides guidelines for managers to coordinate internal departments and collaborate with external customers to enhance firms’ operational performance.

Looking back on the last 12 years, the whole planet went through two major economic crises (2008 and 2019), which both had a profound impact on the survival of businesses. The present study aims to develop and empirically test a conceptual framework that investigates the factors that have an influence on firm survival. More specifically, the study proposes a three-dimensional framework that includes performance drivers (utilizing resource-based view [RBV] factors), performance measures and the measurement of firm survival. Such a multi-dimensional approach has very rarely been explored in the existing literature.

A thorough literature review revealed gaps in the literature and offered the basis for developing the proposed conceptual framework of the study. Its empirical examination (hypothesis testing) was conducted with the use of a newly developed structured questionnaire that was distributed to a group of Greek manufacturing organizations (the final sample consists of 364 manufacturing companies). Empirical data were analyzed using the “structural equation modeling” (SEM) technique (multivariate analysis) and other similar techniques (i.e. exploratory factor analysis and analysis of variance). The study is empirical (based on primary data), explanatory (examines cause and effect relationships), deductive (tests research hypotheses) and quantitative (includes the analysis of quantitative data collected with the use of a structured questionnaire).

On the one hand, empirical results point out that “manufacturing-marketing alignment,” “manufacturing capabilities,” “structural configuration” and “business performance under crisis” have the most significant impact and on short-term survival (current situation). On the other hand, “competitive advantage” and “business performance under crisis” have the most significant impact on long-term survival (future situation). Focusing on RBV factors, only “structural configuration” and “manufacturing capabilities” directly affect short-term survival, while “manufacturing–marketing alignment” has an indirect effect on the same factor. Then again, all RBV factors indirectly affect long-term survival. Also, it is confirmed that short-term survival strongly affects long-term survival.

The present study contributes to the debate concerning the antecedents of firm survival, since current empirical findings are quite inconsistent. Specifically, crucial performance drivers and other measures are incorporated into an original model, which reveals their synergies and their impact on the dynamic dimensions of firm survival. Additionally, it enhances the stream of research that investigates firm survival under crisis since very few similar empirical studies have been conducted. Finally, firm survival is not measured as a static concept but rather as a dynamic one (firm survival – current situation and firm survival – future situation). Overall, the final model can explain 35.2% of the variance in “firm survival – current situation” and 46.3% of the variance in “firm survival – future situation.”