The Relationship between hard and soft technologies and competitive performance in the Thailand context

Past empirical studies have investigated the effect of hard and soft technologies on different measures of performance in the operations area. The term ‘hard technologies’ refers to hardware and the associated software consisting of technologies such as computer numerical control (CNC) machines, flexible manufacturing systems, robotics, etc., whilst ‘soft technologies’ refers to manufacturing techniques and know-how and includes Just-In-Time (JIT) manufacturing and Total Quality Management (TQM). Drawing on the resource-based view (RBV) conceptualization of the firm as a bundle of resources, this study argues that the relationship between hard and soft technologies and competitive performance can be additive and interactive. Hence it seeks to advance previous studies in two main ways. Firstly, differing from previous studies that generally consider the effects of either hard or soft technologies on manufacturing performance, this study brings the two types of resources (hard and soft technologies) into one study and examines their additive effects. As suggested by the RBV, it is important to understand the unique roles of the different types of resources on improving different types of performance. Investigating these unique effects is important in guiding managers to align the types of resources and the competitive goals pursued by firms. Secondly, this study investigates the interaction between hard and soft technologies in predicting different types of manufacturing performance. Drawing on the RBV, the concept of complementarity, and Socio-Technical System (STS) Theory, this study examines how the interaction between these two types of resources can be synergistic; this occurs when one resource magnifies the impact of another, multiplying the common effect. However, to date there have been very few empirical studies examining the effects of synergistic relationships between hard and soft technologies on multiple types of manufacturing performance. In addition, this study investigates the effects of hard and soft technologies on different types of performance in the context of an emerging economy, Thailand. Most research has been conducted in the context of advanced economies. There have been very few studies aimed at understanding operations management issues in emerging economies. Hence research is needed to examine whether findings from studies conducted in advanced economies also apply in emerging economies such as Thailand’s. A two-phase design was implemented in this research. The first phase comprised a quantitative, self-administered mail survey, completed by managers from 186 manufacturing plants in Thailand. Structural equation modeling (SEM) was used to test 12 hypotheses about the effects of hard and soft technologies on cost, product quality, lead time and flexibility performance. In the second phase, qualitative interviews were conducted with eight senior/middle managers from four plants to explore and further integrate the quantitative findings in greater depth. There are two major findings from this study relating to the drivers of high performance that have implications for the operations management discipline and practitioners. Firstly, hard and soft technologies are shown as significantly and positively related to the four types of manufacturing performance (cost, product quality, lead time and flexibility performance), hence emphasizing the importance of both hard and soft technologies and confirming their additive relationship. The results also suggest that hard technologies have a relatively stronger relationship with flexibility, cost, and lead time, whilst soft technologies have a stronger relationship with product quality. This study therefore highlights the importance of understanding the unique roles of resources in improving different types of performance as suggested by the RBV. It also offers useful insight into firms’ technology justification. Secondly, the significant, positive interaction between hard and soft technologies in predicting the four types of manufacturing performance represents the synergistic relationships between these two types of technologies. This study has found that development of either hard or soft technologies may be sufficient for incrementally improving performance. Akin to the RBV, the concept of complementarity and the STS, consideration of the synergistic relationship between hard and soft technologies offers an opportunity for multiplying their common effect. Because many manufacturing firms have invested enormous capital in manufacturing technologies over the last three decades, it is important for decision makers to understand costs and benefits from the usage and applicability of these technologies to their companies. Additionally, the results also confirm that the RBV, the concept of complementarity and the STS, originally developed in advanced economies, are applicable in an emerging economy such as Thailand