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A risk, when it occurs, causes negative effects on outputs. Typically, risks are not independent as multiple risks occur simultaneously. The purpose of this paper is to compare the impact various risks have on the performance of manufacturing-oriented and service-oriented firms from a supply chain (SC) perspective.

First, SC risks were identified and classified into two criteria: context and probability. Then, the different characteristics between manufacturing- and service-oriented firms were distinguished by the theory of goods-dominant logic (GDL) and service-dominant logic (SDL). Structural equations modeling and multiple-group analysis were then used to validate research hypotheses and compare the two groups.

The empirical evidence, gathered from the Vietnamese construction sector, indicated that in a serious situation all of the five risks proposed occur at the same time, thus the remarkable 87.1% variance in SC performance was explained. Furthermore, this rate is significant when the two groups are compared: manufacturing-oriented firms (88.3 percent) and service-oriented firms (85.6 percent), implying that risks in the manufacturing-oriented group have a greater effect on SC performance. While manufacturing-oriented companies should pay close attention to the operational and demand risks that adversely affect SC performance, they should treat information risk as an opportunity to improve. Service-oriented companies, however, need to manage supply risk which, in their case, can be attributed to a 51.2 percent variance in SC performance. Moreover, service quality can also be improved remarkably if information risk is well managed.

This study provides a detailed picture of the relationship between risks and performance in the SC. Risks are illustrated as affecting the SC performance simultaneously, (not separately) and so the approach outlined here will give firms a comprehensive view of their SCs and provide guidelines for predicting the impact risks will have on the SC performance. Moreover, by comparing manufacturing- and service-oriented firms, a thorough overview of risk behaviors is provided and appropriate solutions for each type of company can be determined.

The “novelty of approach” of this study is in applying GDL and SDL theory to classify the manufacturing-oriented and service-oriented firms. The different characteristics between the two groups are identified and explained in terms of resources, value, network, effectiveness vs efficiency and communication, thus providing an insight into risk management activities in the SC network.

The purpose of this paper is to examine the push effect of risk on supply chain (SC) performance, a new concept in the SC risk body of literature, at service-oriented firms.

Two models were compared: first, contains relationships between risks that show the mechanism of the push effect, i.e. the theoretical model. The other, only exists in direct effects of risks on SC performance, i.e. the competitive model.

Test results proved that the mechanism of the push effect can increase the degree of impact of each and all risks on outputs. By the push effect, risks can explain up to 65 percent variance of SC performance compared with 52 percent of the model without push effect. Moreover, the research found two kinds of the push effect: positive – increasing the impact of “pushed” factors on outputs and vice versa for negative.

The mechanism of the push influence will be broken if mutual interaction among risks was minimized. Practitioners and managers can apply the resultant model as a “road map” in their context to achieve this purpose.

Vargo and Lusch (2008) argued that service-oriented firms will be a new trend since the modern-day industry tends to more focus on customer demand. SC management gradually shifted toward demand chain management that organizations will not make and sell units of output but producing customized services to customers (Walters, 2008). This transformation has led to the emergence of new risks, the impact of risk on the SC also varies and the mismatch of the current risk mitigation strategies (Lusch et al., 2007). Dealing with these changes is the purpose of this research.