Hypotheses B aimed to objective number 3: To explore the effect of firm competitive and collaborative behaviour on supply chains, in terms of competitive and collaborative behaviour on supply chains, in terms of

demand fulfilment and survivability of supply chains over the long-term

Most existing studies regarding SCM assess the collaboration performance based on the manufacturer’s standpoint. This is related to the real world situation where manufacturers tend to initiate and lead the collaboration practice. However, in reality, competition and collaboration involve behaviour in the supply and demand market, by considering supplier and customer behaviour. To define and formalise the experimental design, the following hypotheses were constructed. They are related to the important behavioural factors of the manufacturers in both competition and collaboration that are identified in Chapter 2.

79 Hypothesis B.1: Duration of collaboration

The hypothesis for the issue of the duration of collaboration is that

"Adopting longer duration of collaboration does not lead to a better long-term demand fulfilment and survivability of supply chains".

This hypothesis is proposed according to the previous studies that suggest long-term collaboration does not consistently benefit the firms (section 2.4.3.1), such as Porter (1997), Parker and Hartley (1997), Grayson and Ambler (1999), Anderson and Jap (2005), Li et al. (2006), and Sun and Debo (2014). As the issue of the duration of collaboration is often discussed concurrently with the number of partnerships, this hypothesis is applied and tested under two different number of supplier’s partnerships. These are:

a)! single-link supplier, to represent a situation when both the manufacturer and the supplier are only allowed to collaborate with one firm (one-to-one partnerships).

b)! dual-link supplier, to reflect situations when the manufacturer can only collaborate with one supplier, but the supplier can cooperate with up to two manufacturers (one-to-many partnerships).

Hypothesis B.2: Number of partnerships

The hypothesis of the number of partnerships is:

"Having a lower number of partnerships does not improve long-term demand fulfilment and survivability of supply chains".

As for Hypothesis B.1, this hypothesis is constructed based on SCM literature that presents inconsistent suggestions on the number of partnerships (section 2.4.3.2). With respect to the close association between the issue of the duration of collaboration and the number of partnerships in the literature, this

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hypothesis is enacted under two situations: when the duration of collaboration between the manufacturer and the supplier is short and long.

Hypothesis B.3: Trust

This study intends to observe whether the trust which applies at only one side of the supply chains affects the supply chains for a long-term period of competition. Thus, Hypothesis B.3 is arranged into three hypotheses.

Hypothesis B.3.1: The manufacturer trust of the supplier

"Higher manufacturer trust of the supplier does not enhance long-term demand fulfilment and survivability of supply chains".

Hypothesis B.3.2: The supplier trust of the manufacturer

"Higher supplier trust of the manufacturer does not enhance long-term demand fulfilment and survivability of supply chains".

Hypothesis B.3.3: The customer loyalty towards manufacturer

"Higher customer loyalty towards manufacturer does not improve long-term demand fulfilment and survivability of supply chains".

These hypotheses refer to the conclusions of the literature review, which find that trust among firms and customer may not be advantageous significantly to supply chains (section 2.4.3.3). As the behaviour of the manufacturer and the supplier are the main interest of this study, Hypothesis B.3.1 and Hypothesis B.3.2 are thus examined with respect to two situations: when the duration of collaboration between the manufacturer and the supplier is short and long. These situations are considered because trust is popularly considered as the enabler of long-term collaboration and single-sourcing success in previous research. Therefore, both in Hypothesis B.3.1 and Hypothesis B.3.2, the length of the collaboration is highlighted.

81 Hypothesis B.4: Individual firm survivability

As this research investigates the effect of the individual survivability of the manufacturer and the supplier to the supply chains, Hypothesis B.4 is arranged into two following hypotheses.

Hypothesis B.4.1: Manufacturer survivability

"Higher manufacturer survivability does not enhance long-term demand fulfilment and survivability of supply".

Hypothesis B.4.2: Supplier survivability

“Higher supplier survivability does not improve long-term demand fulfilment and survivability of supply chains".

These hypotheses are based on the gap in the literature that indicates that enhancing the firm survivability for supply chain robustness may not improve supply chain performance and survivability for the long term (section 2.5).

Hypothesis B.5: Manufacturer strategic movement (the strategic mutation)

In section 2.5, it is discussed that strategic move should be taken into account to understand the impact of competition strategy on supply chains, with respect to strategic mutations. Although no empirical study discusses the disadvantage of strategic mutation, the mistake in strategic movement has been found to be a cause of business failure, such as the case of Nintendo Wii in its early competitive movement (Kim and Mauborgne 2005; Hollensen 2013). Therefore, the hypothesis of the manufacturer strategic movement (the strategic mutation) is:

"The competition approach suggested in strategic management, regarding the strategic mutation, does not improve demand fulfilment and survivability of supply chains for the long-term".

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All of these hypotheses proposed in this study can be summarised by an illustration shown in Figure 4.1.

Supply chain performance in fulfilling the demand in the

market

Supply chain’s ability to survive (survivability) for the long term

The factors observed in this study

The higher (or longer):

does not always

improve reduces

Figure 4.1 The structure of Hypotheses B

Two main performance measures are used in the hypotheses: demand fulfilment and survivability of supply chains. The first measure is to assess the supply chain’s ability to meet the demand, and the latter represents the supply chain’s robustness towards competition. Both measures are observed for a long duration of the competition. Even though the long-term supply chain profitability or demand fulfilment rate in the market is presumed to be influenced by the

long-83

term supply chain’s survivability, this relationship does not act as a modelling assumption.

4.4!The modelling approach

This study adopts a theory-driven approach to the modelling instead of empirical observations. This is because the complexity level of the problem situation is high and difficult to observe using empirical data. Therefore, the problem situation of this research was described based on the related literature, which are SCM, social science, and ABM. Although three research domains were involved in this research, the dominant domains used in this study are SCM and ABM.

In general, the modelling process in this study involved the four steps of simulation model development suggested by Robinson (2008). The steps are conceptual modelling, model coding or computer modelling, experimentation and analysis. The output of each stage in this research is, consecutively, a conceptual model, a computer model, and a better understanding of the problem ^situation in the real world. The processes were performed in an iterative and repetitive approach as the model was developed incrementally; it started with the simplest representation and then detail was added until the key facets of the problem domain had been characterised.

The conceptual modelling was started from abstracting the problem situation and research aim into the modelling objectives. Each objective is detailed through the hypotheses to make it measurable. The hypotheses led to the description of the model, which includes the experimental factors (or inputs), the model contents, and the responses (or outputs). The experimental design was also constructed in this phase in order to test the hypotheses. These conceptual modelling processes were performed in an iterative and repetitive approach.

The outcome of this conceptual modelling process was a conceptual model. The documentation provides the details required for the computer model development.

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The computer model was verified by comparing the individual agent’s behaviour with the conceptual model and then debugging the model. The validation was conducted in two approaches: employing a face validation with a simulation expert, and explaining the results using several case studies that were obtained from the existing literature.

When the computer model had been verified and validated, the experimentations were run. Analyses of the results were then conducted to obtain an understanding of the problem situation. The knowledge gained from the model was then reflected back into the problem situation defined in this study, to understand whether the research aim had been achieved.

Figure 4.2 illustrates the modelling approaches applied in this study. The figure is adapted from Robinson (2008) with several modifications to represent the real modelling processes performed in this research. The first modifications focused on the implementation of the theory-driven approach, which was the essential element in defining the problem situation as well as analysing the experimental results. The second component added is the part of the hypotheses development, which was constructed after describing the modelling objectives. The verification and validation of the computer model are also expressed in the diagram; this shows the elements that were compared for each test. The double arrows represent the iterative process, and the circular diagram reflects the repetition process in the model’s development. A brief overview of each modelling step is described in the succeeding subsections.

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LITERATURE REVIEW

1. Supply chain management (SCM) and other related field (social science) 2. Agent-based modelling (ABM)

3. Other reliable information sources: business articles and news

Model Building

Validation: - Face validation - Case-based validation

Verification THEORY-DRIVEN

APPROACH

Figure 4.2 The modelling approach of this study (adapted from Robinson 2008)