Implications for Full Quantitative Study

4.4.1 Structural Equation Model as Statistical Method

The pilot quantitative study and the in-depth interviews provided insights into the research methods, the operationalisation of the variables and the general interpretation of the results. The following step was to prepare for the full quantitative study using a new sample of 149 contracts from the Provider. The full study will test the eight hypotheses presented in Chapter 3 and test the proposed research model.

Considering the theoretical lens of TCE and the philosophical position for the research, structural equation modelling (SEM) was selected as the ideal statistical method for building the research model (see Figure 10) and to test the proposed hypotheses (Shook et al., 2004). A SEM consists of a set of linear equations that simultaneously test two or more relationships amongst observable and unobservable (manifest and latent) variables. SEM has the unique ability to examine a series of inter-dependent relationships (where an endogenous variable becomes exogenous and predicts another endogenous within the same analysis), whilst also analysis multiple dependent variables.

Structural equation modelling is a popular and mature statistical method (Mueller, 1997) for data analysis to investigate theory-derived causal hypotheses. It is also known as analysis of covariance structures or causal modelling. The SEM method is suited for confirmatory purposes of cause-and-effect models by assessing in a quantitative way relationships amongst variables. SEM is also used in the prediction of latent variables.

The origins of SEM can be traced to path analysis and confirmatory factor analysis. In management sciences is considered the preeminent multivariate method of data analysis (Marcoulides and Hershberger, 1997; Mulaik, 1990) as it incorporates a set of data analysis tools that allow testing theoretically derived and a priori specified causal hypotheses.

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Any research study using SEM should address four specific stages in the preparation of the full quantitative study (Mueller, 1997)

a. Initial Model Conceptualisation or Specification;

b. Parameter Identification and Estimation;

c. Data-Model Fit Assessment or Validation; and d. Potential Model Modification and Improvements.

The results of a research study using SEM as a confirmatory statistical technique will be judged based on the following aspects that will be properly defined and reported in Chapter 6 (Shook et al., 2004).

i. Theoretical foundation for the postulated relations ii. Accuracy in the description of the specified model iii. Accurate description of applied estimation methods iv. Reports on scale properties

v. Reporting on sample properties including size and data availability 4.4.2 Enhanced Primary Data for Full Study

For the full quantitative study the primary data source was the Provider sponsoring the study. As mentioned before the Provider is a world-wide leader in contract logistics services and maintains a selected group of users under a group called Programme Management Group. In this group each user is assigned a Programme Manager or PM, to manage the relationship with key users. All contract data comes from this group and represents about 50% of the managed contracts. The sample is 149 relationships between active and decommissioned contractual alliances. The Provider’s customers will be referred to as Users during this research.

The database received from the Provider includes critical information for each of the contractual alliance that reflects data for the years 2010 and 2011. Table 24 below describes the fields in the Provider’s database, the type of data and a brief description of the content.

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Table 24 Field Description of Contract Database from Provider

FIELD TYPE DESCRIPTION

ID Categorical Provider ’s internal code for user identification Customer Categorical Name of User company to be kept confidential SLA Description Categorical Description of each one of the operational performance

indicator included in the contract in the service level agreement (SLA)

Goal Continuous Quantitative value expected for each one of the operational performance indicators in the SLAs Diff Continuous Calculation of the difference between the indicator’s

goal and the actual average performance

Ave Continuous Quantitative value of the actual performance for each of the operational performance indicators in the SLAs

High/Low Binary

Guideline to assess the performance indicator. Some indicators are better when they are HIGH (like on-time arrival), and some are better when they are LOW (time between arrival and shipping)

Contractual Binary Registers if the relationship has a signed contract or not

Year Nominal Specific year for performance reporting

Account Vertical Categorical Describes one of five industry verticals that applies to a particular USER

Director Name(Owner) Text Name of the specific Programme manager that is the owner of the contractual relationship

Report to Name

(Owner) Text Name of the Programme manager’s supervisor

Yearly Measured

SLA's Continuous Number of measures taken in a given calendar year for a maximum of 12 observations

YTD SLA's Met Continuous Number of observations where the SLAs were met Average Performance Continuous Average value of the operational performance indicator

for the given year of measurement

In order to complete the profiling of each one of the contractual alliances and to operationalise the constructs required for the analysis, the Provider submitted additional information by user contract in a second database with information detailed in Table 25 below. Appendices A and B present the files for both databases received from the Provider.

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Table 25 Additional Primary Data Related to Contractual Success

FIELD TYPE DESCRIPTION

ID Categorical Provider ’s internal code for user identification and the common field across databases

Longevity Continuous Number of years the User has been a user of the PROVIDER

Renegotiations Continuous

(Azorín and Cameron, 2010; Molina-Azorin, 2012) Number of renegotiations on the conditions of the contractual relationship between User and PROVIDER

Value Continuous Annual value of the relationship in terms of the value of the contract

Active Binary Status of the relationship as active or non-active.

4.5 Summary

As presented throughout the chapter, the pilot study in all its dimensions is consonant with the philosophical position and the theoretical lens chosen to study inter-firm performance alignment. The research design, data collection methods and mixed analytical techniques to measure and analyse alignment, are congruent with the requirements of a QUAN-Qual research design (Azorín and Cameron, 2010; Molina-Azorin, 2012).

The operationalisation of variables such as inter-firm alignment and contractual alliance success has been properly demonstrated. These quantitative methods were supplemented by a qualitative validation from the actual relationship managers of the pilot contracts. Overall, the presented methods provided some initial answers to the stated research questions from the literature review.

Statistical analysis to test correlation between degrees of alignment and contract success was not performed due to sample size, and considering that for the pilot study all contracts were active. The statistical analysis has been deferred for the main study with a larger data set of both types of contracts:

active and decommissioned. The hypotheses will be tested during the full quantitative study. Chapters 5 and 6 summarise the results of the quantitative pilot study and the full qualitative analysis. Chapter 7 will provide a discussion of the results in light of the proposed hypotheses.

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5 RESULTS FROM PILOT STUDIES