With the ever-increasing acceptance of the need to empirically validate theories in the social science disciplines, data and multivariate analysis techniques play a central role in today’s research. The evolution of structural equation modeling (SEM) methods is perhaps the most important and influential statistical development in the social sciences in recent years.
Hair, Ringle and Sarstedt 2012, p.312 6.1. Introduction
With the growing acceptance of the need to empirically validate theories in the social science disciplines, especially marketing (e.g., Davis et al. 2013), multivariate analysis techniques (e.g., Sarstedt et al. 2013) play a central role in contemporary research to validate developed theory. Data analysis and reporting of results connect the research method and data with implications drawn from the study. While the solution literature suffers from a lack of generalisability of findings (Jacob and Ulaga 2008), the present study puts effort into employing a research methodology that would increase the generalisability of the findings in the solution domain. The data analysis undertaken in this Chapter (Chapter Six) aims to address the research problems using statistical techniques to increase the robustness and generalisability of the findings. Building on the suggestion of Perry (2002), Chapter Six focuses on using specific statistical methods to inspect the data and examine the precision and significance of the “B2B Professional service firms service solutions model” which is underpinned by the hypotheses.
To examine the precision and significance of the theoretical model several analytical stages were undertaken. In the first stage, preliminary analysis was undertaken to identify profiles of the sample and descriptive statistics of all indicators (items). Preliminary analysis is discussed in Section 6.2. The analysis technique is presented in Sections 6.3 and 6.4 covers the outer measurement model result. Section 6.5 presents the result of the validity assessment. The common method variance analysis is detailed in Section 6.6. The results of the hypothesis testing are presented in Sections 6.7 and Section 6.8 provides the result of the assessment of the predictive relevance of the inner structural model, and the method fit of inner structural models. Finally, the chapter is closed with a summary of hypotheses results in Section 6.9. The conclusion of results is presented in Section 6.10.
178 6.2. Preliminary data analysis
As indicated in Chapter Five, the data were collected in Taiwan from professional service firms. The significance of Taiwan as an appropriate laboratory for the present research was discussed in Chapter One, Section 1.3. To collect the data from firms within the sampling frame a list of professional service firms was obtained from a commercial list provider. The list included 3000 PSFs and from this list, 650 firms were randomly chosen. Among the 650 PSFs, 10% were disqualified, because they did not meet the sampling criteria established (e.g., did not have more than 50 employees) and 65% were either not interested to participate in data collection, or did not reply to invitation letters to participate in the survey, or provided incomplete surveys. Finally, 150 B2B PSFs completed and returned all three surveys (A, B, and C), providing a response rate of 23%, which demonstrates satisfactory response rate. This response rate compares very favourably with Agarwal and Selen (2009) who reported response rate of 22.13% in telecommunication and Sweeney et al. (2011) who obtained 15% response rate from a study of PSFs.
In undertaking the data analysis for this chapter the suggestion of Anderson et al. (2010) were adopted in that the preliminary analysis encompasses two important tasks. The first task, concerns examining and reporting the profile of the sample based on demographic items of firms and individual respondents across the three surveys. The second task concerns computing the descriptive statistics of the construct measures. The sample profiles in terms of firmographics are discussed in Section 6.2.1, followed by the results of the descriptive analysis of the measures in Section 6.2.2.
6.2.1. Profiles of the Sample
Profiles of the sample are categorised into two categories, the first category explains the PSFs’ characteristics and, the second category outlines the respondents’ characteristics. The PSFs characteristics are characterised by three items including (1) service sectors, (2) PSFs’ size, and (3) PSFs’ age. The information related to respondents’ characteristics include (1) designated tittle, (2) education level, (3) gender, (4) age, (5) total years of experience in their current position, and (6) years of experience in the examined PSF.
The information related to PSFs’ characteristics is presented in Table 6.1. The sample covered a broad range of companies in terms of sectors and size. PSFs included in the sample came from ten different sectors. The sectors were represented as follows: software design firms accounted for 18%, test and inspection services 18%, architectural 17.3%, consultancy 14.7%, research services 8.7%, engineering 6.7%, real
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estate 6%, finance 5.3%, interior design and accounting both with 2% and advertising services 1.3%.
Further, as shown in Table 6.1, 18.7% of PSFs were founded between 1-10 years prior to data collection, 34.6% of PSFs were founded between 11-20 years, 29.3% were founded between 21-30 years, and 17.3 % of the firms were founded more than 30 years ago. The results presented in Table 6.1 reveals that the majority of PSFs were between 11-20 years of age. The results of the analysis presented in Table 6.1 further reveal that 48.7% of the PSFs had between 50 to 100 fulltime employees, 22.7% of PSFs had 101-200 fulltime employees, 14.7% had 201-500 employees, 2.7% had 501- 1000 fulltime employees, and 8.7% of PSF had over 1000 fulltime employees. Further, within the surveys returned, 2.7% of the firms did not indicate the number of employees. The results presented in Table 6.1 reveals that the majority of PSFs had 50-100 full-time employees.
Table 6.1
Profile of sample - PSF characteristics
Variable Industry and their code Observed Frequency Percentage
Industry Sector
10 Software design services 27 18.00%
11 Test and inspection 27 18.00%
3 Architecture 26 17.33%
4 Consultancy 22 14.67%
9 Research 13 8.67%
5 Engineering 10 6.67%
8 Real estate service 9 6.00%
6 Finance 8 5.33% 1 Accounting 3 2.00% 7 Interior design 3 2.00% 2 Advertising 2 1.33% Firm Age 1 to 10 28 18.7% 11 to 20 52 34.6% 21 to 30 44 29.3% Over 31 26 17.3% Firm Size 50 to 100 73 48.7% 101to 200 34 22.7% 201 to 500 22 14.7% 501-1000 4 2.7% Over 1000 13 8.7% Missing 4 2.7%
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The information related to individual respondents is presented below. As mentioned in Chapter Five, Section 5.4.2.1.2, step 4, three different respondents in three different managerial positions from each firm were asked to complete Surveys A, B, and C. As seen in Figure 6.1, the majority of respondents across all three surveys were males, with one 125 respondents of Survey A (83% of respondents), 132 respondents of Survey B (88% of respondents), and 117 respondents of Survey C (78% of respondents) were male. Only 15 respondents of Survey A (17% of respondents), 18 respondents of Survey B (12% of respondents), and 33 respondents of Survey C (22% of respondents) were females.
Figure 6.2 demonstrates the distribution of education among respondents. As shown in Figure 6.2, the majority of respondents who completed Survey A (50% of respondents) and Survey B (54% of respondents) had postgraduate qualifications, while undergraduates were the dominant group of respondents that completed Survey C (53% of respondents). Male Female 0 25 50 75 100 125 150 Survey A Survey B Survey C 125 132 117 15 18 33 Male Female Figure 6.1
Distribution of gender across three surveys
0 10 20 30 40 Survey A Survey B Survey C Others (i.e. PhD) Postgraduate Undergraduate Figure 6.2
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Figure 6.3 presents the distribution of positions across the three surveys. Overall, 35.3% of respondents of Survey A were CEOs, 34.6% vice presidents, and 28% were deputy managers. In Survey B, marketing managers were dominant with 51%, followed by directors (30%) and sales managers (19%). In Survey C, general managers were dominant with 83% and only 6% of respondents held the position of customer service managers.
Figure 6.3 presents the age distribution across Surveys A, B, and C. Overall, 12.1% of respondents in Survey A were between 20 to 30, 36.5% between 31 and 40, 24.3% between 31 and 40, and 24.3% between 51 and 60 and only 2% were over 60 years of age. In Survey B, 18.8% of respondents were between 20-30 years of age, 35.8% were between 31-40, 30% between 31 and 40, and 11.3% between 51and 60 and only 3% were over 60 years of age. In Survey C, 18.1% of respondents were between 20 and 30, 34.5% were between 31 and 40, 36.3% between 31 and 40, and 9% between 51 and 60, and only 1% was over 60 years of age. Further, the average age of respondents for Survey A was 42.33, for Survey B was 40.12, and for Survey C was 39.38. 0 20 40 60 80 100 120 140 Survey A Survey B Survey C Figure 6.3