MODALIDADES DE MATRÍCULA 1 Matrícula Presencial

This section presents the analysis of sample specifications and analytical techniques used by researchers in the CCSP literature. The analysis reveals that single informant designs are the dominant research approach. The use of single versus multiple respondents is reported for 42 out of the 55 studies found, which indicates only 13 studies use multiple informant designs. For single informant designs, 5 studies use the B2B context, 15 studies look at firms in the B2C context, and 22 examined the customers’ points of view. Researchers, who use multiple informant designs, employ different types of informants in their studies. For example, Ordanini and Pasini (2008) employ managers in different organisational levels and Hakanen and Jaakkola (2012) and Ramani and Kumar (2008) employ managers in both B2B and B2C. The analysis shows that other researchers study or focus on employees and customers (Chan et al.

Table 2.2

The focus of research: Topic CCSP

Topic No. of articles % Topic No. of articles % Definitions of CCSP • Behaviour • Cognitive • Emotion • Scale development • Differences between terms 26 24 Strategies • Customer focused • Architecture • Strategic models 4 3.8 Firm Performance • Service performance • Firm performance • Employee satisfaction • Efficiency 23 19.4 Project • Project development • Project solution • Information sharing 4 3.8 Value • Perceived value • Perceived service • Value network • Consumer experience • Service quality 14 12.9 Service recovery 3 2.77 Innovation • New service • Service design • Partnership • Customers' resource 9 8.3 Managing CCSP 3 2.77 CCSP capability • Process • Customer

9 8.3 Culture and shared _meaning 3 2.27 Customer empowerment • Communication channels • Communities • Learning • Self- production

6 5.5 Analytical models applied _{in CCSP} 1 0.92

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2010; Hsieh et al. 2003), managers and customers (Wang et al. 2013; Peled and Dvir 2012; Yi et al. 2011; Guo and Ng 2011; Bettencourt et al. 2002), and employees and managers (Chathoth et al. 2013; Perks et al. 2012) as key informants in their studies. Authors, who employ a multiple informant design, justify their design in two ways. First, they argue that a multiple informant design better accounts for common method variance (e.g., Yi et al. 2011; Chan et al. 2010). Second, the accuracy of information is strengthened if different people answer different questions, where such questions are more related to their duty in their organisation (e.g., Hakanen and Jaakkola 2012; Yi et al. 2011; Arnold et al. 2010; Chan et al. 2010).

The studies included in the database were also examined for patterns in terms of sample size and response rates. Interestingly, three studies report respondents of over 1500. The average number of respondents identified is 546, the largest sample size is 2679 and the smallest was 79 respondents. Further, the analysis revealed that the average response rate for quantitative studies is 33.74%, with the highest response rate recorded at 45% and the lowest at 14.2%. The average number of respondents in qualitative studies is 42.5, the largest sample size is 78, and the smallest was 6.

The results of the analysis of studies presented in Figure 2.3 also shows that 37 studies are based on survey data (quantitative methods), 17 are based on interview protocols (qualitative methods), and 5 are based on case study applying both interviews (qualitative methods) and analysis of firm’s official documents (secondary data). Other methods such as observation, jury/expert judgement, and secondary data have been used rarely. Figure 2.3 illustrates the distribution of data collection methods in the current content analysis with the number of studies used each method of data collection.

To provide a deeper understanding of the research methodologies applied by researchers, data analysis methods are presented in Table 2.3. The results of this review indicate that qualitative-based studies commonly utilise analysis techniques such

37 17 5 2 1 1 0 5 10 15 20 25 30 35 40 Figure 2.3

Method of data collection

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as text analysis of the interviews and focus groups. Text analysis includes coding of the interview transcripts or focus groups, and key word searches (e.g., Vivek et al. 2012; Aarikka-Stenroos and Jaakkola 2012; Guo and Ng 2011; Bowden 2009). Other qualitative methods such as sequential analysis (Perks et al. 2012), inductive and deductive analysis (Peled and Dvir 2012; Aarikka-Stenroos and Jaakkola 2012), and iterative narration approach (Ordanini and Pasini 2008) have been undertaken in studies.

Interestingly, some studies use secondary data in the form of organisational documents and reports to evaluate changes in informant responses because of the effect of revealing the analysis of organisational documents and reports. For instance, Enz and Lambert (2012) and Ordanini and Pasini (2008) interview informants and obtain their first responses to questions located in the survey. In the second stage, they study official documents and reports and present the results of their observation to managers. In the third stage, after revealing the information to managers, they interview managers to understand whether the information affect their responses. The dominant software used in the qualitative research is NVIVO and Microsoft Word (e.g., Aarikka-Stenroos and Jaakkola 2012; Guo and Ng 2011). Qualitative researchers adopt different approaches to explain their designs in their research methodologies. While some explain the method of data analysis systematically (e.g., Aarikka-Stenroos and Jaakkola 2012; Perks et al. 2012), others do not clearly articulate how they code scripts and data, nor do they explain the analysis procedure (e.g., Hakanen and Jaakkola 2012; Peled and Dvir 2012, Bowden 2009, Bettencourt et al. 2002).

The analytical techniques applied to analyse data in quantitative studies are presented in Table 2.3. In particular, researchers adopting quantitative methods report two categories of analysis: (1) preliminary analysis to test the reliability, data structures and fitness of the research model and constructs, and (2) theory testing analysis to analyse the proposed hypothesis. In doing so, quantitative researchers employ a variety of analyses in both preliminary data analysis and theory testing analysis. Table 2.3 shows that the most frequently employed preliminary analytical techniques used by quantitative research is reliability analysis via Cronbach alpha, discriminant validity, convergent validity, CFA, AVE, AVA, and EFA. As shown in Table 2.3, covariance- based SEM and variance-based SEM (e.g., Partial Least Squares [PLS]) appear to be commonly emploied for theory testing. To test theories, software such as Smart-PLS and PLS-Graph, as well as covariance-SEM such as Lisrel, and Amos are commonly used by researchers. Interestingly, all studies report direct effect or mediation/moderation relationships between constructs in their proposed research model. None of the studies analyse data via curvilinear analysis to study more complex relationships.

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Table 2.3

Analytical techniques reported Preliminary analysis No of used No of used Discriminant validity

- Determine by the AVE and squared correlation.

– An indicator’s loadings should be higher than all of its cross loadings.

16 Convergent validity - Calculated based on the

average variance extracted (AVE) 13

CFA 13 EFA 2

Harman single-factor 1 Descriptive statistics 1

GoF 1 Frequency 1

Theory testing

SEM 10 Regression 7

t-tests 4 PLS 5

AVA 4 Moderation effect 3

Mediation 3 ANOVA 2

MANOVA 2 Hierarchical Moderated _Regression 2

3-stage least square 1 Cluster analysis 1

Correlation 1 ANCOVA 1

Maximum-likelihood estimation

method 1