PhD completion seminar: SerVal in B2B cloud computing

Service Value in Business-to-Business Cloud Computing

PhD completion seminarof

Roland Padilla

Department of Computing and Information SystemsThe University of Melbourne

06 September 2013

Monday, 9 September 13

Hallatt, A. 2013, Arctic Circle, accessed 03 September, 2013, from <http://www.thecomicstrips.com/store/add.php?iid=32573>.


Presentation Outline

1. Context2. Gap3. Purpose4. Research questions5. Research design6. Components of SV in B2B cloud computing7. Structural and measurement model8. Discussion


Context• KPMG shows increased adoption of cloud

services across the Australian economy would grow GDP by $3.3B by 2020.

• EU predicts a 1% GDP growth and create 2.5M jobs by 2020.

• IDC estimates 14M jobs and US$1.1trillion a year in new business ventures by 2015.

• Forrester projected cloud services of $14.7B in 2010 will grow to $94.1B by 2015.

1)European Commission (2012) Unleashing the Potential of Cloud Computing in Europe, http://ec.europa.eu/information_society/activities/cloudcomputing/docs/com/com_cloud.pdf. 2)KPMG (2012) Modelling the Economic Impact of Cloud Computing, www.kpmg.com/au/en/issuesandinsights/ articlespublications/pages/modelling-economic-impact-cloud-computing.aspx, 3)IDC (2012) Quantitative Estimates of the Demand for Cloud Computing in Europe and the Likely Barriers to Up-take, http://ec.europa.eu/information_society/activities/cloudcomputing/docs/quantitative_estimates.pdf4)Journal of International Commerce and Economics (2012) Policy challenges of cross border cloud computing www.usitc.gov/journals/policy_challenges_of_cross-border_cloud_computing.pdf.


Little is known about the measurement of service value in business-to-business (B2B) cloud computing services from a customer perspective.

Gap


Purpose

The ultimate aim of the research project is to measure service value for customers of B2B cloud computing services and then to test a model explaining how service value relates to both customer satisfaction, and the intention of the customer to repurchase the service.


Research Questions

RQ1: What are the components of service value in B2B cloud computing?

RQ2: How do we measure the components of service value?


Service value (Ruiz et al., 2008, p. 1281) comprises four components: 1) Service quality measures customer perceptions of the quality of service delivered by the company providing the service.

2) Service equity concerns the image or brand equity as perceived by the customer. Brand image can engender a strong feeling of “proximity, affection and trust” (Ruiz et al., 2008, p. 1281) in the brand purchased. Brand equity, built up through customers sharing experiences of services delivered, is also important in the overall equity of the service

3) Confidence benefits concerns customers “having belief, trust, or faith in an organization, its staff and services” (Flanagan et al., 2005, p. 374) and is often built by a company going beyond the core service delivered.

4) Perceived sacrifice measures customer perceptions of the costs, monetary and non-monetary (e.g., time, effort), they face by purchasing a service.

Components of Service Value

Ruiz, DM, Gremler, Dwayne D., Washburn, JH & Carrión, GC 2008, ‘Service value revisited: specifying a higher-order, formative measure’, Journal of Business Research, vol. 61, no. 12, pp. 1278–1291.


Ruiz et al’s (2008) Model Augmented Model

Ruiz, DM, Gremler, Dwayne D., Washburn, JH & Carrión, GC 2008, ‘Service value revisited: specifying a higher-order, formative measure’, Journal of Business Research, vol. 61, no. 12, pp. 1278–1291.

Service Value Model


Qualitative à-21 managers-30mins average-156-page transcript-tested components-additional theme -Australia only

Quantitative-328 participants-nearly 10% of total -PLS-SEM-SmartPLS software

Instrument à-30-item measure-7-pt Likert scale-Strongly disagree to strongly agree-Australia, US, Europe and Asia-Survey Monkey

Research Design

Creswell, J. W., & Clark, V. L. P. (2007). Designing and conducting mixed methods research. Sage publications. Thousand Oaks, CA.


ParticipantsPosition Industry Cloud Service

1) CIO Research IaaS

2) IT Director Legal IaaS

3) Director IT IaaS

4) Executive Director

Education IaaS

5) Director Logistics IaaS

6) GM Hospitality SaaS & PaaS

7) GM Transportation SaaS

8) IT Manager

Not-for-profit IaaS

9) Managing Director

Recruitment IaaS & SaaS

10) Director Accounting SaaS

Position Industry Cloud Service

11)Investor Startup IaaS12) Director Education IaaS

13) CTO IT IaaS

14) Solutions Architect

Financial IaaS

15) Founder Startup IaaS & PaaS

16) IT Director Transport IaaS & PaaS

17) Manager IT IaaS, SaaS & PaaS

18) Partner Accounting IaaS & PaaS

19) IT Manager IT IaaS & PaaS

20) Solution Adviser

IT IaaS & SaaS

21) Executive Director

IT IaaS & PaaS

IaaS – Infrastructure-as-a-ServicePaaS – Platform-as-a-ServiceSaaS – Software-as-a-Service


Components of Service Value in B2B Cloud Computing

1) Service Quality2) Service Equity3) Confidence Benefits4) Perceived Sacrifices5) Cloud Service Governance


Indicators of the Measurement Models (1/2)Service Quality (SQ)

SQ1 This cloud provider’s service is reliable.SQ2 This cloud provider’s service is consistent.SQ3 When I need the cloud service, I can always access it.SQ4 My experiences of having a cloud service monitoring tool are always excellent.SQ5 Overall, I can rely on my cloud provider to deliver the expected services.

Service Equity (SE)SE1 Even if another cloud provider offers the same type of cloud service, I would still prefer this cloud provider.SE2 If another provider offers cloud computing services as good as my provider, I would still prefer my current provider. SE3 If another provider is not different from my current cloud provider in any way, it is still smarter to purchase my existing provider’s services.

Confidence Benefits (CB) CB1 I have less anxiety when I receive alert notifications whenever there is a service outage.CB2 I have less anxiety when I use the services of this cloud provider.CB3 Even if something goes wrong, I still have confidence in dealing with the current cloud provider.

Perceived Sacrifices (PS)PS1 The total cost of securing this cloud service is high.PS2 The time required to use my cloud provider’s services is high.

Cloud Service Governance (SG)SG1 Even if my existing cloud provider’s service levels drop, I would still prefer my current provider.SG2 I can trust my data with this cloud provider.SG3 It is easy to migrate my data to another cloud provider.


Indicators of the Measurement Models (2/2) Service Value (SV)

SV1 The value I receive from this cloud provider’s service is worth my time. SV2 The value I receive from this cloud provider’s service is worth my effort.SV3 The value I receive from this cloud provider’s service is worth my money.SV4 My cloud provider’s services are competitively priced.SV5 My cloud provider offers good value for the price I pay.SV6 My cloud provider understands my business needs.

Customer Satisfaction (CS)CS1 I am satisfied with my cloud provider’s services.CS2 Overall, I am pleased when I purchase this cloud provider’s services.CS3 Using this cloud provider’s services has been a satisfying experience.CS4 I am satisfied with my cloud provider.CS5 I chose well in determining this cloud provider to deliver my business needs.CS6 Having this cloud service has made my job easier.

Repurchase Intentions (RP) RP1 I intend to continue doing business with this cloud provider in the future.RP2 As long as the present service continues, I doubt that I would switch cloud providers.


Reflective à•Bootstrapping diagram•Composite Reliability•Outer Loadings•Average variance extracted (AVE)•Cross Loadings•Fornell-Larcker Criterion•Results Summary for Reflective Measurement Model

Structural

PLS-SEM Procedure

Formative à

1) Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2013). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage. Thousand Oaks, CA.2) Ringle, C., Sarstedt, M., & Straub, D. (2012). Editor’s Comments: A Critical Look at the Use of PLS-SEM in MIS Quarterly. MIS Quarterly (MISQ), vol. 36, no. 1, pp. iii-xiv.

• Outer Weights• t Value• p Value• Significance level

• R2

• f2

• Stone-Geisser’s Q2 and q2 using blindfolding

• Path coefficients using bootstrapping method

• Significance testing of the structural path


Partial Least Squares Structural Equation Model

Ringle, C.M., & Alexander, S.W. (2005). SmartPLS (2.0 beta) [software]. Hamburg, Germany: SmartPLS retrieved from http://www.smartpls.de.


Discussion1) We found broad support for the extended service value model in a B2B

cloud computing context.

2) We found empirical support for the extra component called “cloud service governance (SG)”. Further research will explore whether SG applies to general B2B services.

3) We did not find support for the component called “service equity (SE)” . This may be because of the lack of maturity in the cloud computing service market. As the market matures, re-testing may find this component also applies. However, it may also be that SE is not as important in B2B services compared with B2C services. Both require future research.

4) Advances the literature by extending the established B2C service value model to the B2B context of cloud computing. Knowing this will enable customers to better understand the components of service value. Vendors will be able to measure how well their services are leading to value and satisfaction for their customers.


Questions and Suggestions

Hallatt, A. 2013, Arctic Circle, accessed 03 September, 2013, from <http://www.thecomicstrips.com/store/add.php?iid=85534>.


Reflective à•Bootstrapping diagram•Composite Reliability•Outer Loadings•Average variance extracted (AVE)•Cross Loadings•Fornell-Larcker Criterion•Results Summary for Reflective Measurement Model

Structural

Details of the PLS-SEM Procedure

Formative à

1) Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2013). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage. Thousand Oaks, CA.2) Ringle, C., Sarstedt, M., & Straub, D. (2012). Editor’s Comments: A Critical Look at the Use of PLS-SEM in MIS Quarterly. MIS Quarterly (MISQ), vol. 36, no. 1, pp. iii-xiv.

• Outer Weights• t Value• p Value• Significance level

• R2

• f2

• Stone-Geisser’s Q2 and q2 using blindfolding

• Path coefficients using bootstrapping method

• Significance testing of the structural path


First Step: Evaluation of the reflective measurement model


BootstrapBootstrap T Statistics


Composite Reliability Composite Reliability Cloud Service Governance 0.74 Confidence Benefits 0.85 Customer Satisfaction 0.97 Perceived Sacrifices Repurchase Intentions 0.92 Service Equity 0.89 Service Quality 0.95 Service Value 0.95

The composite reliability varies between 0 and 1, with higher values indicating higher levels of reliability. As all the composite reliability values are above 0.60, this indicates internal consistency of reliability.


SG CB CS PSCB2 0.73 CB3 0.86 CB4 0.83 CS1 0.93 CS2 0.95 CS3 0.94 CS4 0.95 CS5 0.93 CS6 0.83 PS1 0.89 PS3 0.79 RP1 RP2 SE1 SE2 SE4 SG1 0.63

RP SE SQ SVRP1 0.95 RP2 0.90 SE1 0.88 SE2 0.90 SE4 0.79 SQ1 0.92 SQ2 0.94 SQ3 0.89 SQ4 0.76 SQ5 0.93 SV1 0.90 SV2 0.90 SV3 0.93 SV4 0.83 SV5 0.91 SV6 0.73

Outer Loadings

Indicators with very low outer loadings (below 0.40) should, however, always be eliminated from the scale. We are therefore retaining all the reflective indicators.


Average Variance Extracted (AVE)

As the AVE value of 0.50 or higher is indicated above, the construct explains more than half of the variance of its indicators. Therefore, all the values establish convergent validity on the construct level.

AVE Cloud Service Governance 0.50 Confidence Benefits 0.65 Customer Satisfaction 0.85 Perceived Sacrifices Repurchase Intentions 0.85 Service Equity 0.73 Service Quality 0.79 Service Value 0.76


Cross Loadings SG CB CS PS RP SE SQ SVCB1 0.41 0.73 0.37 -0.08 0.28 0.36 0.34 0.35 CB2 0.62 0.86 0.61 -0.07 0.53 0.59 0.49 0.56 CB3 0.57 0.83 0.61 -0.09 0.58 0.44 0.45 0.58 CS1 0.55 0.63 0.93 -0.20 0.76 0.50 0.66 0.81 CS2 0.60 0.65 0.95 -0.23 0.77 0.52 0.63 0.84 CS3 0.56 0.63 0.94 -0.25 0.75 0.50 0.59 0.81 CS4 0.58 0.65 0.95 -0.25 0.78 0.53 0.62 0.82 CS5 0.56 0.62 0.93 -0.23 0.82 0.54 0.61 0.83 CS6 0.47 0.56 0.83 -0.24 0.72 0.41 0.52 0.73 PS1 -0.11 -0.08 -0.23 0.94 -0.16 -0.04 -0.10 -0.28 PS2 0.01 -0.10 -0.21 0.70 -0.16 0.01 -0.12 -0.20 RP1 0.55 0.61 0.86 -0.21 0.95 0.52 0.59 0.80 RP2 0.47 0.47 0.64 -0.12 0.90 0.65 0.46 0.58 SE1 0.52 0.53 0.52 -0.08 0.58 0.88 0.62 0.48 SE2 0.56 0.52 0.47 -0.00 0.55 0.90 0.45 0.44 SE3 0.51 0.45 0.39 0.02 0.44 0.79 0.35 0.39 SG1 0.63 0.41 0.29 0.02 0.33 0.52 0.20 0.29 SG2 0.87 0.65 0.61 -0.12 0.55 0.56 0.52 0.58 SG3 0.58 0.28 0.29 -0.01 0.22 0.19 0.19 0.30 SQ1 0.40 0.49 0.58 -0.12 0.53 0.49 0.92 0.53 SQ2 0.43 0.49 0.61 -0.14 0.54 0.51 0.94 0.56 SQ3 0.40 0.42 0.55 -0.09 0.49 0.47 0.89 0.51 SQ4 0.44 0.42 0.53 -0.04 0.44 0.50 0.76 0.49 SQ5 0.45 0.55 0.65 -0.13 0.56 0.53 0.93 0.58 SV1 0.51 0.57 0.77 -0.27 0.69 0.45 0.57 0.90 SV2 0.49 0.57 0.79 -0.25 0.69 0.46 0.58 0.90 SV3 0.55 0.60 0.82 -0.31 0.73 0.47 0.56 0.93 SV4 0.49 0.48 0.69 -0.25 0.58 0.40 0.46 0.83 SV5 0.59 0.57 0.80 -0.32 0.68 0.48 0.51 0.91 SV6 0.48 0.51 0.69 -0.10 0.56 0.41 0.46 0.73

An analysis of the cross-loadings provides support for the reflective constructs' discriminant validity as each reflective indicator loads highest on the construct it is linked to.


Fornell-Larcker Criterion SG CB CS PS RP SE SQ SV SG 0.71CB 0.68 0.81CS 0.60 0.68 0.92PS -0.08 -0.10 -0.25 Formative measurement modelRP 0.56 0.60 0.83 -0.19 0.92SE 0.62 0.59 0.54 -0.03 0.62 0.85SQ 0.48 0.54 0.66 -0.12 0.58 0.56 0.89SV 0.60 0.63 0.86 -0.29 0.76 0.51 0.60 0.87The Fornell-Larcker criterion also suggests that the constructs exhibit discriminant validity because the square root of the AVE of each reflective construct is larger that the correlations with the remaining constructs in the model. The square root of AVE values is shown on the diagonal and printed in bold (reflective constructs only); non-diagonal elements are the latent variable correlations. Note that when assessing the Fornell-Larcker criterion in a model that includes a formative construct, one needs to compare the reflective construct's AVE value (more precisely their square root) with all latent variable correlations (i.e., also those of formative constructs).


Results Summary for Reflective Measurement Model

Discriminant validity has been established as the indicator's loading on a construct is higher that all of its cross loadings with other constructs.


Second step: Evaluation of the formative measurement model


Outer Weights Significance Testing Results

Formative Construct (Perceived Sacrifice)

Indicators Outer Weights (Loadings) t Value Significance Level p ValuePS1 0.78 (0.93) 5.41 *** 0.000 PS2 0.35 (0.68) 1.84 * 0.067 Note:* p<0.10** p<0.05*** p<0.01Notes: In the next step, we need to analyse the outer weights for their significance and relevance. We first consider the significance of the outer weights by means of bootstrapping. The default report of the SmartPLS software also provides their outer loadings and t values in the results table for the outer loadings. Using this information, we note that the lowest outer loading of the two indicators has a value of 0.68, and all t values are clearly above 1.84, which indicate the significance of their outer loading (p<0.10). Moreover, theory and field studies provide support for the relevance of these indicators for capturing the perceived sacrifices of service value. Thus, we retain the indicators in the formative constructs even though their outer weights are not significant. The analysis of outer weights concludes the evaluation of the formative measurement models. At this stage, all reflective and formative constructs exhibit satisfactory levels of quality. Thus, we can proceed with the evaluation of the structural model.


Third & final step: Evaluation of the structural model


Total Effects SG CB CS PS RP SE SQ SVSG 0.20 0.17 0.23 CB 0.24 0.21 0.28 CS PS -0.18 -0.16 -0.21 RP SE 0.03 0.02 0.03 SQ 0.26 0.23 0.30 SV 0.88 0.76

Notes: We find the path coefficients as shown. Looking at the relative importance of the exogenous driver constructs for the service value (SV), one finds that the business users' perception of the provider's service quality (SQ) is most important, followed by confidence benefits (CB), and then by service governance (SG). The perceived sacrifices (PS) have a negative relationship while service equity (SE) has very little bearing on service value. More interesting, though, is the examination of total effects. Specifically, we can evaluate how strongly the formative driver construct (PS) ultimately influences the key target variables RP and CS via the mediating construct SV. Here we can see that among the driver constructs, SQ has the strongest effect on SV while SV has the strongest total effect on both CS and RP. Therefore, it is advisable that could service providers focus on marketing and management activities that positively influence the business users' perception of the quality of their services.


Path Coefficients using Bootstrapping

SG -> SV CB -> SV PS-> SV SE -> SV SQ -> SV SV -> CS SV -> RP Sample 0 0.35 0.15 -0.15 0.01 0.36 0.91 0.80 Sample 1 0.23 0.31 -0.22 -0.01 0.31 0.90 0.79 Sample 2 0.24 0.35 -0.18 0.01 0.22 0.89 0.77 Sample 3 0.15 0.29 -0.23 0.03 0.37 0.88 0.78 Sample 4 0.14 0.39 -0.17 0.16 0.17 0.88 0.81 Sample 5 0.26 0.24 -0.24 -0.01 0.28 0.89 0.78 … … … … … … … ... Sample 4999 0.20 0.27 -0.28 -0.04 0.31 0.82 0.67

Notes: By also taking the construct's indicator weights into consideration, we can even identify which specific elements of quality needs to be addressed. Looking at the outer weights reveal that PS1 has the highest outer weight (0.79). This item relates to the survey question "The total cost of securing this cloud service is high." Thus, managers should try to enhance the customer's perception of the cost of their cloud services by means of promotional activities. The analysis of structural model relationships showed that several path coefficients (e.g., SE --> SV) had rather low values. To assess whether such relationships are significant, we run the bootstrapping procedure. The extraction of bootstrapping results for the structural model estimates is analogous to the descriptions in the context of formative measurement model assessment. We use mean replacement in the PLS-SEM algorithm setting and the no sign changes option. As we ran 5,000 cases for the bootstrapping calculation, we are showing only a fraction of the results in the above table. The full table includes the estimates of all the path coefficients for all 5,000 subsamples. As bootstrapping is a random process, please expect that there are different results for every run.


Significance Testing Results Orig Sample (O) Sample Mean (M) Std Deviation (STDEV) Std Error (STERR) T Stat (|O/STERR|)SG-> SV 0.23 0.23 0.06 0.06 3.94 CB->SV 0.28 0.27 0.06 0.06 4.42 PS->SV -0.21 -0.2 0.05 0.05 4.36 SE->SV 0.03 0.04 0.06 0.06 0.57 SQ->SV 0.30 0.30 0.08 0.08 3.99 SV->CS 0.88 0.88 0.02 0.02 52.40 SV->RP 0.76 0.76 0.03 0.03 23.32

Notes: These estimates are used to compute the bootstrapping mean values, standard deviations, standard errors, and t values of all the path coefficient estimates.The user usually reports either the t values and their significance levels or the p value or the confidence intervals (note that the significance levels, p values, and confidence intervals were determined separately because the SmartPLS software does not provide them as output). We find that all relationships in the structural model are significant, except SE-->SV. This result suggest that cloud providers should concentrate their marketing efforts on enhancing their service image . This is not surprising, considering that customers have just recently increased their adoption of cloud services and the brand or name of the service provider is not relevant for now. As cloud computing matures further, this perception might change.


Significance Testing Results of the Total Effects OrigSample (O) Sample Mean (M) Std Deviation (STDEV) Std Error (STERR) T Stat (|O/STERR|)SG->CS 0.20 0.20 0.05 0.05 3.90 SG->RP 0.17 0.17 0.04 0.04 3.89 SG->SV 0.23 0.23 0.06 0.06 3.94 CB->CS 0.24 0.24 0.06 0.06 4.39 CB->RP 0.21 0.21 0.05 0.05 4.32 SB->SV 0.28 0.27 0.06 0.06 4.42 PS->CS -0.18 -0.18 0.04 0.04 4.42 PS->RP -0.16 -0.16 0.03 0.03 4.61 PS->SV -0.21 -0.21 0.05 0.05 4.36 SE->CS 0.03 0.03 0.05 0.05 0.57 SE->RP 0.02 0.03 0.04 0.04 0.57 SE->SV 0.03 0.04 0.06 0.06 0.57 SQ->CS 0.26 0.27 0.07 0.07 3.93 SQ->RP 0.23 0.23 0.06 0.06 3.82 SQ->SV 0.30 0.30 0.08 0.08 3.99 SV->CS 0.88 0.88 0.02 0.02 52.40 SV->RP 0.76 0.76 0.03 0.03 23.32 Shows the corresponding results for the total effects of the exogenous constructs SG, CB, PS, SE, SQ, and SV on the target constructs CS, RP and SV. We find that all relationships in the results are significant except SE-->CS, SE-->RP, and SE-->SV.


Blindfolding Results for the Construct Crossvalidated Redundancy of SV

Blindfolding results for SV

Total SSO SSE 1-SSE/SSOCustomer Satisfaction 1968.00 696.83 0.65 Repurchase Intentions 656.00 351.79 0.46 Service Value 1968.00 1124.06 0.43 In our path model, the predictive relevance Q2 of SV has a value of 0.43, which implies that the model has predictive relevance for this construct. There are further results for the remaining endogenous constructs, CS (0.65) and RP (0.48).


Results of the Q2 and R2 values

Endogenous Latent Variable R2 Value Q2 ValueSV 0.57 0.43RP 0.58 0.48CS 0.77 0.65

CS has the highest predictive relevance among the endogenous latent variables.


Summary of Results

SV RP CS Path coefficients f2 Effect q2 Effect Path f2 q2 Path f2 q2

SQ 0.30 0.13 -0.15SE 0.03 0.00 -0.25CB 0.28 0.08 -0.18PS -0.21 0.10 -0.17SG 0.23 0.05 -0.20SV 0.76 0.13 -0.09 0.88 0.63 0.07RPCSThe final assessments address the f2 and q2 effect sizes. These must be computed manually because the SmartPLS does not provide them. To compute the f2 value of a selected endogenous latent variable, we need the R2(included) and R2(excluded) values. The R2(included) results from the overall model estimation are available from Table 6.26. The R2(excluded) value is obtained from a model reestimation after deleting a specific predecessor of that endogenous latent variable. For example, the endogenous latent variable SV has an original R2 value of 0.57 (R2 included). If SV is deleted from the path model and the model is restimated, the R2 of SV has a value of only 0.43 (R2 excluded). These two values are the inputs for commuting the f2 effect size of of SV. We could see that SE has no effect size while SQ has the greatest effect size among the constructs.


Technology

PhD completion seminar: SerVal in B2B cloud computing