Question 2: NILE

NILE is the dominant market force in digital marketing. Not only have they established the most visited E-commerce platform (nile.com), they have also developed a cloud and payment architecture supporting their overall strategic performance. One of their expert teams is working on the development and marketing of an autonomous vehicle (AV) for private and business use. The prototype was named NX01. In order to assess the adoption of NX01 by users a NILE expert team used the theory of Planned Behavior (See Exhibit 5, Panel A). The measurement instruments for the theory of Planned Behavior are included in Exhibit 5, Panel B. Apart from the measures in Exhibit 5 gender [GENDER], age [AGE], driving experience [EXPERIENCE] and digital experience [DIGITAL] were recorded. In total, 107 “early adopter” respondents participated in the pilot study. All respondents received a prototype NX01 and were allowed to use it freely. Behavior was operationalized as the (average) hours per week the respondents used NX01 (log files) in the 6 week test period.

Panel B: Measures for NX01 Study

Attitude

Using an autonomous vehicle (AV) is a good idea [ATT01]. a

I like the idea of using an autonomous vehicle (AV) [ATT02]. a

Using an autonomous vehicle (AV) is likely to be a pleasant experience [ATT03]. a

Social Norm

Individuals who influence my behavior indicate that I should use an autonomous vehicle (AV) [SN01]. a

Individuals whose opinions I respect feel that I should use an autonomous vehicle (AV) [SN02]. a

Individuals who are important to me think that I should use an autonomous vehicle (AV) [SN03]. a

Planned Behavioral Control (PBC)

I have the knowledge necessary to use an autonomous vehicle (AV) [PBC01]. a

I am confident that I can control an autonomous vehicle (AV) [PBC02]. a

It would be easy for me to control an autonomous vehicle (AV) [PBC03]. a

Behavioral Intention (BI)

If autonomous vehicles (AV) would be available I would use them [BI01]. b

In future, I would use an autonomous vehicle (AV) if they were available [BI02]. b

I intend to use an autonomous vehicle (AV) if they were available [BI03]. b

Behavior

The (average) hours per week the respondents used NX01 on the basis of the log files.

What is you gender? [GENDER]

1 Male 2 Female

What is your age? [AGE]

1 18-24 6 45-49

2 25-29 7 50-54

3 30-34 8 55-59

4 35-39 9 60-64

5 40-44 10 ≥ 65

How many years of driving experience do have? [EXPERIENCE]

_____ years

What is level of your digital experience? [DIGITAL]

Very inexperienced Very experienced

1 2 3 4 5 6 7

References

Mathieson, K. (1991). Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior. Information Systems Research, 2(3), 173-191.

Morris, M. G., Venkatesh, V., & Ackerman, P. L. (2005). Gender and Age Differences in Employee Decisions about New Technology: An Extension to the Theory of Planned Behavior. IEEE Transactions on Engineering Management, 52(1), 69-84.

a Measured on a 7-point Likert scale (1= Completely disagree; 7= Completely agree).

b Measured on a 7-point Likert scale (1= Very unlikely; 7= Very likely).

(a) The NILE expert team decided to use principal axis factor analysis (PAF) to determine unidimensionality and (factorial) validity of the measures used for attitude, social norm, perceived behavioral control and behavioral intention. The SPSS output for the principal axis factor analysis (PAF; Panel A) and Parallel Analysis (Panel B) are included in Exhibit 6. Explain the rationale for using PAF as opposed to principal components (PC) analysis for this analysis, interpret the SPSS output, and discuss the findings and implications using the SPSS output in Exhibit 6. You may assume a significance level of 5% (α=0.05)

(b) In the next stage of the analysis the NILE expert team used hierarchical OLS regression analysis to explore how attitude, social norm and perceived behavioral control affect behavioral intention using gender [GENDER], age [AGE], driving experience [EXPERIENCE] and digital experience [DIGITAL] as control variables. Mean scores were calculated for attitude, social norm, perceived behavioral control and behavioral intention (See Exhibit 5, Panel B). The output from the SPSS regression analysis is included in Exhibit 7. Assess the assumptions for the analysis, formulate the null and alternative hypotheses for the relevant tests, interpret the SPSS output, discuss the findings and implications using the SPSS output in Exhibit 7. Refer to specific elements in the output you have used to reach your conclusions. You may assume a significance level of 5% (α=0.05)

(c) In the conceptual model in Exhibit 5, Panel A, behavioral intention mediates the relationships with attitude, social norm and perceived behavioral control and behavior. Mean scores were calculated for attitude, social norm, perceived behavioral control and behavioral intention (See Exhibit 5, Panel B). The SPSS output for mediation testing using the PROCESS macro is included in Exhibit 8. Formulate the null and alternative hypotheses for the relevant tests, interpret the SPSS output, and discuss the findings and implications using the SPSS output in Exhibit 8. Refer to specific elements in the output you have used to reach your conclusions. You may assume a significance level of 5% (α=0.05)

(d) The NILE expert team assumes that personality traits might affect the findings of the study. In order to assess the potential impact of personality trait a five factor personality inventory was used. The respondents of the pilot study completed the five factor personality inventory consisting of 4 items for each factor: (1) openness, (2) conscientiousness, (3) extraversion, (4) agreeableness and (5) neuroticism. Factor analysis and reliability analysis confirmed (factorial) validity and reliability. Mean scores were calculated for each factor and used as input for a two-stage cluster analysis as suggested by Punj And Stewart (1983). Describe how a two-stage approach to cluster analysis is conducted and what the advantages and disadvantages are of the cluster procedures making up the two-stage procedure to cluster analysis.