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Measurement, Meaning and Consequences of Satisfaction

Measurement, Meaning and Consequences of .com Satisfaction. Qimei Chen. Introduction. Fast growth of Internet usage Exponential increase of e-commerce Lack of consensus definition of online satisfaction Lack of standard , affordable and accurate measure of online consumer satisfaction.

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Measurement, Meaning and Consequences of Satisfaction

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  1. Measurement, Meaning and Consequences of .com Satisfaction Qimei Chen

  2. Introduction • Fast growth of Internet usage • Exponential increase of e-commerce • Lack of consensus definition of online satisfaction • Lack of standard,affordableandaccuratemeasure of online consumer satisfaction

  3. Research Questions 1.Is the two-factor .com Satisfaction|Dissatisfaction approach significantly better than the traditional one-factor approach?

  4. Research Questions 2. What are the major facets of .com Satisfaction and .com Dissatisfaction?

  5. Research Questions 3. Do .com Satisfaction|Dissatisfaction facets provide more information than the summated .com Satisfaction and .com Dissatisfaction scales?

  6. Research Questions 4. Is attitude toward the site a mediating variable between satisfaction and behavioral intentions?

  7. Research Questions 5. What variables moderate the relationship between attitude toward the site and behavioral intentions?

  8. Research Questions 6. Does the two-factor .com Satisfaction|Dissatisfaction approach perform significantly better than the traditional one-factor approach in the Expectancy-Disconfirmation with Performance model?

  9. Theoretical Background • Traditional Satisfaction Concept Satisfaction Dissatisfaction

  10. Theoretical Background • Herzberg’s Two-Factor Theory • Motivators Satisfiers • Hygienes Maintainers

  11. Two-factor .com Satisfaction|Dissatisfaction Concept .com Satisfaction Lack of .com Dissatisfaction Lack of .com Satisfaction .com Dissatisfaction

  12. Data Collection Processes Literature Review • Identify initial item pool based on earlier literature

  13. Data Collection Processes Depth Interviews (Web designers) • Supplement initial item pool; generate initial .com satisfaction|dissatisfaction model

  14. Data Collection Processes Pilot Survey • Purify the .com sastisfaction|dissatisfaction instrument • Cross-checking the final .com satisfaction|dissatisfaction instrument (questionnaire) with • Depth Interviews (Web users) • Informal Survey of Industry Literature

  15. Data Collection Processes Main Study • Confirm the .com satisfaction|dissatisfaction instrument; test competing models and test moderating effects of control variables

  16. Data Collection Processes • Main Study—Respondents • Three sources • Students enrolled in SJMC and IDSc • Adults referred by student participants • Respondents recruited via Service Quality Institute Listserv mailing list • 697 responses (33 were dropped)

  17. Data Collection Processes • Main Study—Web Sites • Half of the respondents were directed to name an e-commerce site they had positive experience with • Half of the respondents were directed to name an e-commerce site they has negative experience with

  18. Findings (R1) 1. Is the two-factor .com Satisfaction|Dissatisfaction approach significantly better than the traditional one-factor approach? • Tests of Semi-Independency • Tests of Competing Models • Relationships with Specific Behavioral Intentions.

  19. Findings (R1) Tests of Semi-Independency • .com Satisfaction and .com Dissatisfaction are semi-independent • .com S/D is the overlapping part of .com Satisfaction and Dissatisfaction

  20. Findings (R1) Tests of Competing Models

  21. Competing Model 1 .04 .21** .67** Traditional Satisfaction Attitude Behavioral Intention Adjusted R2=.313 Adjusted R2=.118

  22. Competing Model 2 .com Satisfaction .42** .44** .65** Attitude Behavioral Intention -.26** .com Dissatisfaction -.41** Adjusted R2=.118 Adjusted R2=.313 Adjusted R2=.477 Adjusted R2=.421

  23. Competing Model 3 .com Satisfaction .51** .50** .46** Attitude Behavioral Intention .19** .com Satisfaction -.32** .com Dissatisfaction -.36** Adjusted R2=.118 Adjusted R2=.313 Adjusted R2=.421 Adjusted R2=.477 Adjusted R2=.479 Adjusted R2=.436

  24. Findings (R1) Relationships with Specific Behavioral Intentions • .com Satisfaction correlates most significantly with specific positive behavioral intentions • .com Dissatisfaction correlates most significantly with specific negative behavioral intentions

  25. Therefore… • The two-factor .com Satisfaction|Dissatisfaction approach is significantly better than the traditional one-factor approach.

  26. .com Satisfaction .com Dissatisfaction • Positive Unipolars • Attractive • Forgiving • Sense of Community • Flexible • Personalizable • Responsive • Bricks parallel clicks • Considerate • Bipolars • Organization • Service Quality • Simplicity • Accuracy • Negative Unipolars • Difficult to use • Cheap looking • Deceptive • Complicated • Violates privacy • Inconvenient • Violates design norms Findings (R2) 2. What are the major facets of .com Satisfaction and .com Dissatisfaction?

  27. Findings (R3) 3. Do .com Satisfaction|Dissatisfaction facets provide more information than the summated .com Satisfaction and .com Dissatisfaction scales? • Regression analysis • Bivariate correlation analysis

  28. Attitude Behavioral Intention Findings (R3) Regression analysis • facets account for more variance than summated scales in explaining attitudes and behavioral intentions All Facets Adjusted R2=.521 Adjusted R2=.446 Adjusted R2=.118 Adjusted R2=.313 Adjusted R2=.421 Adjusted R2=.477 Adjusted R2=.479 Adjusted R2=.436

  29. Findings (R3) Bivariate correlation analysis • facets offer more informative and meaningful associations with specific behavioral intentions

  30. Findings (R3) Snapshot of some findings I would like to visit this Web site again in the future Top Significant Correlations Service Quality Simplicity Accuracy Attractive Organization Bricks parallel Clicks

  31. Findings (R3) Snapshot of some findings I might send an email to express my appreciation Top Significant Correlations Sense of Community Responsive Attractive Service Quality Personalizable

  32. Findings (R3) Snapshot of some findings I might convince my friends not to use this Web site Top Significant Correlations Deceptive Violates Design Norms Violates Privacy Cheap Looking Complicated Difficult to Use

  33. Therefore… • .com Satisfaction|Dissatisfaction facets do provide more information than the summated .com Satisfaction and .com Dissatisfaction scales.

  34. Findings (R4) 4. Is attitude toward the site a mediating variable between satisfaction and behavioral intentions? • 3-step Least-squares multiple regression analysis • com Satisfaction and .com Dissatisfaction (partial mediation) are more important predictors of behavioral intentions than Traditional Satisfaction (full mediation).

  35. Findings (R5) 5. What variables moderate the relationship between attitude toward the site and behavioral intentions? • Moderated Multiple Regression Analyses • Brand Equity • Monopoly • Involvement • Self-Efficacy • Internet Efficacy • Online Shopping Efficacy

  36. Moderating Variable Test

  37. Moderating Variable Test

  38. Findings (R6) 6. Does the two-factor .com Satisfaction|Dissatisfaction approach perform significantly better than the traditional one-factor approach in the Expectancy-Disconfirmation with Performance model? • Path Analyses

  39. Expectations .com Satisfaction Calculated Disconfirmation Subjective Disconfirmation Performance Outcomes .com Dissatisfaction Attitude Behavioral Intention Behavior Findings (R6) Antecedents of .com S|DS Consequences of .com S|DS .com S|DS

  40. Findings (R6) • Expectancy Disconfirmation with Performance Model holds true in the e-commerce domain • Treating .com Satisfaction and .com Dissatisfaction as partially independent constructs increases model fit • The two-factor .com Satisfaction|Dissatisfaction approach yields more meaningful associations with antecedent variables

  41. Theoretical Implications • Produced an instrument that can be used in future theoretically-oriented studies • Proves that treating .com Satisfaction and .com Dissatisfaction as partially independent concepts increases explanatory power • Shows that facet level analysis reveals important information • Indicates that Expectancy-Disconfirmation with Performance model works well in e-commerce domain • Enriches marketing theory by introducing insights from the MIS and job satisfaction arenas

  42. Managerial Implications • The instrument • Reliable, comprehensive, affordable and easy-to-apply • Uses • Cost-Benefit Analysis • Competitive Analysis • Longitudinal Analysis

  43. Managerial Implications • Moderating Variables • Monopoly • Involvement

  44. Suggestion for Future Studies • Other kinds of Web Sites • .gov • .edu • Other kinds of satisfaction in consumer research • Brick-mortar settings (travel, banking) • Other domains of satisfaction • Student satisfaction • Patient satisfaction • Communication • Organization behavior

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