Synopsis

1. Synopsis • Job Hunt (MIS/OM) • What Employees Really Want? • Skill Sets Demanded • Research Pipeline • For Better or For Worse (An Assistant Professor Life) • Research Presentation • General Question

2. Job Hunt (MIS/OM)

3. What Employees Really Want? • It depends… • Depends on what?

4. Skill Sets Demanded • Teaching or Research? Both? • Fit is King! • Teaching • Minimalist vs. High T.E. • Research • State of Art • Innovative • 知己知彼，百战百胜（If you know your enemies and know yourself, you will not be imperiled in a hundred battles.）-Sun Tzu

5. Showcase of Your Skills

6. How to Create Research Pipeline

7. How to Create Research Pipeline?

8. How to Create Research Pipeline?

9. An Assistant Professor Life • Dara, Qiwei, Vicky, and Yang

10. The Effect of Personal and Virtual Word-of-Mouth on Technology Acceptance Mark E. Parry and Qing Cao November 13, 2013

11. Outline Introduction Literature Review Hypotheses Data Collection Methodology Results Discussions and Conclusion

12. Introduction: Studies of Online Reviews • Liu 2006 • 12,1236 reviews of 40 movies over 5 months • Volume significant, but not % of +/- review • Chevalier and Mayzlin (2006) • Amazon and B&N book sales rankings • Average ranking OR 1 and 5 star ratings mattered • Length of reviews mattered • “consumers actually read and respond to written reviews, not merely the average star ranking summary statistic provided by Web sites” • Wang, Xie 2011 • Compared WOM & OL impact on camera sales • Average ranking mattered OR • 1 star, but not 5 star variable significant

13. Introduction: 2 Key Questions • What do reviewers write about? • Does this content influence potential adopters?

14. Literature Review: What Might Reviewers Write About? • Existing research has examined behavioral responses to product consumption or use. These responses are driven by: • Product judgments about quality and value • Cognitive responses to consumption or use • Satisfaction: a judgment about the degree to which the consumption or use or a product or service has pleasurably fulfilled one’s needs, desires, and expectations (Oliver 1999; Oliver 2008) • Trust: “a willingness to rely on an exchange partner in whom one has confidence” (Moorman, Zaltman, and Deshpande 1992, p. 315). • Commitment: “an enduring desire to maintain a valued relationship” (Moorman, Zaltman, and Deshpandé 1992, p. 316).

15. Literature Review: Do Reviewers Write about these Responses? • Research on motivations for sharing WOM (De Angelis et al. 2012; Hennig-Thurau et al. 2004; Sundaram, Mitra, and Webster 1998) • Altruism • Self-enhancement • Helping the firm • Implications: • WOM in general, and product reviews in particular, should provide insight into the information reviewers believe will be helpful to potential adopters. • Reviewers are likely to believe that they information they believe is important will also be perceived as important by potential adopters. • Thus we expect that product reviews will contain information about reviewer perceptions of product quality and value, as well as reviewer expressions of satisfaction, trust, and commitment.

16. Literature Review: Will This Content Affect Potential Adopters? • Innovation adopters rely in part on information gathered from personal communications to make adoption decisions (Graham and Havlena2007; Bickart and Schindler 2001; Engel et al., 1969) • Adoption literature: importance of perceptions of quality and value (Rogers 2003; Davis 1989) • Positive reviewer statements can lower perceived risk (Conchar et al. 2004; Conchar et al. 2004; Bickart and Schindlar 2001; Kirmani and Rao 2000; Duhan et al. 1997; Holak and Lehmann, 1990; Holak and Lehman 1990) • Experience and credence attributes: Positive reviewer statements can be particularly effective when the adoption decision depends on attributes that are hard to assess (Golder et al. 2012; Singh and Sirdeshmukh 2000).

17. Hypotheses • H1: New product adoption is positively related to reviewer statements regarding the perceived quality of an innovation. • H2: New product adoption is positively related to reviewer statements regarding the perceived value of an innovation. • H3: New product adoption is positively related to reviewer statements about the satisfaction or dissatisfaction generated by an innovation. • H4: New product adoption is positively related to reviewer expressions of trust in an innovative product. • H5: New product adoption is positively related to reviewer expressions of commitment to an innovative product.

18. Source: CNET Download.com (www.download.com, abbreviated as CNETD). • Time: May 1 2009 to February 26 2010. • Pretreatment: Blank and duplicated records are deleted and the sentences in each review are normalized (e.g., typos corrected) and stored. • 594,886 sentences from 75,372 reviews covering 216 software products. Data Collection

19. Methodology - Sentiment Analysis • Pretreatment Step • Content Assignment Step • Created a vocabulary based on previous research and expert judgment • Refined with analysis of 500 randomly-selected interviews • Polarity Analysis Step • Used dictionary of sentiment words in the Cornell movie review dataset (http://www.cs.cornell.edu/People/pabo/movie-review-data/). • Widely used in the analysis of other product categories (Pang and Lee, 2004; Prabowo and Thelwall, 2009; Taboada, 2011; Li and Liu, 2012). • Examples of positive sentiment words are enjoy, phenomenal, excellent, and fantastic, while examples of negative words include dislike, bad, terrible, and awful.

20. Vocabulary for Sentiment Analysis

21. Vocabulary for Sentiment Analysis

22. Results - Review Assignment by Dimension

23. Polarity Analysis Results: Sentiment Assignment by Dimension

24. Accuracy of Polarity Analysis

25. Results: Assessment of Accuracy • Quartenary F-measure (content and polarity) • Benchmark: 60% correctly assigned (Pang and Lee 2005) • Assignment in 500 review test sample: 69% correctly assigned • Binary F-measure (polarity only) • Benchmark: 90% correctly assigned (Pang and Lee 2005) • Assignment in 500 review test sample: 89% correctly assigned

26. Results: Distinguishing Positive Sentiments by Focus

29. Variable Definitions

30. Variable Definitions

31. Means and Standard Deviations

32. Fixed Effect Regressions

33. F-Tests for Differences in Regression Coefficients

34. Impact of Computing Sentiment Scores over Longer Time Periods

35. Variable Definitions: Positive Count Variables

36. Means and Standard Deviations (Counts)

37. Fixed Effect Regressions: Count Variables

38. Fixed Effect Regressions: Count Variables (cont.)

39. F-Tests for Differences in Regression Coefficients

40. Academic Implications • Reviewers write about the content examined in this study: • “44% addressed product quality, followed by customer satisfaction (20%) of the review statements, followed by perceived value (15%), trust (13%), and commitment (8%). • These content dimensions influence downloads. • Reviews were much more likely to statements about the new product itself. However, all three types of motivations had a significant impact on software downloads. • Negative statements about new products had a greater impact than positive statements.

41. Managerial Implications • Review content matters (not just about global evaluations) • Tracking of review content (not just volume and polarity) • Enhance perceptions of quality, value and feelings of satisfaction, trust, and commitment • Encourage reviewers to address these issues • Develop strategies to lessen the number of negative statements

42. Directions for Future Research • Generalize to other product categories • Control for price (many downloads are free or free to try) • Analyze sub-dimensions of quality and perhaps other dimensions of review content analyzed in this paper • Antecedents of perceptions/judgments analyzed here • Impact of reviewer claims of expertise • Impact of reader perceptions of homophily

43. General Questions?