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

Job Hunt (MIS/OM)

What Employees Really Want?

• It depends…• Depends on what?

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

Showcase of Your Skills

Skill Sets

Research

Publication Pipeline

Teaching

Tread hold

Service

Minimalist

How to Create Research Pipeline

Research

Streams

Social Media

PURs

WIPs

Supply Chain

Management

PURs

WIPs

How to Create Research Pipeline?

Social Media

PURs

JMKT ISR JOR

WIPs

Review Bias SM and EA Helpfulness

How to Create Research Pipeline?

Supply Chain Management

PURs

JOM POMS

WIPs

Sustainability

An Assistant Professor Life

• Dara, Qiwei, Vicky, and Yang

The Effect of Personal and Virtual Word-of-Mouth on Technology

Acceptance

Mark E. Parry and Qing Cao

November 13, 2013

Outline

1. Introduction2. Literature Review3. Hypotheses4. Data Collection5. Methodology6. Results7. Discussions and Conclusion

11

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

Introduction: 2 Key Questions

• What do reviewers write about?

• Does this content influence potential adopters?

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).

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 firmImplications: • 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.

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 Havlena 2007; 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).

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.

Data Collection • 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.

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.

Variable Vocabulary Satisfaction Satisfied, pleased, contented, happy, delighted, thrilled, ecstatic,

exhilarated, gratified, overjoyed, glad, contentment, satisfaction, wise, wisdom, feel good, feel bad, right thing, happy, not happy, unhappy, regret, excitement, excited, surprise, surpr ising, boredom, boring, sad, sadness, fear, afraid, fulfillment, astute, intelligence, miserable, feel sorry, thrill, shock, dull, dread

Perceived Quality

Quality, excellence, first-rate, superiority, product functionality, work, performance, operate, act, behave; Reliable, consistent, unfailing, reliable, reliability, security, secure, conformance, specs specifications, standards, help, response, support, superior, excellence, excellent, attention, ease of use, easy, versatility, versatile, flexible, flexibility, durability, durable, help, reliability, reliably, performance, function, malfunction, supposed to do, consistent, consistency, consistently, inco nsistency, design, error, dependable, safe, values, superiority, user-friendliness, agile, robustness, functionality

Perceived value

Value, worth, merit, pay, willing to pay, price, payback, money, economical, good buy, bargain, deal, money's worth, reasonable price, high price, cheap, good deal, value, best buy, pricey

Vocabulary for Sentiment Analysis

Variable Vocabulary Trust Trust, believe, count on, have faith in, be sure about, credibility,

believable, plausible, trustworthy, authentic, confidence, credible, trust, rely, be counted on, company meets expectations, company is reliable, company's product quality, worry about company's products, concerned about company's products, trustworthy, rely on, expectations, be anxious

Commitment Faithful, loyal, close, devoted, committed, dedicated, care, first choice, priority, favorite, preferred, liking, fondness, much loved, most wanted, desired, appeal, one of the best firms, proud of this firm, usually agree with this firm, good firm, care about this firm, like this firm, firm understands me, want to help this firm, enjoy doing business with this firm, have a special relationship with this firm, belonging to company, want firm to succeed, committed, favored, attached, beloved, most demanded

Vocabulary for Sentiment Analysis

Results - Review Assignment by Dimension

Dimension Assignment of Reviews a

# of Assignments

% of Assignments

by Dimension

Perceived Quality 40,324 44% Perceived Value 13,823 15% Satisfaction 18,504 20% Trust 11,758 13% Commitment 7,537 8% Total 91,946 100% The 91,946 total assignments of reviews were generated from 75,372 reviews, of which 64,365 were assigned to a single dimension, 6,815, were assigned to two dimensions 2,817 were assigned to three dimensions, and 1,375 were assigned to four dimensions.

Polarity Analysis Results: Sentiment Assignment by Dimension

Dimension Assignment of Review Sentences b # of

Positive Assignm

ents

# of Negative Assignm

ents

Total Number of

Assigned Sentences

% of Assignments

by Dimension

Perceived Quality

198,733 (76%) c

63,856 (24%)

262,589 (100%)

46%

Perceived Value

63,438 (82%)

13,483 (18%)

76,921 (100%)

13%

Satisfaction 104,384 (90%)

11,093 (10%)

115,477 (100%)

20%

Trust 53,234 (83%)

11,274 (17%)

64,508 (100%)

11%

Commitment 41,412 (81%)

9,836 (19%)

51,248 (100%)

9%

Total 461,201 109,542 570,743 100%

Accuracy of Polarity Analysis

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

Results: Distinguishing Positive Sentiments by Focus

Distribution of Positive Sentiments (Sentences)

Dimension Current Product

Past Products

Company Total #

of Positive

Perceived Quality

174,352 (88%)

12,746 (6%)

11,635 (6%)

198,733 (100%)

Perceived Value

56,408 (89%)

3,654 (6%)

3,376 (5%)

63,438 (100%)

Satisfaction 91,043 (87%)

7,237 (7%)

6,104 (6%)

104,384 (100%)

Trust 38,639 (73%)

7,790 (15%)

6,805 (13%)

53,234 (100%)

Commitment 28,473 (69%)

6,805 (16%)

6,134 (15%)

41,412 (100%)

Total 388,915 (84%)

38,232 (8%)

34,054 (7%)

461,201 (100%)

Distribution of Negative Sentiments (Sentences)

Dimension Current Product

Past Products

Company Total #

of Positive

Perceived Quality

49,543 (78%)

7,281 (11%)

7,032 (11%)

63,856 (100%)

Perceived Value

10,343 (77%)

1,649 (12%)

1,491 (11%)

13,483 (100%)

Satisfaction 7,034 (63%)

2,138 (19%)

1,921 (17%)

11,093 (100%)

Trust 6,715 (60%)

2,356 (21%)

2,203 (20%)

11,274 (100%)

Commitment 5,708 (58%)

2,134 (22%)

1,994 (20%)

9,836 (100%)

Total 79,343 (72%)

15,558 (14%)

14,641 (13%)

109,542 (100%)

Overall Positive and Negative Sentiment Share

Dimension Current Product

Past Products

Company Overal

Positive 83% 71% 70% 81% Negative 17% 29% 30% 19%

Variable Description and Measure Dependent Variable lnWeekDownloadi,t Natural log of the average number of daily downloads

for software i at week t. Control Variables lnWeekNumi,t-1 Natural log of the average number of daily user reviews

for software i in week t-1. lnWeekRatingi,t-1 Natural log of the average daily user review rating for

software i in week t-1. lnWeekRanki,t-1 Natural log of the average daily download.com

popularity ranking for software i in week t-1. 5-StarRev%i,t-1 Percentage of five-star reviews for software i in week t-1. 1-StarRev%i,t-1 Percentage of one-star reviews for software i in week t-1.

Variable Definitions

Variable Description and Measure Sentiment Score Variables a Quality-NSi,t-1 Sentiment score of Perceived Quality for new software i in

week t-1. Value-NSi,t-1 Sentiment score of Perceived Value for new software i in week

t-1. Satisfaction-

NSi,t-1 Sentiment score of Customer Satisfaction for new software i in week t-1.

Trust-NSi,t-1 Sentiment score of Trust for new software i in week t-1. Commitment-

NSi,t-1 Sentiment score of Commitment for new software i in week t-1. Quality-PSi,t-1 Sentiment score of Perceived Quality for past software i in

week t-1. Value-PSi,t-1 Sentiment score of Perceived Value for past software i in week

t-1. Quality-COi,t-1 Sentiment score of Perceived Quality for

software company i in week t-1. Value-COi,t-1 Sentiment score of Perceived Value for

software company i in week t-1. Commitment- Sentiment score of Commitment for

Variable Definitions

Variable a N Mean Standard Deviation Minimum Maximum

lnWeekRatingit 9288 3.61 1.61 0.87 7.30 lnWeekNumit 9288 0.76 0.43 0.00 1.65 lnWeekRankit 9288 2.26 1.24 0.05 4.69 1-StarRev%it 9288 0.23 0.11 0.05 0.45 5-StarRev%cit 9288 0.18 0.09 0.03 0.37 Quality-NSit

a 9288 0.52 0.31 -0.50 1.00 Value-NSit 9288 0.54 0.50 -1.00 1.00 Satisfaction-NSit 9288 0.75 0.39 -1.00 1.00 Trust-NSit 9288 0.61 0.43 -1.00 1.00 Commitment-NSit 9288 0.58 0.49 -1.00 1.00 Quality-PSit

b 9288 0.21 0.63 -1.00 1.00 Value-PSit 9288 0.21 0.59 -1.00 1.00 Satisfaction-PSit 9288 0.40 0.58 -1.00 1.00 Trust-PSit 9288 0.41 0.57 -1.00 1.00 Commitment-PSit 9288 0.38 0.60 -1.00 1.00 Quality-COit

b 9288 0.15 0.62 -1.00 1.00 Value-COit 9288 0.18 0.61 -1.00 1.00 Satisfaction-COit 9288 0.36 0.60 -1.00 1.00 Trust-COit 9288 0.38 0.60 -1.00 1.00 Commitment-COit 9288 0.36 0.60 -1.00 1.00

Means and Standard Deviations

Fixed Effect Regressions

Model 1 Model 2 Model 3 Model 4 Variable a β t β t β t β t lnWeekNumit 0.20 2.23* 0.18 2.12* 0.14 1.75 0.09 1.31

lnWeekRatingit -0.41 -1.11 -0.33 -0.94 -0.50 -1.53 -0.32 -1.07

lnWeekRankit -0.02 -0.13 0.03 0.24 0.00 0.03 0.05 0.44

1-StarRev%it -3.48 -2.56* -2.80 -2.18* -2.02 -1.68 -1.97 -1.80

5-StarRev%it 0.78 0.47 -0.01 -0.01 -0.60 -0.41 -0.26 -0.20

Quality-NSit a 0.63 14.40*** 0.61 15.01*** 0.62 16.79***

Value-NSit 0.37 14.38*** 0.37 15.28*** 0.38 17.30***

Satisfaction-NSit 0.31 9.51*** 0.31 10.10*** 0.32 11.59***

Trust-NSit 0.47 15.42*** 0.47 16.58*** 0.48 18.69***

Commitment-NSit 0.52 18.75*** 0.52 19.99*** 0.50 20.94***

Quality-PSit b 0.59 29.43*** 0.59 32.24***

Value-PSit 0.27 11.47*** 0.25 11.87***

Satisfaction-PSit 0.28 13.16*** 0.27 13.66***

Trust-PSit 0.16 7.11*** 0.14 7.17***

Commitment-PSit 0.21 10.19*** 0.20 10.47***

Quality-COit b 0.65 36.79***

Value-COit 0.25 10.98***

Satisfaction-COit 0.25 13.17***

Trust-COit 0.24 12.38***

Commitment-COit 0.15 7.92***

R2 0.29 0.37 0.45 0.54

F-Statistic 14.05*** 19.51*** 26.84*** 38.76***

Model 2: New-Software Variables

Value Satisfaction Trust Commitment Quality 25.31*** b 33.08*** 9.24** 3.89 Value 1.82 5.89** 16.55*** Satisfy 11.82*** 23.25*** Trust 1.94 Model 3: Past-Software

Variables

Value Satisfaction Trust Commitment Quality 106.93*** 107.53*** 209.32*** 171.59*** Value 0.19 12.34*** 3.17 Satisfy 16.55*** 5.45*** Trust 3.45 Model 4: Company Variables Value Satisfaction Trust Commitment Quality 189.98*** 235.37*** 245.44*** 374.08*** Value 0.00 0.15 11.46*** Satisfy 0.15 13.96*** Trust 10.86***

F-Tests for Differences in Regression Coefficients

Impact of Computing Sentiment Scores over Longer Time Periods

1 Week 2 Weeks 3 Weeks 4 Weeks 5 Weeks Variable a β t β t β t β t β t lnWeekNumit 0.18 2.12* 0.18 2.12 0.18 2.00 0.18 2.07 0.19 2.14

lnWeekRatingit -0.33 -0.94 -0.29 -0.80 -0.37 -1.01 -0.38 -1.05 -0.37 -1.02

lnWeekRankit 0.03 0.24 0.01 0.11 0.01 0.09 0.01 0.08 0.02 0.12

1-StarRev%it -2.80 -2.18* -3.05 -2.32 -3.07 -2.31 -3.10 -2.32 -3.26 -2.42

5-StarRev%it -0.01 -0.01 -0.09 -0.05 0.44 0.27 0.41 0.25 0.50 0.30

Quality-NSit a 0.63 14.40*** 0.73 10.65*** 0.75 8.39*** 0.70 6.53*** 0.73 5.85***

Value-NSit 0.37 14.38*** 0.46 9.20*** 0.61 7.89*** 0.77 7.68*** 0.76 6.44***

Satisfaction-NSit 0.31 9.51*** 0.58 7.05*** 0.90 6.61*** 0.77 4.32*** 0.76 3.62***

Trust-NSit 0.47 15.42*** 0.71 11.89*** 0.86 10.21*** 0.82 7.86*** 0.86 7.08***

Commitment-NSit 0.52 18.75*** 0.68 13.12*** 0.84 11.41*** 0.90 9.73*** 0.83 7.66***

R-Square 0.37 0.33 0.32 0.31 0.31 F-Statistic 19.51*** 16.79*** 16.00*** 15.30*** 14.91*** Notes: The letter β denotes regression coefficients and t denotes t -statistics. A * denotes p < 0.05, ** denotes p < 0.01, and ***

denotes p < 0.001. a In the sentiment variable labels, NS denotes sentiments about “New Software” (i.e., the software being reviewed), while PS

denotes sentiments about “Past Software” from the same company and CO denotes sentiments about the company. All independent variables are lagged one week relative to the dependent variable.

Variable Description and Measure Positive Sentiment Count Variables a Quality-NS-

Pi,t-1 Perceived Quality positive sentiment counts for new software i in week t-1.

Value-NS-P i,t-1 Perceived Value positive sentiment counts for new software i in week t-1.

Satisfaction-NS-P i,t-1

Customer Satisfaction positive sentiment counts for new software i in week t-1.

Trust-NS-P i,t-1 Trust positive sentiment counts for new software i in week t-1. Commitment-

NS-P i,t-1 Commitment positive sentiment counts for new software i in week t-1.

Quality-PS-P

i,t-1 Perceived Quality for past software i in week t-1. Value-PS-P i,t-1 Perceived Value for past software i in week t-1. Satisfaction-

PS-P i,t-11 Satisfaction for past software i in week t-1. Trust-PS-P i,t-1 Trust for past software i in week t-1. Commitment-

PS-P i,t-1 Commitment for past software i in week t-1. Quality-CO-P Perceived Quality for software company i in week t-1.

Variable Definitions: Positive Count Variables

Means and Standard Deviations (Counts)Variable a N Mean Std. Deviation Minimum Maximum Positive Count Variables Quality-NS-Pi,t-1 9288 18.77 8.63 4.00 34.00 Value-NS-P i,t-1 9288 6.07 3.75 0.00 13.00 Satisfaction-NS-P i,t-1 9288 9.69 5.86 0.00 21.00 Trust-NS-P i,t-1 9288 4.16 2.35 0.00 8.00 Commitment-NS-P i,t-1 9288 3.07 1.78 0.00 7.00 Quality-PS-Pi,t-1 9288 1.37 0.94 0.00 3.00 Value-PS-Pi,t-1 9288 0.39 0.54 0.00 3.00 Satisfaction-PS-Pi,t-1 9288 0.78 0.81 0.00 4.00 Trust-PS-Pi,t-1 9288 0.84 0.87 0.00 4.00 Commitment-PS-Pi,t-1 9288 0.73 0.77 0.00 4.00 Quality-CO-Pi,t-1 9288 1.24 1.07 0.00 4.00 Value-CO-Pi,t-1 9288 0.36 0.64 0.00 5.00 Satisfaction-CO-Pi,t-1 9288 0.66 0.75 0.00 5.00 Trust-CO-Pi,t-1 9288 0.73 0.80 0.00 5.00 Commitment-CO-Pi,t-1 9288 0.66 0.74 0.00 5.00 Negative Count Variables Quality-NS-Ni,t-1 9288 5.33 3.11 0.00 27.00 Value-NS-Ni,t-1 9288 1.11 0.78 0.00 7.00 Satisfaction-NS-Ni,t-1 9288 0.76 0.94 0.00 16.00 Trust-NS-Ni,t-1 9288 0.72 0.63 0.00 7.00 Commitment-NS-Ni,t-1 9288 0.61 0.53 0.00 4.00 Quality-PS-Ni,t-1 9288 0.78 0.63 0.00 2.00 Value-PS-Ni,t-1 9288 0.18 0.42 0.00 3.00 Satisfaction-PS-Ni,t-1 9288 0.23 0.46 0.00 3.00 Trust-PS-Ni,t-1 9288 0.25 0.49 0.00 4.00 Commitment-PS-Ni,t-1 9288 0.23 0.47 0.00 4.00 Quality-CO-Ni,t-1 9288 0.76 0.62 0.00 4.00 Value-CO-Ni,t-1 9288 0.16 0.40 0.00 4.00

Model 1 Model 5 Model 6 Model 7 Variable a β t β t β t β t WeekRatingi,t-1 0.20 2.23* 0.15 1.81 0.10 1.58 0.08 1.17 WeekNumi,t-1 -0.41 -1.11 -0.30 -0.88 -0.47 -1.36 -0.53 -1.88 WeekRanki,t-1 -0.02 -0.13 0.05 0.41 0.01 0.29 0.06 0.62 1-StarRevPerci,t-1 -3.48 -2.56* -2.72 -2.22* -2.37 -1.91 -2.33 -2.27* 5-StarRevPerci,t-1 0.78 0.47 -0.07 -0.05 -0.09 0.13 0.19 0.15 Positive Count Variables Quality-NS-Pi,t-1

a 0.00 3.38*** 0.00 3.68*** 0.00 3.83*** Value-NS-Pi,t-1 0.02 6.96*** 0.02 7.50*** 0.02 8.11*** Satisfaction-NS-Pi,t-1 0.01 3.52*** 0.01 4.13*** 0.01 5.01*** Trust-NS-Pi,t-1 0.02 3.04*** 0.02 3.77*** 0.02 5.02*** Commitment-NS-

Pi,t-1

0.04 5.52*** 0.04 5.94*** 0.03 5.74***

Quality-PS-Pi,t-1b 0.12 9.69*** 0.12 10.73***

Value-PS-Pi,t-1 0.34 13.77*** 0.33 14.46*** Satisfaction-PS-Pi,t-1 0.21 11.84*** 0.22 13.61*** Trust-PS-Pi,t-1 0.11 6.22*** 0.10 6.23*** Commitment-PS-Pi,t-

1

0.18 10.04*** 0.18 11.17***

Quality-CO-Pi,t-1b 0.16 12.58***

Value-CO-Pi,t-1 0.28 12.20*** Satisfaction-CO-Pi,t-1 0.23 13.76*** Trust-CO-Pi,t-1 0.21 13.24*** Commitment-CO-

Pi,t-1

0.12 7.44***

Fixed Effect Regressions: Count Variables

Fixed Effect Regressions: Count Variables (cont.) Model I Model II Model III Model IV

Variable a β t β t β t β t Negative Count Variables

Quality-NS-Ni,t-1 -0.07 -15.26*** -0.06 -16.15*** -0.07 -18.23***

Value-Ni,t-1 -0.33 -17.94*** -0.32 -19.00*** -0.33 -21.34*** Satisfaction-Ni,t-1 -0.29 -13.42*** -0.28 -14.15*** -0.28 -15.71*** Trust-Ni,t-1 -0.49 -19.87*** -0.48 -20.99*** -0.49 -23.54*** Commitment-NS-Ni,t-1 -0.77 -25.62*** -0.76 -27.27*** -0.73 -29.10*** Quality-PS-Ni,t-1

b -0.68 -30.73 -0.68 -33.56*** Quality-CO-Ni,t-1

c -0.74 -35.68*** Value-CO-Ni,t-1 -0.14 -4.08*** R2 0.29 0.42 0.51 0.60

F-Statistic 14.50*** 24.41*** 32.61*** 45.73*** Notes: The letter β denotes regression coefficients and t denotes t-statistics. A * denotes p < 0.05, ** denotes

p < 0.01, and *** denotes p < 0.001. a In the variable labels, NS-P denotes positive sentiments about “New Software” (i.e., the software being

reviewed), while PS-P denotes positive sentiments about “Past Software” from the same company and CO-P denotes positive sentiments about the company itself. Similarly, NS-N denotes negative sentiments about “New Software,” while PS-N denotes negative sentiments about “Past Software” from the same company and CO-NP denotes negative sentiments about the company. All independent variables are lagged one week relative to the dependent variable.

b Only one of the five negative-count sentiment variables referring to past products was statistically significant.

c Only two of the five negative-count sentiment variables referring to the company were statistically significant.

F-Tests for Differences in Regression Coefficients

Model 2: New-Software Variables (NS) Quality Value Satisfaction Trust Commit Quality 236.56*** b 25.49*** b 1.09 4.30* 22.31*** Value 194.20*** 354.05*** 15.68*** 1.19 4.19* Satisfy 102.58*** 2.10 186.21** 2.31 18.15*** Trust 285.98*** 26.87*** 36.32*** 401.4*** 6.64** Commitment 535.76*** 154.95*** 160.79*** 48.41*** 684.67*** Model 3: Past-Software Variables (PS) Quality Value Satisfaction Trust Commit Quality 996.62*** 63.11*** 18.40*** 0.12 7.77** Value 206.52*** 66.07*** 17.32*** 53.29 26.75*** Satisfy 306.22*** 1.12 26.66** 15.15*** 1.65 Trust 315.20*** 1.14 0.00 6.48** 7.00** Commitment 269.70*** 0.04 0.80 0.83 28.86*** Model 4: Company Variables (CO) Quality Value Satisfaction Trust Commit Quality 1378.46*** 22.60*** 12.87*** 7.87** 2.34 Value 212.66*** 95.61*** 2.95 5.89* 29.86*** Satisfy 361.77*** 8.45*** 39.94** 0.67 20.21*** Trust 380.98*** 6.73** 0.18 44.82*** 14.39*** Commitment 341.03*** 4.80* 0.53 0.10 19.47*** Notes: a Table entries are F -statistics testing the equality of the coefficients of two variables and are

based on model estimates reported in Table 11. b The F-statistics in the upper -left to lower-right diagonal test the hypotheses the coefficient of a

positive count variable is equal to the coefficient of the corresponding negative count variable. For example, the first entry in the diagonal (996.62) tes ts the hypothesis that the coefficients of Quality-NS-Pi,t-1 and Quality-NS-Ni,t-1 are equal. A * denotes p < 0.05, ** denotes p < 0.01, and *** denotes p < 0.001.

c Table entries above (below) the upper -left to lower -right diagonal compare the magnitudes of the coefficients of the positive (negative) count variables. For example, the F -statistic in the first row, second column (25.49) tests the hypothesis that the co efficients of Quality-NS-Pi,t-1 and Value-NS-Pi,t-1 are equal, while the F-statistic in the third row, first column (212.66) tests

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.

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

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

General Questions?