Measuring Customer Agility from Online Reviews Using Big Data Text Analytics

Abstract Large volumes of product reviews generated by online users have important strategic value for product development. Prior studies often focus on the influence of reviews on customers‘ purchasing decisions through the word-of-mouth effect. However, little is known about how product developers respond to these reviews. This study adopts a big data analytical approach to investigate the impact of online customer reviews on customer agility and subsequently product performance. We develop a singular value decomposition-based semantic keyword similarity method to quantify customer agility using large-scale customer review texts and product release notes. Using a mobile app data set with over 3 million online reviews, our empirical study finds that review volume has a curvilinear relationship with customer agility. Furthermore, customer agility has a curvilinear relationship with product performance. Our study contributes to innovation literature by demonstrating the influence of firms‘ capability of utilizing online customer reviews and its impact on product performance. It also helps reconcile inconsistencies found in literature regarding the relationships among the three constructs.

[1]  S. Ogawa,et al.  User-Generated Versus Designer-Generated Products: A Performance Assessment at Muji , 2012 .

[2]  S. Zahra,et al.  Absorptive Capacity: A Review, Reconceptualization, and Extension , 2002 .

[3]  B. Menguc,et al.  Customer and Supplier Involvement in Design: The Moderating Role of Incremental and Radical Innovation Capability , 2014 .

[4]  Hrushikesha Mohanty,et al.  Big Data: An Introduction , 2015 .

[5]  Alain Pinsonneault,et al.  Survey Research Methodology in Management Information Systems: An Assessment , 1993, J. Manag. Inf. Syst..

[6]  J. Alberto Espinosa,et al.  Big Data: Issues and Challenges Moving Forward , 2013, 2013 46th Hawaii International Conference on System Sciences.

[7]  Bin Gu,et al.  Do online reviews matter? - An empirical investigation of panel data , 2008, Decis. Support Syst..

[8]  Ralph Katz,et al.  Shifting Innovation to Users via Toolkits , 2002, Manag. Sci..

[9]  Hock-Hai Teo,et al.  Comparing Potential and Actual Innovators: An Empirical Study of Mobile Data Services Innovation , 2015, MIS Q..

[10]  Eric R. Ziegel,et al.  Survey Errors and Survey Costs , 1990 .

[11]  Robert D. Austin,et al.  Research Commentary - Weighing the Benefits and Costs of Flexibility in Making Software: Toward a Contingency Theory of the Determinants of Development Process Design , 2009, Inf. Syst. Res..

[12]  Anindya Ghose,et al.  Towards a Digital Attribution Model: Measuring the Impact of Display Advertising on Online Consumer Behavior , 2015 .

[13]  Daniel A. Levinthal,et al.  The myopia of learning , 1993 .

[14]  Arjun Mukherjee,et al.  Analyzing and Detecting Opinion Spam on a Large-scale Dataset via Temporal and Spatial Patterns , 2015, ICWSM.

[15]  Rahul Telang,et al.  Inferring App Demand from Publicly Available Data , 2013, MIS Q..

[16]  FininTim,et al.  Robust semantic text similarity using LSA, machine learning, and linguistic resources , 2016, LREC 2016.

[17]  E. Hippel.  Sticky Information and the Locus of Problem Solving: Implications for Innovation , 1994 .

[18]  Viswanath Venkatesh,et al.  A Control Theory Perspective on Agile Methodology Use and Changing User Requirements , 2009, Inf. Syst. Res..

[19]  John Cullen,et al.  Democratizing Innovation , 2020, Encyclopedia of Creativity, Invention, Innovation and Entrepreneurship.

[20]  Jorge A. Colazo.  Performance implications of stage-wise lead user participation in software development problem solving , 2014, Decis. Support Syst..

[21]  Andrew Whinston,et al.  The Dynamics of Online Word-of-Mouth and Product Sales: An Empirical Investigation of the Movie Industry , 2008 .

[22]  Mohammad Salehan,et al.  Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics , 2014, Decis. Support Syst..

[23]  Scott M. Carr,et al.  Pricing Software Upgrades: The Role of Product Improvement and User Costs , 2009 .

[24]  Allan Afuah,et al.  Users as Innovators: A Review, Critique, and Future Research Directions , 2009 .

[25]  R. G. Fichman,et al.  Digital Innovation as a Fundamental and Powerful Concept in the Information Systems Curriculum , 2014, MIS Q..

[26]  Prasanna Tambe,et al.  The Extroverted Firm: How External Information Practices Affect Innovation and Productivity , 2012, Manag. Sci..

[27]  Jackie Rees Ulmer,et al.  Competing for Attention: An Empirical Study of Online Reviewers' Strategic Behavior , 2015, MIS Q..

[28]  S. Zeger,et al.  Longitudinal data analysis using generalized linear models , 1986 .

[29]  Alexander Dekhtyar,et al.  Information Retrieval , 2018, Lecture Notes in Computer Science.

[30]  Ying Lu,et al.  Understanding the Link Between Information Technology Capability and Organizational Agility: An Empirical Examination , 2011, MIS Q..

[31]  H. Peter Hofstee,et al.  Big Data text-oriented benchmark creation for Hadoop , 2013, IBM J. Res. Dev..

[32]  Juan Enrique Ramos,et al.  Using TF-IDF to Determine Word Relevance in Document Queries , 2003 .

[33]  Martin Ester,et al.  Detecting Singleton Review Spammers Using Semantic Similarity , 2015, WWW.

[34]  X. Zhang,et al.  Impact of Online Consumer Reviews on Sales: The Moderating Role of Product and Consumer Characteristics , 2010 .

[35]  David Schuff,et al.  What Makes a Helpful Review? A Study of Customer Reviews on Amazon.com , 2010 .

[36]  Christoph Fuchs,et al.  The Innovation Effect of User Design: Exploring Consumers’ Innovation Perceptions of Firms Selling Products Designed by Users , 2012 .

[37]  D. Lei,et al.  Collaborative Innovation with Customers: A Review of the Literature and Suggestions for Future Research , 2012 .

[38]  Martin Schreier,et al.  The Value of Crowdsourcing: Can Users Really Compete with Professionals in Generating New Product Ideas? , 2009 .

[39]  D. Leonard-Barton.  CORE CAPABILITIES AND CORE RIGIDITIES: A PARADOX IN MANAGING NEW PRODUCT DEVELOPMENT , 1992 .

[40]  David M. W. Powers,et al.  Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.

[41]  Foster J. Provost,et al.  Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics , 2016, MIS Q..

[42]  Thomas Kramer.  The Effect of Measurement Task Transparency on Preference Construction and Evaluations of Personalized Recommendations , 2006 .

[43]  Sali Li,et al.  Insights and New Directions from Demand-Side Approaches to Technology Innovation, Entrepreneurship, and Strategic Management Research , 2012 .

[44]  Insu Park,et al.  Using Big Data to Model Time-Varying Effects for Marketing Resource (Re)Allocation , 2016, MIS Q..

[45]  Thomas Y. Lee,et al.  Needs-based analysis of online customer reviews , 2007, ICEC.

[46]  Anindya Ghose,et al.  Towards a Digital Attribution Model: Measuring the Impact of Display Advertising on Online Consumer Behavior , 2015 .

[47]  Rick Kazman,et al.  How Lufthansa Capitalized on Big Data for Business Model Renovation , 2017, MIS Q. Executive.

[48]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[49]  Varun Grover,et al.  Leveraging Information Technology Infrastructure to Facilitate a Firm's Customer Agility and Competitive Activity: An Empirical Investigation , 2012, J. Manag. Inf. Syst..

[50]  Timothy W. Finin,et al.  Robust semantic text similarity using LSA, machine learning, and linguistic resources , 2015, Language Resources and Evaluation.

[51]  Richard L. Priem.  A Consumer Perspective on Value Creation , 2007 .

[52]  Wenpin Tsai.  Social Structure of Coopetition Within a Multiunit Organization: Coordination, Competition, and Intraorganizational Knowledge Sharing , 2002 .

[53]  Monic Sun,et al.  How Does the Variance of Product Ratings Matter? , 2010, Manag. Sci..

[54]  Weiguo Fan,et al.  An Integrated Text Analytic Framework for Product Defect Discovery , 2015 .

[55]  Fang Wu,et al.  Utilizing customer knowledge in innovation: antecedents and impact of customer involvement on new product performance , 2016 .

[56]  David Godes,et al.  Using Online Conversations to Study Word-of-Mouth Communication , 2004 .

[57]  Weiguo Fan,et al.  Tapping the power of text mining , 2006, CACM.

[58]  Varun Grover,et al.  Shaping Agility through Digital Options: Reconceptualizing the Role of Information Technology in Contemporary Firms , 2003, MIS Q..

[59]  J. Heckman.  Sample selection bias as a specification error , 1979 .

[60]  Omer Levy,et al.  word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method , 2014, ArXiv.

[61]  Phil Edwards,et al.  Survey Errors and Survey Costs , 1991 .

[62]  Qing Hu,et al.  Value co-creation between firms and customers: The role of big data-based cooperative assets , 2016, Inf. Manag..

[63]  Alan R. Hevner,et al.  Control of Flexible Software Development Under Uncertainty , 2009, Inf. Syst. Res..

[64]  Kevin J. Boudreau,et al.  Let a Thousand Flowers Bloom? An Early Look at Large Numbers of Software 'Apps' Developers and Patterns of Innovation , 2011, Organ. Sci..

[65]  Wei Li,et al.  Research on Deep Web Query Interface Clustering Based on Hadoop , 2014, J. Softw..

[66]  Richard A. Harshman,et al.  Indexing by Latent Semantic Analysis , 1990, J. Am. Soc. Inf. Sci..