Yubo Chen

Online Social Interactions: A Natural Experiment on Word of Mouth Versus Observational Learning

Consumers’ purchase decisions can be influenced by others’ opinions, or word of mouth (WOM), and/or others’ actions, or observational learning (OL). Although information technologies are creating increasing opportunities for firms to facilitate and manage these two types of social interaction, to date, researchers have encountered difficulty in disentangling their competing effects and have provided limited insights into how these two social influences might differ from and interact with each other. Using a unique natural experimental setting resulting from information policy shifts at the online seller Amazon.com, the authors design three longitudinal, quasi-experimental field studies to examine three issues regarding the two types of social interaction: (1) their differential impact on product sales, (2) their lifetime effects, and (3) their interaction effects. An intriguing finding is that while negative WOM is more influential than positive WOM, positive OL information significantly increases sales, but negative OL information has no effect. This suggests that reporting consumer purchase statistics can help mass-market products without hurting niche products. The results also reveal that the sales impact of OL increases with WOM volume.

Does a Firm's Product-Recall Strategy Affect Its Financial Value? An Examination of Strategic Alternatives During Product-Harm Crises

Product-harm crises often result in product recalls, which can have a significant impact on a firms reputation, sales, and financial value. In managing the recall process, some firms adopt a proactive strategy in responding to consumer complaints, while others are more passive. In this study, the authors examine the impact of these strategic alternatives on firm value using Consumer Product Safety Commission recalls during a 12-year period from 1996 to 2007. Using the event study method, the authors show that regardless of firm and product characteristics, proactive strategies have a more negative effect on firm value than more passive strategies. An explanation for this surprising result is that the stock market interprets proactive strategies as a signal of substantial financial losses to the firm. When a firm proactively manages a product recall, the stock market infers that the consequence of the product-harm crisis is sufficiently severe that the firm had no choice but to act swiftly to reduce potential financial losses. Therefore, firms dealing with product recalls must be sensitive to how investors might interpret a proactive strategy and be aware of its potential drawbacks.

The Role of Marketing in Social Media: How Online Consumer Reviews Evolve

Social media provide an unparalleled platform for consumers to publicize their personal evaluations of purchased products and thus facilitate word-of-mouth communication. This paper examines relationships between consumer posting behavior and marketing variables - such as product price and quality - and explores how these relationships evolve as the Internet and consumer review websites attract more universal acceptance. Based on automobile-model data from several leading online consumer review sources that were collected in 2001 and 2008, this study demonstrates that the relationships between marketing variables and consumer online-posting behavior are different at the early and mature stages of Internet usage. For instance, in the early stage of consumer Internet usage, price is negatively correlated with the propensity to post a review. As consumer Internet usage becomes prevalent, however, the relationship between price and the number of online consumer reviews shifts to a U-shape. In contrast, in the early years, price has a U-shaped relationship with overall consumer rating, but this correlation between price and overall rating becomes less significant in the later period. Such differences at the two different stages of Internet usage can be driven by different groups of consumers with different motivations for online review posting.

When Do Third-Party Product Reviews Affect Firm Value and What Can Firms Do? The Case of Media Critics and Professional Movie Reviews

Third-party product reviews (TPRs) have become ubiquitous in many industries. Aided by communication technologies, particularly on the Internet, TPRs are widely available to consumers, managers, and investors. The authors examine whether and how TPRs of new products influence the financial value of firms introducing the products. An event study covering 14 major media and professional reviews of movies released by 21 studios shows that TPRs exert significant impact on stock returns in the direction of their valence. However, the impact comes from the valence of a review that is measured relative to other, previously published reviews and not from the absolute valence of the review itself. The authors further study the dynamics of TPR impact on firm value and find that the impact exists only for prerelease reviews and is the strongest on the product release date, though it disappears when sales information becomes available after product release. These results demonstrate that TPRs play significant roles as the investors update their expectation about new product sales potential. The authors also find that advertising spending increases the positive impact of TPRs on firm value and buffer the negative impact. Therefore, firms could strategically use marketing instruments such as advertising to moderate the impact of TPRs.

Deciphering word-of-mouth in social media: Text-based metrics of consumer reviews

Enabled by Web 2.0 technologies, social media provide an unparalleled platform for consumers to share their product experiences and opinions through word-of-mouth (WOM) or consumer reviews. It has become increasingly important to understand how WOM content and metrics influence consumer purchases and product sales. By integrating marketing theories with text mining techniques, we propose a set of novel measures that focus on sentiment divergence in consumer product reviews. To test the validity of these metrics, we conduct an empirical study based on data from Amazon.com and BN.com (Barnes & Noble). The results demonstrate significant effects of our proposed measures on product sales. This effect is not fully captured by nontextual review measures such as numerical ratings. Furthermore, in capturing the sales effect of review content, our divergence metrics are shown to be superior to and more appropriate than some commonly used textual measures the literature. The findings provide important insights into the business impact of social media and user-generated content, an emerging problem in business intelligence research. From a managerial perspective, our results suggest that firms should pay special attention to textual content information when managing social media and, more importantly, focus on the right measures.

User-Generated Content on Social Media: Predicting Market Success with Online Word-of-Mouth

Enabled by Web 2.0 technologies, online social media in the forms of discussion forums, message boards, and blogs has become a prevalent channe lof communication for consumers and businesses. Online social media allows consumers to share their product opinions and experience at an unprecedented pace and scale. This user generated content, or online word of mouth (WOM), has the potential to influence product sales and firm strategy. Consequently, as Web-mining and opinion-mining tools and technology continue to proliferate, it is critical to examine how WOM information can be measured and used to improve managerial decisions.In this article, we explore the predictive validity of various text and sentiment measures of online WOM for the market success of new products. From the firms’ perspective, it is important to effectively predict the sales of new products in the product development process. The earlier such a forecast can be made, the more useful it will be, since marketing strategies can then be adjusted accordingly. We thus examine online WOM that appears at different stages of the new-product life cycle, such as before production, before introduction, and after introduction. New-product development is a highly risky process, and it is useful to examine different aspects of its success. In addition to examining product sales directly, we also study product evaluation by third-party professionals and how the product would receive marketing support from the firm, both of which could influence sales. The context of our study is the Hollywood movie industry. The forecast of movie sales is highly challenging and has started to incorporate online WOM. We collected online WOM information from the message board of Yahoo Movies for a total of 257 movies released from 2005 to 2006. We used Senti Word Net and Opinion Finder, two lexical packages of computational linguistics, to construct the sentiment measures for the WOM data. We will first examine the evolution patterns of online WOM over time, followed by a correlation analysis of how various sentiment measures relate to the metrics of new product success.When Adam Smith wrote the Wealth of Nations in 1776, he concluded that individuals, firms, and nations obtain comparative advantage by specialization. Markets worked as the invisible hand to efficiently allocate resources between specialized parties. During the Industrial Revolution, manufacturing organizations helped the nation become wealthy by creating mechanisms for the internal allocation and integration of resources to produce largely tangible output. Today, both markets and organizations are undergoing a phase transition.to the “skills, technologies, applications, and practices used to support decision making” (http:// en.wikipedia.org/wiki/Business_intelligence). On the basis of a survey of 1,400 CEOs, the Gartner Group projected BI revenue to reach

The Phase Transition of Markets and Organizations: The New Intelligence and Entrepreneurial Frontier

3 billion in 2009.1 Through BI initiatives, businesses are gaining insights from the growing volumes of transaction, product, inventory, customer, competitor, and industry data generated by enterprise-wide applications such as enterprise resource planning (ERP), customer relationship management (CRM), supply-chain management (SCM), knowledge management, collaborative computing, Web analytics, and so on. The same Gartner survey also showed that BI surpassed security as the top business IT priority in 2006.1 BI has been used as an umbrella term to describe concepts and methods for improving business decision making by using fact-based support systems. BI also includes the underlying architectures, tools, databases, applications, and methodologies. BI’s major objectives are to enable interactive and easy access to diverse data, enable manipulation and transformation of these data, and give business managers and analysts the ability to conduct appropriate analyses and then act.2 BI is now widely adopted in the world of IT practice and has also become popular in information systems curricula.3 Successful BI initiatives have been reported for major industries—from healthcare and airlines to major IT and telecommunications fi rms.2 As a data-centered approach, BI relies heavily on various advanced data collection, extraction, and analysis technologies.2,3 Data warehousing is often considered the foundation of BI. Design of data marts and tools for extraction, transformation, and load (ETL) are essential for converting and integrating enterprise-specifi c data. Organizations often next adopt database query, online analytical processing (OLAP), and advanced reporting tools to explore important data characteristics. Business performance management (BPM) using scorecards and dashboards allow analysis and visualization of various employee performance metrics. In addition to these well-established business analytics functions, organizations can adopt advanced knowledge discovery using data and text mining for association rule mining, database segmentation and clustering, anomaly detection, and predictive modeling in various information systems and human resources, accounting, fi nance, and marketing applications. Since about 2004, Web intelligence, Web analytics, Web 2.0, and user-generated content have begun to usher in a new and exciting era of business research, which we could call Business Intelligence 2.0. An immense amount of company, industry, product, and customer information can be gathered from the Web and organized and visualized through various knowledge-mapping, Web portal, and multilingual retrieval techniques.4 By analyzing customer clickstream data logs, Web analytics tools such as Google Analytics provide a trail of the user’s online activities and reveal the user’s browsing and purchasing patterns. Web site design, product placement optimization, customer transaction analysis, and product recommendations can Business Intelligence (BI), a term coined in 1989, has gained much traction in the IT

The Emergence of Innovation as a Social Process: Theoretical Exploration and Implications for Entrepreneurship and Innovation

Enabled by Web 2.0 technologies, online social media in the forms of discussion forums, message boards, and blogs has become a prevalent channe lof communication for consumers and businesses. Online social media allows consumers to share their product opinions and experience at an unprecedented pace and scale. This user generated content, or online word of mouth (WOM), has the potential to influence product sales and firm strategy. Consequently, as Web-mining and opinion-mining tools and technology continue to proliferate, it is critical to examine how WOM information can be measured and used to improve managerial decisions.In this article, we explore the predictive validity of various text and sentiment measures of online WOM for the market success of new products. From the firms’ perspective, it is important to effectively predict the sales of new products in the product development process. The earlier such a forecast can be made, the more useful it will be, since marketing strategies can then be adjusted accordingly. We thus examine online WOM that appears at different stages of the new-product life cycle, such as before production, before introduction, and after introduction. New-product development is a highly risky process, and it is useful to examine different aspects of its success. In addition to examining product sales directly, we also study product evaluation by third-party professionals and how the product would receive marketing support from the firm, both of which could influence sales. The context of our study is the Hollywood movie industry. The forecast of movie sales is highly challenging and has started to incorporate online WOM. We collected online WOM information from the message board of Yahoo Movies for a total of 257 movies released from 2005 to 2006. We used Senti Word Net and Opinion Finder, two lexical packages of computational linguistics, to construct the sentiment measures for the WOM data. We will first examine the evolution patterns of online WOM over time, followed by a correlation analysis of how various sentiment measures relate to the metrics of new product success.When Adam Smith wrote the Wealth of Nations in 1776, he concluded that individuals, firms, and nations obtain comparative advantage by specialization. Markets worked as the invisible hand to efficiently allocate resources between specialized parties. During the Industrial Revolution, manufacturing organizations helped the nation become wealthy by creating mechanisms for the internal allocation and integration of resources to produce largely tangible output. Today, both markets and organizations are undergoing a phase transition.to the “skills, technologies, applications, and practices used to support decision making” (http:// en.wikipedia.org/wiki/Business_intelligence). On the basis of a survey of 1,400 CEOs, the Gartner Group projected BI revenue to reach

Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix

3 billion in 2009.1 Through BI initiatives, businesses are gaining insights from the growing volumes of transaction, product, inventory, customer, competitor, and industry data generated by enterprise-wide applications such as enterprise resource planning (ERP), customer relationship management (CRM), supply-chain management (SCM), knowledge management, collaborative computing, Web analytics, and so on. The same Gartner survey also showed that BI surpassed security as the top business IT priority in 2006.1 BI has been used as an umbrella term to describe concepts and methods for improving business decision making by using fact-based support systems. BI also includes the underlying architectures, tools, databases, applications, and methodologies. BI’s major objectives are to enable interactive and easy access to diverse data, enable manipulation and transformation of these data, and give business managers and analysts the ability to conduct appropriate analyses and then act.2 BI is now widely adopted in the world of IT practice and has also become popular in information systems curricula.3 Successful BI initiatives have been reported for major industries—from healthcare and airlines to major IT and telecommunications fi rms.2 As a data-centered approach, BI relies heavily on various advanced data collection, extraction, and analysis technologies.2,3 Data warehousing is often considered the foundation of BI. Design of data marts and tools for extraction, transformation, and load (ETL) are essential for converting and integrating enterprise-specifi c data. Organizations often next adopt database query, online analytical processing (OLAP), and advanced reporting tools to explore important data characteristics. Business performance management (BPM) using scorecards and dashboards allow analysis and visualization of various employee performance metrics. In addition to these well-established business analytics functions, organizations can adopt advanced knowledge discovery using data and text mining for association rule mining, database segmentation and clustering, anomaly detection, and predictive modeling in various information systems and human resources, accounting, fi nance, and marketing applications. Since about 2004, Web intelligence, Web analytics, Web 2.0, and user-generated content have begun to usher in a new and exciting era of business research, which we could call Business Intelligence 2.0. An immense amount of company, industry, product, and customer information can be gathered from the Web and organized and visualized through various knowledge-mapping, Web portal, and multilingual retrieval techniques.4 By analyzing customer clickstream data logs, Web analytics tools such as Google Analytics provide a trail of the user’s online activities and reveal the user’s browsing and purchasing patterns. Web site design, product placement optimization, customer transaction analysis, and product recommendations can Business Intelligence (BI), a term coined in 1989, has gained much traction in the IT

The Firm's Management of Social Interactions

Understanding of business venturing cannot be complete without an understanding of the preconditions and drivers of the emergence of innovation in society. Considerable thought and theory have focused on the diffusion of innovation in society and the properties of innovation attributable to the entrepreneur or the firm. This chapter moves beyond these topics and focuses on theorizing about the emergence of innovations. The authors document how both theory and industry practices are moving away from viewing the emergence of innovation as a proprietary and mostly entrepreneur, individual or firm-centric process to a more open and largely social process. Importantly, institutions are viewed as both barriers to and opportunities for innovation and thus become the fundamental proposition for the emergence of innovation. The chapter develops five additional propositions on the emergence of innovation with both micro (firm-level) and macro (societal) implications. Implications for venture management, public policy, and research conclude the chapter.This chapter reviews the burgeoning literature on academic entrepreneurship, including studies of university licensing, patenting, and startup formation. I also attempt to synthesize these results and consider the managerial and policy implications of such trends.This chapter explores the meaning of reshoring and its drivers in the case of the U.K. manufacturing, with a particular focus on its automotive sector. Using a mixed methods approach, drawing on interviews, policy reviews, and a range of recent surveys, the chapter finds that while reshoring is indeed a discernable trend in the U.K. manufacturing, it is less pronounced than many have claimed. In the U.K. case at least there are severe limits as to how far this reshoring trend can go, particularly in relation to the availability of skills and finance in the supply chain, and the availability of land for manufacturing. This is in turn raises questions over the stance of British industrial policy and whether more could be done, with comparisons made to recent U.S. experience.We unpack the black box of open innovation, external knowledge search in particular, by showing how firms use not only R&D but also manufacturing and marketing for external knowledge sourcing for product and process innovation. Our econometric model reveals that the inverted U-shaped relationships found by Laursen and Salter (2006) for external search breadth hold for all functional areas but those for external search depth hold only for R&D-based search aiming at product innovation. In every case, we find that observed firms’ breadth suboptimal. We also find suboptimality for search depth: the optimal depth level is the maximum for marketing-based and manufacturing-based searches. We also specifically show that marketing is the dominant base for external sourcing for product innovation, and manufacturing is the dominant base for external sourcing for process innovation. Finally, we find strong complementarities among the roles of the three functional areas, which suggests that firms develop different absorptive capacities at the same time and do not substitute one for another.Open innovation has attracted considerable attention from both practitioners and researchers; however, whether open innovation is a sustainable trend or managerial fad is still unsolved. In this chapter, we investigate open innovation practice from a service-dominant (S-D) logic perspective and conclude that open innovation is a sustainable trend, because all axioms of S-D logic suggest that open innovation is a normative approach to innovation. According to the foundational premises of S-D logic, we further provide several strategic implications for open innovation. Those implications are under the themes of service provision, value cocreation, resources integration, service ecosystem, and institutions. In sum, this chapter provides both theoretical support and practical implications for open innovation.Universities globally have to respond to the budgetary implications of declining government expenditure on higher education. Coupled with the increasing cost research for research intensive universities, the higher education sector is facing unprecedented pressure to evolve in order to remain competitive and leverage advantages. Innovative universities are responding to these negative change drivers in remarkably similar ways. In this chapter we explore the proactive response of an exemplar Australian university, The University of Queensland (UQ). For UQ to sustain its research capabilities in a highly competitive environment, it had to break its path dependence and undertake a significant strategic shift. Its research had already provided a competitive advantage for each of its leading research institutions, supported by extensive resources in research infrastructure, such as buildings and laboratories, as well as close collaborations with major hospitals, companies and government, and their internal knowledge resources, expertise, technical and administrative skills sets. This is the story of how a University built and disseminated the wider research commercialization benefits across the University, and beyond. In the discussion, we examine the implications of attempting to respond to both of these stimuli simultaneously, given the restraints imposed by declining government funding and increasing costs of science research infrastructure, as well as alternative strategies.