social media in a social phenomenon: social media in the ... · kim / social media in entrainment...

Thirty Third International Conference on Information Systems, Orlando 2012 1

SOCIAL MEDIA IN A SOCIAL PHENOMENON: SOCIAL MEDIA IN THE ENTRAINMENT OF

CONTENTION TO INNOVATION Research-in-Progress

Inchan Kim Price College of Business University of Oklahoma Norman, OK 73019-4006

[email protected]

Abstract

In this inductive theory building paper, I examine the impact of social media on a social phenomenon. I particularly focus on a social phenomenon that concerns firms—the entrainment of contention to innovation. The entrainment of contention to innovation is defined as the alignment of the pace and/or timing of contention (e.g., opposition, petition, protest) with those of firms’ innovation. To examine the impact of social media on that entrainment, I first examine the entrainment of contention to innovation in general and then look into the innovation-contention entrainment by innovation type. Based on this, I finally examine the societal impact of social media. I focus on the energy sector and gather such qualitative data as patents, news releases, and the texts on social media. I perform a content analysis on those data and graphically analyze the outcomes of the content analysis. Preliminary findings will be presented at the conference.

Keywords: Social Impact of IT, Social Impact of Social Media, Social Media, Entrainment, Time lens, Inductive Study, Theory Building, Contention, Innovation, Content Analysis, Graphical Analysis, Patent, News Article

Social and Organizational Impacts of IS

2 Thirty Third International Conference on Information Systems, Orlando 2012

Introduction

In this inductive theory building paper, I examine the societal impact of social media. Social media are defined as a group of Internet-based applications in which users participate and on which users collaborate (Kaplan and Haenlein 2010). They include blogs, wikis, and social networking sites (Asur and Huberman 2010; Kane et al. 2009). Social media have drawn a great deal of scholarly attention (Kane et al. 2010). For example, researchers have examined underlying schemas in organizational usage of social media (Miranda et al. 2012), impacts of social media membership turnover on collaborative outcomes (Ransbotham and Kane 2011), practical strategies for managing online communities (Kane et al. 2009), effects of twitter on group decision-making (Choi et al. 2011), antecedents and consequences of continuous knowledge revision in organizational wikis (Majchrzak et al. Forthcoming), and appropriate organizational governance structures with social media (Dean 2011).

As can be seen from the aforementioned examples, the importance and value of social media for business have been acknowledged. However, while our understanding of the business value has been increasing, our understanding of the societal impact of social media lags. Unlike most information systems (e.g., ERP)—which are mainly utilized by firms for their internal usage (e.g., Ranganathan and Brown 2006), social media tend to be used as a platform to communicate with stakeholders (Gallaugher and Ransbotham 2010) as well as with the public (Kane et al. 2009). Not only this, social media are heavily used amongst the crowd (Kane et al. 2010; Qualman 2009). These social media usage patterns suggest that not only do social media impact social phenomena in general (e.g., relationship between friends, political activism), but they may also affect or amplify the social phenomena that are concerned with firms (e.g., protest against firms). A social phenomenon that tends to be provoked by and thus concerned with firms is the focus of this study—in particular, the innovation-contention entrainment. I examine the role of social media in that entrainment (i.e., the societal impact of social media).

Entrainment is conceptualized as the alignment of the pace and/or timing of a cyclic activity with those of another activity (Ancona and Chong 1996). For example, the extent to which the pace and the timing of the occurrence of contention (e.g., opposition, petition, protest) are aligned with the pace and the timing of innovation over time is the degree to which contention is entrained to innovation—i.e., innovation-contention entrainment (See McAdam et al. (2001) for detailed description of contention). The innovation-contention entrainment is, then, defined as the alignment of the pace and/or timing (i.e., cycle) of contention with those of innovation. It should be noted that I focus on the innovation-contention entrainment, in which contentious reaction is provoked by firms’ innovation.

Entrainment theory is a theoretical lens focusing on time (Ancona and Chong 1992, 1996; Ancona et al. 2001; McGrath and Rotchford 1983). As described in the previous paragraph, entrainment theory emphasizes “when (timing)” and “at what rate (pace)” an activity—or change—occurs in relation to another (Perez-Nordtvedt et al. 2008). Thus, the entrainment lens informs managers of “when” and “at what rate” they should respond to change, adding to the insights from incumbent theoretical lenses. For instance, the strategic lens primarily underscores “what” and “how”, not “when” or “what rate” (Ancona et al. 2001). Despite such potential to improve our understanding of diverse phenomena that concern organizations, theory-building efforts on entrainment have been minimal in organization sciences (Bluedorn 2000; Pérez-Nordtvedt et al. 2008).

As I mentioned earlier, I particularly examine the innovation-contention entrainment. Arguably, innovation is a crucial business activity (Miron-Spektor et al. 2011; Obstfeld, 2005; Rigby and Bilodeau 2011). However, it also tends to attract contention from interested actors (Soule 2009). Being able to handle disgruntled actors is important for building and maintaining reputation, legitimacy, and competitive advantage (Lee 2008). This is particularly crucial in modern days as we see more anti-corporate activism than ever before (Earl and Kimport 2011; Soule 2009). Building a theory that can explain and predict “when” and “at what rate” contentious actions occur against firms’ innovation would, then, be a key to understanding how firms can innovate while maintaining or enhancing stakeholders’ favorable perception.

To sum up, in this paper I aim to examine how social media impact the innovation-contention entrainment by synthesizing the IS, organization theory, sociology, and innovation literatures. I conduct a field-level historical analysis (Martin 2003). The field I propose to analyze is the energy sector. The energy

Kim / Social Media in Entrainment of Contention to Innovation


sector is fraught with contention about the implications of different energy technologies—e.g., natural gas, bio fuel (McAdam and Boudet 2012). This is also reflected in the fact that the energy sector in general was recently rated as America’s most hated field (Sauter and Frohlich 2012). The energy sector is, then, the purposive or theoretical sample for this study. The energy sector well represents the focal phenomenon of the study for the purpose of theory building (Pratt 2009; Eidenhardt 1989a).

In achieving the goal of this paper, I make three contributions. First, I will have demonstrated a social impact of IT—in particular, social media. I particularly focus on a social phenomenon that concerns firms (i.e., the entrainment of contention to firm innovation). Second, I will have opened the black box of the entrainment of contention to innovation. This will provide theoretical grounds for more fine-grained future entrainment theory. Finally, this study will inform managers of when they could expect contentious action against their innovation and of how fast the contention could plays out.

In the following section, I propound three research questions. Then, I elaborate on research methods. Next, I discuss possible research findings and then conclude with possible contributions.

Research Questions

In this paper, I address three research questions. Figure 1 pictorially summarizes my research questions.

Figure 1. Pictorial Summary of Research Questions

First, with 10 years of secondary data, I track the contention and innovation cycles in the energy sector to examine how the contention cycle matches the innovation cycle. Contention has been found to cluster over time, engendering cycles of contention (Koopmans 1993, 2004; Tarrow 1993). Why does this clustering occur? One answer might be that cycles of contention are linked to technological innovation. Oftentimes, innovation is one of the main causes of contention (Soule 2009). In particular, new energy technologies (e.g., shale gas, ethanol) often provoked contention over the past 10 years (e.g., Hond 2012). Moreover, technological innovation has also been observed to cluster over time (Anderson and Tushman, 1991; Tushman et al. 1997; Van de Ven et al. 1999). Therefore, it may be that there is strong entrainment between contention and innovation cycles. More important issue, however, is that there exist little priori to predict the patterns of the entrainment of the two cycles. Hence, examining the contention-innovation entrainment could serve as a good starting point in improving our understanding of entrainment.

The energy sector provides an excellent opportunity to investigate the innovation-contention entrainment. Over the last decade, this sector has witnessed considerable innovation as well as contention on diverse energy technologies (e.g., natural gas, biofuel, wind energy). Moreover, the cost of not pre-



empting or addressing contention on such issues with appropriate action will be considerable not only to energy firms but also to the whole nation—as can be seen from the contention on hydro-fracturing (e.g., Gasland contention (Hond 2012)). Thus, understanding how contention occurs in relation to innovation also bears crucial practical meanings. My first research question is therefore:

Research Question 1: How and to what extent is the contention cycle entrained to the innovation cycle in the energy sector?

Second, I refine my theory by dividing the data by innovation type. In doing so, I look into how the contention cycle fluctuates with the cycles of different types of innovation. The patterns of the innovation-contention cycle may vary depending on innovation types. Among various types, I particularly focus on two innovation types: innovation complexity (i.e., simple vs systemic (Heeley et al. 2007)) and innovation discontinuity (i.e., radical vs incremental (Bower and Christensen 1995; Henderson and Clark 1990)). Complex, or systemic, innovations are less transparent to stakeholders (Heeley et al. 2007). As a consequence, it tends to take time to see the implications of those innovations. Then, it might be that contention arises more slowly, demonstrating relatively larger temporal gaps in the entrainment of contention to complex innovation. As with innovation discontinuity, discontinuous change (or radical innovation) instigates sense-making (Weick 1995) and thus draw more attention. Radical innovation also often provokes more controversy (Rindova and Petkova 2007). Hence, the alignment of contention cycle to innovation cycle for radical innovation may be swifter than the alignment for incremental innovation.

These theoretical refinements also convey practical implications. If firms can predict when and how fast contention arises against the type of their innovation, they will, at least, be in the better position to deal with such contention. Hence, pursuing the following research question would appeal to both academics and practitioners.

Research Question 2: How does innovation type—namely, simple versus systemic and incremental versus radical—influence the innovation-contention entrainment?

Finally, on the basis of Research Question 1 and 2, I endeavor to examine the role of social media in the innovation-contention entrainment. Amongst other types of IT, especially social media may play an important part in the entrainment of contention to innovation.

The Internet has facilitated non-institutional activism—e.g., protests, sit-ins (Kahn and Kellner, 2003; Stolle and Micheletti 2005). Operating on the Internet, most social media tend to further strengthen the contention-conducive functionality of the Internet. Social media give voice to ordinary people without gatekeeping (Cross 2011; Du et al. 2010); that is, their opposition can be broadcast more easily without filtering. It is also likely that people can come across those people with similar opinions through social media. Such interaction facilitates the creation of “virtual identity groups” (Stolle and Micheletti 2005). Moreover, some social media allow rapid communication (e.g., Twitter). This feature can be useful for mobilization of contention (e.g., protest). Furthermore, social media are great communication conduits between firms and stakeholders for new business activities (Gallaugher and Ransbotham 2010). Social media can, then, increase stakeholders’ awareness of the likely consequences of innovation, which could lead to stakeholders’ expressions of contention.

On the basis of these characteristics of social media, recent research demonstrates that social media are great platforms through which contention develops and diffuses (Kim and Miranda 2011a; Vaast et al. 2012; Yetgin et al. 2012). Moreover, it seems that many social movements already harness generic social media such as Twitter and Facebook (Kim and Miranda 2011a; Kim and Miranda 2011b). Not only this, contention-specific social media sites (e.g., petition sites), which are becoming prevalent, are often used for a medium for voicing oppositions.

Taken together, the nature of social media as well as the aforementioned studies suggests that in the era of great social media influence, the entrainment of contention to innovation might occur differently than in the pre-social media era. Again, insights from this research question could help firms understand the role of social media in the emergence of contention relative to their innovation, possibly stimulating conservative firms, such as energy and distribution firms (Culnan et al. 2010), to understand their likely payback from social media investments.

Research Question 3: How does the entrainment of contention to innovation in the social media era compare with that of the pre-social media era?



Data on Innovation & Contention

In this paper, I strive to understand the impact of social media on the manner and the extent to which contention is entrained to firms’ innovation. That is, I am trying to make sense of a temporally dynamic social phenomenon. Such temporal phenomenon tends to be best described by qualitative research methods (Bansal and Corley 2011; Cheney 2000). Therefore, I utilized qualitative data and employ qualitative analytical techniques. In particular, I conduct a content analysis (Krippendorff 2004) on patents and news articles to extract the innovation cycle and the contention cycle. I utilize longitudinal data as I try to understand a dynamic phenomenon occurring over time.

Natural Experiment

I compare the innovation cycle occurring in the energy sector as a whole (e.g., oil & gas industry, alternative energy industry, and pipeline industry) with the contention cycle, which occurs partly owing to the innovation cycle. Thus, this will be a sector-level case study—a research method appropriate for theory building (Eisenhardt 1989a; Yin 2009). I will investigate the two five-year time spans before and after the year of 2008 as most social media have gained popularity since 2008.

Twitter was launched in July 2006, Facebook in February 2004, and MySpace in August 2003. Yet, Google Trends demonstrates exponential growth for “social media” searches in 2008. Searches for Facebook and Twitter grew considerably at that time as well. Moreover, online petition sites started booming around 2008. For example, one of the most popular petition sites, Change.org, was launched in February 2007, and Google searches for Change.org commenced late 2008. These trends of diffusion of general social media as well as contention-specific social media provide a good opportunity for a natural experiment for the effect social media has had, since 2008, on the innovation-contention entrainment. I will treat 2003-2007 as the pre-social media period and the 2008-2012 as the social media period.

Sample

I develop two sets of sample firms for this study. The first sample is US energy firms that have appeared on the Fortune 500 list between 2003 and 2012. Relative to small and mid-sized firms, large firms tend to be more visible (Baker et al. 1999) and take higher expectations of social responsibility (Udayasankar, 2008). Consequently, innovation by large firms tends to attract more attention and thus more contention from the public. There are 101 energy firms listed at least once on the Fortune 500 list during the period of 2003 and 2012. The second sample consists of those energy firms that appeared on the Inc. 500 list between 2002 and 2011. Research shows that radical innovations are at least as likely to emerge from startups as from incumbent firms (Chandy and Tellis, 2000). There are 81 firms listed at least once on the Inc. 500 list between 2002 and 2011 (the 2012 list has not been released yet). From these two lists, I note revenues ranging from $4.8 billion to $453 billion for the Fortune firms and $2 million to $1.6 billion for the Inc. firms. This range in firm size permits me to account for Chandy and Tellis’ observation that both large, incumbent firms and small, non-incumbent firms tend to be the source of radical innovations.

Data Collection

For the innovation cycle, I gather 10 years’ patent data from USPTO in the energy sector from 2003. Patents are often used as surrogates for firms’ innovations (e.g., Belenzon and Berkovitz 2010).

For the contention cycle, I tap into five highly-regarded national newspapers through Factiva—namely, Wall Street Journal, USA Today, New York Times, LA Times, and Washington Post. Newspaper data are one of the most frequently used forms of data in the field of social movements (e.g., Earl et al. 2004; Soule, 2009). McAdam and Su (2002: 704) even note that the analysis of protest data gained from newspapers is a “methodological staple” in social movement studies and that many of the “classical empirical works in the field” use newspaper data.

Given my interest in the effect of social media proliferation on the entrainment of contention to innovation, I will also search social media sites, such as Twitter. In particular, Twitter has been frequently used to coordinate social movements (ForeignAffairs, 2011; Kim and Miranda 2011a). I will also search for



contention-specific petition sites such as Change.org and thepetitionsite.org.

Content Analysis

Innovation is operationalized, per period, as the number of energy-related patents (e.g., innovations on energy technologies, drilling activities, extraction methods). The number of periods between 2003 and 2012 will be determined after the content analysis in order to minimize the number of zero-counts within each period and to generate meaningful cycles of innovation and contention. As of September 7th, there exist a number of energy-related patents granted to 28 of the sample firms.

Contention is operationalized, per period, as the number of the program claims articulated in two sources: (1) newspaper articles referencing company-specific contention and (2) social media posts or online petitions referencing company-specific contention (Twitter archives and protest sites permit such advanced searches). A program claim is a statement “in support of or in opposition to actual or proposed actions” (Tilly 2006: 209).

To locate news articles containing program claims in an enormous number of articles, I develop search terms bases on previous studies (e.g., Tilly 2006; Clemens and Hughes 2002; Elsbach 1994; Soule 2009). Example search terms are opposition, disagreement, support, demonstration, and protest. After locating articles containing those search terms, I look at the context surrounding those search terms in the article. By doing so, I ensure that the article truly entails program claims. Then, the number of program claims is counted. It is possible that a single article about a firm denounces the firm’s work on both biofuel and natural gas. Each, then, represents a separate program claim. The same process is applied to social media posts and petitions.

In addition to operationalizing contention at the firm level, sector-level metrics is developed to account for spillovers in contention (e.g., firm A’s innovation stimulates non-firm specific contention). The field-level contention metric is per-period count of non-firm specific program claims featured in news articles and social media posts/petitions.

Innovation discontinuity is operationalized as the number of citations earned by a patent (Trajtenberg et al. 1997). Prior operationalization of technological complexity in terms industry (Heeley et al. 2007) is unsuitable to my sector-level investigation. Thus, innovation complexity is operationalized as the number of classes in which a patent is filed.

Data Analysis & Expected Findings

In this section, I describe what I could possibly find through data analysis, in particular graphical analysis. However, it should be noted that since I take an inductive approach, insightful findings that I cannot really foresee at this stage can emerge through iterations of data analysis (Bansal and Corley 2011). For example, depending on the number of periods between 2003 and 2012, the nature of entrainment could differ significantly.

Graphical Analysis

To analyze the innovation-contention entrainment graphically, I appropriate longitudinal data and graphical data analysis techniques. Figure 2 depicts an example. Extant entrainment studies have assessed entrainment by applying cross-sectional survey (Jansen and Kristof-Brown 2005; Khavul et al. 2010), by employing anecdotal approaches (Ancona et al., 2001; Eisenhardt, 1989b), or by conducting one-time experiments (Waller 1999). However, cross-sectional studies can only show a one-off timing alignment (i.e., only partial entrainment) but cannot fully demonstrate the pace and the timing of activity.

Graphical analyses can demonstrate both the pace and the timing of activity over time and, hence, the complete form of entrainment. Using graphs is a utilized and recommended technique in assessing entrainment (Ancona and Chong 1992; Ancona et al., 2001; Lawrence et al. 2001). There are also research streams that heavily employ graphical analyses for theory building purposes—such as the administrative innovation research stream (e.g., Abrahamson 1997; Abrahamson and Eisenman, 2008; Abrahamson and Fairchild 1999; Anderson and Tushman 1990) and the IS innovation research stream (e.g., Wang 2008;



Wang 2010). To illustrate, by analyzing graphs, Abrahamson (2008) demonstrates that the nature of language used to promote and demote management techniques changes over time. Besides, in another theory development study, Abrahamson (1999) discovers, again through graphical analyses, the shapes of cycles of management techniques and shows how those cycles coevolve.

As can be seen from aforementioned studies, graphical data analyses may be useful in examining diverse social or organizational phenomena. In fact, graphical data analyses boast their powerful story-telling functionality as well as the ability to discover various patterns hidden in data (see Tukey 1977; Cleveland 1993). Nonetheless, since Sir Fisher’s invention of ANONA, confirmatory data analysis has dominated research fields; as a result, graphical analyses have not been sufficiently leveraged. In this paper, I will explore the innovation-contention entrainment with different graphical techniques—e.g., Rose Diagram, Multiway dotplots (Cleveland 1993; Rodgers and Udry 1998). As different graphs emphasize different aspects of data, I will make sure I see every possible aspect of the data in order to unravel the innovation-contention entrainment, if any. All graphical analyses will be conducted on R.

Example Analysis & Results (Hypothetical)

As one of the analytical graphs, a line graph as in Figure 2 will be drawn. My analysis on entrainment will begin by plotting, over time, the two sets of data collected. In order to address the first research question, the numbers of selected patents, which represent innovation, will be plotted on a graph across the timeline (i.e., the x-axis). The numbers of gathered program claims, which stand for contention, will also be plotted across the timeline. I will adjust the per-period counts of patents and program claims for the total number of patents and program clams respectively (i.e., standardization). I will, then, be able to compare the innovation cycle and the contention cycle. The y-axis will be the standardized coefficients of contention and innovation. The presence of entrainment will be manifested in a visual approximation as in Figure 2.

Figure 2. Graphical Analysis: Expected Graphical Approximation of Entrainment

Through graphical analysis, I will be able to identify how frequently innovation and contention should be recorded to be able to recognize meaningful patterns of cycles. I will also be able to determine the shapes of the innovation cycle and the contention cycle. Furthermore, I will be able to identify general and specific time lags between the two cycles. These findings will theoretically guide those researchers



who are interested in research on the innovation-contention entrainment. Managers will be informed of the patterns of those two cycles.

To address the second research question, I will divide the patent data by innovation discontinuity and by innovation complexity. I generally anticipate large lags for the entrainment between the cycle of contention and the cycles of complex or incremental innovations. By contrary, small time lags will likely be observed for the entrainment between the cycle of contention and the cycles of simple or radical innovations.

Finally, to address the third research question, I will show how entrainment might transpire before and after social media. Figure 2 demonstrates that after January 2008, the contention cycle very closely follows the innovation cycle with almost no time lag (i.e., strong entrainment) showing similar paces and timings. The amount of contention might also increase. On the other hand, the entrainment before January 2008 seems weak, displaying some time and rhythm lags. Such findings will illustrate how social media impact our society through entrainment. Regarding innovation types, I expect to observe similar cycling patterns to the patterns in Figure 2 across the four innovation categories—namely, incremental, radical, complex, and simple innovations.

To further corroborate my findings, if necessary, I will also employ time series analytical techniques (Hamilton, 1994) to measure entrainment. Two possible analytical strategies are a non-parametric approach that was previously used for assessing patent cycles (Achilladelis et al. 1990) and vector auto-regression, which was used to assess the effects of social-media-based behaviors over time (Trusov et al. 2009).

Contributions

Overall, this paper endeavors to fill gaps primarily in the information systems (IS) literature and in the organization theory (OT) literature. In bridging theoretical gaps in those literatures, this paper also makes practical contributions by providing an understanding of when and how fast contention occurs in relation to innovation in the energy sector. Specific anticipated contributions are listed and elaborated below.

First, this paper will demonstrate the role of social media in the entrainment of contention to innovation. Most extant research focuses on business-focused social media phenomena. For example, scholars demonstrate how to extract business intelligence from blogs (Chau and Zu 2012) and the effects of social media in word-of-mouth marketing (Trusov et al. 2009). However, due to the nature of social media, social media are widely used outside firms (Qualman 2009). This usage pattern creates and affects phenomena outside business (i.e., social phenomena). Systematic examination of social media’s impact on social phenomena may be at least as important as the examination of business-focused phenomena, especially when the social phenomena concern business firms. This is what this paper will have accomplished in the end. I will have demonstrated the impact of social media on the entrainment of contention to innovation—a social phenomenon that concerns firms.

Second, this paper will open the black box of an entrainment cycle, in turn providing some useful theoretical grounds for more fine-grained future entrainment theory. There have been calls for the incorporation of temporality into organizational research (George and Jones 2000). Yet, temporal lenses have been largely neglected, particularly at the macro level. Most importantly, time lenses can provide different perspectives and managerial solutions on firm structure, performance, and survival (Ancona et al. 2001; George and Jones, 2000; Ployhart and Vandenberg 2010; Waller 1999). For example, the time lens applied in this paper, entrainment, suggests the importance of the timing and pace of firms’ reaction to contention. It should be noted that this insight is hardly to be found when the three predominant theoretical lenses are applied (Ancona et al. 2001): the strategic lens, the political lens, and the cultural lens. To illustrate, the strategic lens would emphasize organizational structure that fits the environment as a response to change. The political lens would underscore power, influence, conflict, and network for successful adjustment. The cultural lens would focus on the alignment of norms and values in successful collective reaction and thus higher firm performance. As the foregoing argument suggest, the temporal lens could enrich our thinking on organizational phenomena. This paper could serve as one of the fundamental theories in improving our understanding of diverse organizational phenomena.



Third, as previous paragraphs suggest, this paper is inter-disciplinary. I tap into the IS literature for social media; the OT literature for the time lens (e.g., entrainment); and the sociology literature for contention. Through my synthesis of these literatures, I will have afforded an answer to one of the long-waiting and important questions in the field of organization sciences: how and to what extent a cycle is entrained to another cycle. Moreover, my synthesis will provoke more inter-disciplinary thinking among scholars in those three areas of study, in turn possibly generating fresh insights in currently taken-for-grated phenomena.

Finally, with regard to practical contribution, this paper will particularly inform energy firms of the patterns of contention in relation to their innovations. If energy companies are aware of when and at what rate contention arises against their innovations, they will, at least, be better prepared to tackle such contention. Moreover, this paper will help firms to understand a possible disadvantage of not utilizing social media—some industries are known to be conservative in adopting social media (Culnan et al. 2010). Energy firms will possibly realize that much contention is created and spread in social media and that ignoring social media may harm their reputation or performance.

Conclusion & Future Trajectory

In an effort to build a theory, this paper examines the impact of social media on a social phenomenon—in particular, the entrainment of contention to innovation. To do so, I perform a content analysis on qualitative data (e.g., patents and news articles) and analyze the outcomes of the analysis through graphical analyses. Upon completion of the paper, I will be able to describe (1) the entrainment of contention to innovation in general, (2) the innovation-contention entrainment by innovation type, and (3) the possible variance of the entrainments before and after social media diffusion.

Although generalizability is not the main purpose of inductive research, the findings of this paper might still be generalizable. In particular, change in entrainment by innovation type and by social media era could apply to other contexts. For example, it would not be inconceivable to observe that there is stronger entrainment in the “automobile” sector in the social media era when the results of this paper show stronger entrainment in the “energy” sector in the social media era than in the pre-social media era. However, again, whether the theory to be developed in this paper holds in other contexts should be best left to future research. My aim is not to test existing theories in a generalizable context, but to better understand the focal phenomenon—in effect possibly building a good theory.

Currently, I am in the process of gathering data. Preliminary findings will be presented at the conference.

Acknowledgements

This research is funded by Business Energy Solutions Center at the Price College of Business, University of Oklahoma. I am sincerely thankful to Dr. Shaila Miranda for her guidance and comments on this paper. I am also grateful to the associate editor and the three reviewers for their constructive reviews.



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