Market Competition and Firm Action in a Market: History
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Contributor:
Hua Yuan
,
Wenjun Deng
,
Baojun Ma
,
Yu Qian

Understanding how competitors act in a market is a critical component of strategic decision-making. Understanding market competition is important for firms, consumers of products, and investors.

  • Market Competition
  • Social-Media

1. Introduction

Understanding market competition is important for firms, consumers of products, and investors [1]. In the literature on management, firms have adopted two main streams of approaches to competitor identification, i.e., the supply-based [2][3] and the demand-based approaches [2]. The former is based on the firms’ attributes to divide the competitor, while the latter is based on the customers’ attributes. The growing complexity of organization and market structures in modern industries brings new issues for managers in identifying competition; for example, it has been found that not all firms will take the same competitive action simultaneously [4]. Thus, another new research stream has been developed to identify competitive relationships between firms by judging the similarity of their activities in the market [2][5]. In this approach, the term competitors refers to a set of firms in a market that share similar behaviors/activities [4][6]. However, previous methods rely on the data collected from traditional sources, such as market research [7], which may be outdated and time-consuming to analyze manually [2][8]. Particularly, such data may lead to “managerial myopia” in identifying competitive relationships [8]. Therefore, a critical step in analyzing market competition is to acquire and analyze relevant data that can effectively facilitate scanning the activities of firms.
With the rapid development of Internet technology, social media has been widely adopted in business environments [9]. For example, firms can use social media to deliver information [10], communicate with followers [11], and build relationships with consumers [12] and other organizations [13]. All the messages posted by firms on their official social media pages are firm-generated content [14]. As a result, microblog platforms, such as Twitter.com and Weibo.com, have provided abundant and timely firm-generated content. These contents are usually related to firms’ business events [15], recording a series of events that happened in firms, and showing great value in the form of business strategies [16]. Therefore, it is a feasible way to collect data from social media and extract further information about business events in a market [17].

2. Market Competition and Firm Action

2.1. Social-Media-Based Market Intelligence

One early work in market intelligence (MI) is the BrandPlus platform [18], which explores consumers’ buzzwords about brands and companies. An automatic method by Netzer et al. [19] identified which brands are discussed in consumer forums for the markets of sedan cars and diabetes drugs. Xu et al. [20] focused on extracting comparative relationships from Amazon customer reviews. Wu et al. [21] developed a recommendation system based on the historical weblog posts of users. Recently, many scholars have conducted studies of information extraction from social media for business reasons, and most studies have been focused on the commercial value of enterprise microblogs, such as brand analysis [22], marketing promotion [23], and customer engagement [24]. For example, Onishi and Manchanda [25] specified a log-linear system for market outcomes (sales) and the volume of blogs, and their results suggested that new and traditional media act synergistically. The analysis results in Colicev et al. [26] show that user-generated content has a stronger relationship with awareness and satisfaction, while firm-generated content is more effective for consideration and purchase intent.

2.2. Market Competition and Firm Action in a Market

In both academic and commercial literature, the main tool for explaining rivals’ behavior is game theory models [27], in which it is presumed that all players use the same basic principles to take strategic actions in a market [28]. However, in the real world, game theory models become unwieldy when a competitor has many options for management actions or when there are multiple competitors, each of whom might react differently [29]. Therefore, it is equally important for market managers to know the actions of competitors in a timely manner so as to know their strategies.
In the literature on market competition, one consensus is to use market segmentation and category management strategies to differentiate market competition [30][31].
However, it has been shown that a firm may not recognize a potential competitor even if its action appears obvious in the market [32]. This is primarily because most companies rely on incomplete data, such as market research [7], to evaluate changes in a market. Particularly, such data may lead to a “managerial myopia” [33] in identifying competitive relationships [8].
There is a wealth of research in the literature on analyzing competition in the marketplace, and studies related to “observable events” in the marketplace can be briefly categorized as competitor identification, competitive positioning, dynamic competitive monitoring based on resources and capabilities, and product competitiveness analysis. Some examples of typical studies are listed in Table 1. It can briefly summarize the following characteristics of the work on market competition. Firstly, managerial myopia in identifying competitive threats is a well-recognized phenomenon. Secondly, broad competitor identification is an increasingly important task for managers, but there is no particularly efficient solution. Finally, online data have attracted the attention of managers, but the literature on its application to market competition is still scarce.
Table 1. Research literature related to business market.
Literature Research Goals Methods Contributions
[2][33][34][35] Develop models for competitor identification Hypotheses test Propose a cognitive framework for the identification of competitors
[5][6][7][8][9][10][36][37][38][39][40][41] Competitive positioning;
testing market structure		 Empirical Managers must be mindful to incorporate new information proactively from many sources and to actively discard old, automatic maps in order to develop reliable maps for changing environments
[11][12][13][42][43][44] Dynamic competition identification; resource-based competition Concept model Develop a market-based and resource-based framework for broad competitor identification
[14][15][16][45][46][47] Product competitive advantage analysis Data mining Identify competitors from UGC

This entry is adapted from the peer-reviewed paper 10.3390/jtaer18020047

References

  1. Hitt, M.A.; Ireland, R.D.; Hoskisson, R.E. Strategic Management: Competitiveness and Globalization-Concepts and Cases, 10th ed.; Cengage Learning: Boston, MA, USA, 2012.
  2. Clark, B.H.; Montgomery, D.B. Managerial Identification of Competitors. J. Mark. 1999, 63, 67–83.
  3. Wei, C.P.; Chen, L.C.; Chen, H.Y.; Yang, C.S. Mining suppliers from online news documents. In Proceedings of the Pacific Asia Conference on Information Systems, PACIS, Jeju Island, Republic of Korea, 18–22 June 2013; p. 261.
  4. Coyne, K.P.; Horn, J.L. Predicting Your Competitor’s Reaction. Harv. Bus. Rev. 2009, 87, 90–97.
  5. Hsieh, K.-Y.; Vermeulen, F.J.O.S. The Structure of Competition: How Competition Between One’s Rivals Influences Imitative Market Entry. Organ. Sci. 2014, 25, 299–319.
  6. Ramaswamy, V.; Gatignon, H.; Reibstein, D.J. Competitive Marketing Behavior in Industrial Markets. J. Mark. 1994, 58, 45–55.
  7. Porter, M.E. Competitive Strategy: Techniques for Analyzing Industries and Competitors; Free Press: New York, NY, USA, 1980.
  8. Pant, G.; Sheng, O.R.L. Web Footprints of Firms: Using Online Isomorphism for Competitor Identification. Inf. Syst. Res. 2015, 26, 188–209.
  9. Holsapple, C.W.; Hsiao, S.-H.; Pakath, R. Business social media analytics: Characterization and conceptual framework. Decis. Support Syst. 2018, 110, 32–45.
  10. Lee, D.; Hosanagar, K.; Nair, H.S. Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook. Manag. Sci. 2018, 64, 5105–5131.
  11. Rishika, R.; Kumar, A.; Janakiraman, R.; Bezawada, R. The Effect of Customers’ Social Media Participation on Customer Visit Frequency and Profitability: An Empirical Investigation. Inf. Syst. Res. 2012, 24, 108–127.
  12. Opesade, A.O. Twitter-Mediated Enterprise–Customer Communication: Case of Electricity Distribution Services in a Developing Country. Soc. Sci. Comput. Rev. 2021, 40, 1578–1594.
  13. Martín-Rojas, R.; Garrido-Moreno, A.; García-Morales, V.J. Fostering Corporate Entrepreneurship with the use of social media tools. J. Bus. Res. 2020, 112, 396–412.
  14. Kumar, A.; Bezawada, R.; Rishika, R.; Janakiraman, R.; Kannan, P.K. From Social to Sale: The Effects of Firm-Generated Content in Social Media on Customer Behavior. J. Mark. 2016, 80, 7–25.
  15. Hogenboom, F.; Frasincar, F.; Kaymak, U.; de Jong, F.; Caron, E. A Survey of event extraction methods from text for decision support systems. Decis. Support Syst. 2016, 85, 12–22.
  16. Lefever, E.; Hoste, V. A Classification-based Approach to Economic Event Detection in Dutch News Text. In Proceedings of the International Conference on Language Resources and Evaluation; European Language Resources Association (ELRA): Paris, France, 2016; pp. 330–335.
  17. Sheng, J.; Lan, H. Business failure and mass media: An analysis of media exposure in the context of delisting event. J. Bus. Res. 2019, 97, 316–323.
  18. Glance, N.; Hurst, M.; Nigam, K.; Siegler, M.; Stockton, R.; Tomokiyo, T. Deriving marketing intelligence from online discussion. In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, Chicago, IL, USA, 21–24 August 2005; pp. 419–428.
  19. Netzer, O.; Feldman, R.; Goldenberg, J.; Fresko, M. Mine Your Own Business: Market-Structure Surveillance through Text Mining. Mark. Sci. 2012, 31, 521–543.
  20. Xu, K.; Liao, S.S.; Li, J.; Song, Y. Mining comparative opinions from customer reviews for Competitive Intelligence. Decis. Support Syst. 2011, 50, 743–754.
  21. Wu, S.; Rand, W.; Raschid, L. Recommendations in social media for brand monitoring. In Proceedings of the Fifth ACM Conference on Recommender Systems, Chicago, IL, USA, 23–27 October 2011; pp. 345–348.
  22. Klostermann, J.; Plumeyer, A.; Böger, D.; Decker, R. Extracting brand information from social networks: Integrating image, text, and social tagging data. Int. J. Res. Mark. 2018, 35, 538–556.
  23. Hays, S.; Page, S.J.; Buhalis, D. Social media as a destination marketing tool: Its use by national tourism organisations. Curr. Issues Tour. 2013, 16, 211–239.
  24. Schivinski, B. Eliciting brand-related social media engagement: A conditional inference tree framework. J. Bus. Res. 2021, 130, 594–602.
  25. Onishi, H.; Manchanda, P. Marketing activity, blogging and sales. Int. J. Res. Mark. 2012, 29, 221–234.
  26. Colicev, A.; Kumar, A.; O’Connor, P. Modeling the relationship between firm and user generated content and the stages of the marketing funnel. Int. J. Res. Mark. 2019, 36, 100–116.
  27. McAfee, R.P.; McMillan, J. Competition and Game Theory. J. Mark. Res. 1996, 33, 263–267.
  28. Ailawadi, K.L.; Kopalle, P.K.; Neslin, S.A. Predicting Competitive Response to a Major Policy Change: Combining Game-Theoretic and Empirical Analyses. Mark. Sci. 2005, 24, 12–24.
  29. Chevalier-Roignant, B.; Trigeorgis, L. Competitive Strategy Options and Games; The MIT Press: Cambridge, MA, USA, 2011.
  30. Han, S.; Ye, Y.; Fu, X.; Chen, Z. Category role aided market segmentation approach to convenience store chain category management. Decis. Support Syst. 2014, 57, 296–308.
  31. Smith, W.R. Product Differentiation and Market Segmentation as Alternative Marketing Strategies. J. Mark. 1956, 21, 3–8.
  32. Montgomery, D.B.; Moore, M.C.; Urbany, J.E. Reasoning About Competitive Reactions: Evidence from Executives. Mark. Sci. 2005, 24, 138–149.
  33. Bergen, M.; Peteraf, M. Competitor identification and competitor analysis: A broad-based managerial approach. Manag. Decis. Econ. 2002, 23, 157–169.
  34. Bloodgood, J.M.; Bauerschmidt, A. Competitive Analysis: Do Managers Accurately Compare Their Firms to Competitors? J. Manag. Issues 2002, 14, 418–434.
  35. Clark, B.H. Managerial identification of competitors: Accuracy and performance consequences. J. Strateg. Mark. 2011, 19, 209–227.
  36. Gilbert, R.A. Bank Market Structure and Competition: A Survey. J. Money Credit Bank. 1984, 16, 617–645.
  37. Reger, R.K.; Palmer, T.B. Managerial Categorization of Competitors: Using Old Maps to Navigate New Environments. Organ. Sci. 1996, 7, 22–39.
  38. Shubik, M.; Levitan, R. Market Structure and Behavior; Harvard University Press: Cambridge, MA, USA, 2013.
  39. Ciliberto, F.; Murry, C.; Tamer, E. Market Structure and Competition in Airline Markets. J. Political Econ. 2021, 129, 2995–3038.
  40. Hooley, G.; Greenley, G. The resource underpinnings of competitive positions. J. Strateg. Mark. 2005, 13, 93–116.
  41. Urban, G.L.; Johnson, P.L.; Hauser, J.R. Testing Competitive Market Structures. Mark. Sci. 1984, 3, 83–112.
  42. Fabrizio, C.M.; Kaczam, F.; de Moura, G.L.; da Silva, L.S.C.V.; da Silva, W.V.; da Veiga, C.P. Competitive advantage and dynamic capability in small and medium-sized enterprises: A systematic literature review and future research directions. Rev. Manag. Sci. 2022, 16, 617–648.
  43. Peteraf, M.A.; Bergen, M.E. Scanning dynamic competitive landscapes: A market-based and resource-based framework. Strateg. Manag. J. 2003, 24, 1027–1041.
  44. Roberts, P.W.; Amit, R. The Dynamics of Innovative Activity and Competitive Advantage: The Case of Australian Retail Banking, 1981 to 1995. Organ. Sci. 2003, 14, 107–122.
  45. Liu, Y.; Jiang, C.; Zhao, H. Assessing product competitive advantages from the perspective of customers by mining user-generated content on social media. Decis. Support Syst. 2019, 123, 113079.
  46. Liu, Y.; Qian, Y.; Jiang, Y.; Shang, J. Using favorite data to analyze asymmetric competition: Machine learning models. Eur. J. Oper. Res. 2020, 287, 600–615.
  47. Zhao, K.; Cong, G.; Chin, J.-Y.; Wen, R. Exploring market competition over topics in spatio-temporal document collections. VLDB J. 2019, 28, 123–145.
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