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Firms are increasingly seeking to harness the potential of social networks for marketing purposes. Therefore, marketers are interested in understanding the antecedents and consequences of relationship formation within networks and in predicting interactivity among users. The authors develop an integrated statistical framework for simultaneously mod...
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Citations
... These networks are typically represented by edges among nodes in distinct layers (Mucha et al., 2010;Kivelä et al., 2014;Boccaletti et al., 2014). For instance, in social sciences, individuals are connected through various social platforms such as Facebook, Twitter, Instagram, and emails, forming a multi-layer social network with each layer denoting a distinct type of social relationship, ranging from friendships to professional connections (Ansari et al., 2011;Oselio et al., 2014). Similarly, in biological sciences, proteins engage in interactions through various biological processes or different stages of development, resulting in a multi-layer protein-protein interaction network where different layers signify different types of biological interactions or different times (Bakken et al., 2016; The multi-layer stochastic block model (MLSBM) is a powerful model in describing the hidden community structure of multi-layer networks. ...
Community detection is a crucial problem in the analysis of multi-layer networks. In this work, we introduce a new method, called regularized debiased sum of squared adjacency matrices (RDSoS), to detect latent communities in multi-layer networks. RDSoS is developed based on a novel regularized Laplacian matrix that regularizes the debiased sum of squared adjacency matrices. In contrast, the classical regularized Laplacian matrix typically regularizes the adjacency matrix of a single-layer network. Therefore, at a high level, our regularized Laplacian matrix extends the classical regularized Laplacian matrix to multi-layer networks. We establish the consistency property of RDSoS under the multi-layer stochastic block model (MLSBM) and further extend RDSoS and its theoretical results to the degree-corrected version of the MLSBM model. The effectiveness of the proposed methods is evaluated and demonstrated through synthetic and real datasets.
... In the context of IS implementations, it is necessary to account for technology-induced conflicting relationships to understand how two entities can jointly shape outcomes. Dissonant RM thus allows researchers to move beyond the limited relational approach of analyzing only a single type of stakeholder relationship (Ansari et al. 2011). ...
In this study we investigate information system (IS) failures by leveraging a novel construct—dissonant relational multiplexity (RM)—to develop a unique perspective of these failures. Dissonant RM exists when two organizational stakeholders have multiple types of relationships that are in conflict. To investigate the salience of dissonant RM in IS failures, we use a case study combined with the analysis procedures of the grounded theory methodology (GTM) to examine a major failure in enterprise resource planning (ERP) implementation. Our analysis and theorization highlight that RM became increasingly dissonant in the relationships between key organizational stakeholders because of a shift in technological frames, which represent cognitive perceptions about technology. Further, a key insight from our findings is that the move to dissonant RM occurred through a process that we term relational unbalancing. In addition, we also find evidence of an opposing relational balancing process that was used by stakeholders to address dissonant RM. Such stakeholder efforts were often undermined by inherent constraints in the implemented technology. The relational balancing efforts were not productive, and the dissonant RM continued to exist, ultimately contributing to the failure of the ERP implementation. Our study shows that IS failures are characterized by elements of both determinism and indeterminism, are undoubtedly sociotechnical in nature, and are shaped by technological constraints and stakeholder perceptions of those constraints. From a practical standpoint, our study highlights the importance of managing multiplex stakeholder relationships in an IS implementation process, especially when the multiplexity is shaped by the technology.
... Surprisingly only 28% of respondents agreed to appreciating companies responding to inquiries, which indicates that the majority of Social Media users prefers to keep their platforms in a private environment. The high number of respondents disagreeing with question 33 (40.7%) shows that peer pressure, in this case to always have the same interests as friends, was not essential for the respondents' behaviour on these platforms, depicting the differences in social structures on Social Media sites (Ansari et al., 2011;Kleinberg, 2008). Based on this, companies would be able to access data on customers with more diverse interests, increasing the companies' ability to assess a broader range of perceptions (Table 7). ...
... Research on customers is conducted in their own interest rather than just being 'researched' (Ang, 2011;Maklan et al., 2008) (A2) Reaching a large number of customers (Sterne, 1955) (Ansari et al., 2011;Zsolt et al., 2011) (AC1) Customer reaction (e.g., passed message along) (Sterne, 1955) (C4) Online relationships are formed to forge collaborative relationships (Ansari et al., 2011:725) (CBC1) SM helps to build social network; Increase social capital (Ang, 2011;Martínez Alemán & Lynk Wartman, 2009) ...
This study examines the possibility of enhancing Customer Relationship Management through Social Media. As Social Media is a trending topic in companies’ strategies, the aim of this research is to find a possible link between the use of Social Media, and its impact on Fashion retails’ Customer Relationship Management in both UK and Germany. The use of Social Media platforms was explored by using a semi-structured survey. Significant differences were found in the usage of social media by age group and sex. The results revealed that using Social Media in marketing strategies for fashion retailors would be most effective when targeting younger demographics, offering companies the benefit of longer customer lifetime value when enhancing their Customer Relationship Management through Social Media.
... Studies have shown that high degree centrality facilitates the focal seller's product sales (Stephen and Toubia 2010), information diffusion (Iyengar et al. 2011), marketing effectiveness (Hinz et al. 2011), and social commerce success (Ansari et al. 2018). Betweenness has also been found to be informative in predicting the marketing performance (Hinz et al. 2011), product diffusion (Katona et al. 2011), and social commerce success of a focal seller (Ansari et al. 2011(Ansari et al. , 2018. ...
Purpose
Drawing on social network and information diffusion theories, the authors study the impact of the structural characteristics of a seller’s local social network on her promotion effectiveness in social commerce.
Design/methodology/approach
The authors define a local social network as one formed by a focal seller, her directly connected users and all links among these users. Using data from a large social commerce website in China, the authors build econometric models to investigate how the density, grouping and centralization of local social networks affect the number of likes received by products posted by sellers.
Findings
Local social networks with low density, grouping and centralization are associated with more likes on sellers’ posted products. The negative effects of grouping and centralization are reduced when density is high.
Originality/value
The paper deepens the understanding of the determinants of social commerce success from a network structure perspective. In particular, it draws attention to the role of sellers’ local social networks, forming a foundation for future research on social commerce.
... Sundararajan et al. (2013) point out the importance of these tacit connections as "information" relevant for decision making, an idea that has been previously studied under the domain of collaborative filtering. On a related note, Ansari et al. (2011) discuss the potential of social network analysis for marketing purposes. They stress the importance of understanding the antecedents and consequences of relationship formation within online social networks and in predicting future outcomes. ...
... While most existing research focuses on descriptive and predictive properties of information networks (Malhotra and Bhattacharyya 2022;Oestreicher-Singer and Sundararajan 2012;Zhang, Bhattacharyya, and Ram 2016), statistical analyses of the generative features of information networks on social media is a relatively understudied topic. Previous work by Ansari, Koenigsberg, and Stahl (2011) is one of the few marketing studies to explore a statistical framework for modeling the connectivity structure of multiple network relationships of different types on a common set of actors. ...
The rise in electronic interactions has made information networks ubiquitous. Information networks result from users' activities on information systems (or on electronic platforms) and can include various types of social structures of firms/brands. There has been a growing consensus among researchers to understand the structural relationship among members of an information network as a first step to utilizing these networks for marketing purposes. A better understanding of information networks can help marketers develop a clearer overview of the interests of their brand communities on social media and effectively predict marketing outcomes. In this paper, we use extant statistical models, in particular Exponential Random Graph Models (ERGM), to understand the drivers of co-engagement patterns within brand networks on Twitter and to predict the future connectivity patterns between brands. Unlike conventional social networks that involve direct interaction between individuals, edges within a brand network arise due to common followership activity between Twitter users. The ERGM model reveals a mix of network and individual level brand characteristics responsible for network formation, thereby disclosing a list of significant brand (and network) features likely associated with users co-following brands on social media. Marketing implications of the work are also discussed. Statement of Intended Contribution The study of online brand (or product) networks has become a crucial area of marketing research. With the rise of information technology, massive social network data is increasingly available for addressing marketing problems that were traditionally solved using survey-based approaches. Information networks, resulting from users' activities on information systems or on electronic platforms, can include various types of digital artifacts and social structures. One such type of information network is a brand network where links between brands arise due to common followership activity of digital users. Specifically, the concept of using network overlap between brands, in the form of common followers on Twitter, has been utilized for automatically mining brand attribute perceptions, identifying brand alliance opportunities, and for understanding content sharing behavior. Despite the rich usage of network overlap for marketing purposes, the
... Sundararajan et al. (2013) point out the importance of these tacit connections as "information" relevant for decision making, an idea that has been previously studied under the domain of collaborative filtering. On a related note, Ansari et al. (2011) discuss the potential of social network analysis for marketing purposes. They stress the importance of understanding the antecedents and consequences of relationship formation within online social networks and in predicting future outcomes. ...
... While most existing research focuses on descriptive and predictive properties of information networks (Malhotra and Bhattacharyya 2022;Oestreicher-Singer and Sundararajan 2012;Zhang, Bhattacharyya, and Ram 2016), statistical analyses of the generative features of information networks on social media is a relatively understudied topic. Previous work by Ansari, Koenigsberg, and Stahl (2011) is one of the few marketing studies to explore a statistical framework for modeling the connectivity structure of multiple network relationships of different types on a common set of actors. ...
The rise in electronic interactions has made information networks ubiquitous. Information networks result from users’ activities on information systems (or on electronic platforms) and can include various types of social structures of firms/brands. There has been a growing consensus among researchers to understand the structural relationship among members of an information network as a first step to utilizing these networks for marketing purposes. A better understanding of information networks can help marketers develop a clearer overview of the interests of their brand communities on social media and effectively predict marketing outcomes. In this paper, we use extant statistical models, in particular Exponential Random Graph Models (ERGM), to understand the drivers of co-engagement patterns within brand networks on Twitter and to predict the future connectivity patterns between brands. Unlike conventional social networks that involve direct interaction between individuals, edges within a brand network arise due to common followership activity between Twitter users. The ERGM model reveals a mix of network and individual level brand characteristics responsible for network formation, thereby disclosing a list of significant brand (and network) features likely associated with users co-following brands on social media. Marketing implications of the work are also discussed.
... By having access to these new networking platforms, customers are better able to search for information on products and services and more easily evaluate the processes than they could using the more traditional methods of communication (Hennig-Thurau et al., 2010). Therefore, companies must adapt to the changes within new communication channels and embrace the opportunities offered by these new media platforms (Ansari et al., 2011). ...
... The findings of this study, therefore, reflect the notion that the CRM of companies can be enhanced through the efficient use of social networking platforms. These findings are in line with the majority of the literature that also highlights the effectiveness of social networking platforms as a CRM tool (Kleinberg, 2008;Mangold and Faulds, 2009;Ogneva, 2010;Straley, 2009;Sterne, 2010;Hennig-Thurau et al., 2010;Heckadon, 2010;Ansari et al., 2011;Nitzan and Libai, 2011;Johnson, 2011). ...
... The finding of the criterion 'relationships' as being essential to CRM is in line with what Soares et al. (2012, p.48) have exploited in their research, namely that "... trust is positively related to social relationships in s[ocial] n[etworks] ..." The results of their study also underline how relevant offline proximity is to online relationships. The latter finding agrees with that of Ansari et al. (2011), who stated that offline proximity is essential to the formation of relationships. Heckadon (2010) and Hennig-Thurau et al. (2010) stated that online communities are essential within social networking sites, and that these communities play an important role within these platforms. ...
... By having access to these new networking platforms, customers are better able to search for information on products and services and more easily evaluate the processes than they could using the more traditional methods of communication (Hennig-Thurau et al., 2010). Therefore, companies must adapt to the changes within new communication channels and embrace the opportunities offered by these new media platforms (Ansari et al., 2011). ...
... The findings of this study, therefore, reflect the notion that the CRM of companies can be enhanced through the efficient use of social networking platforms. These findings are in line with the majority of the literature that also highlights the effectiveness of social networking platforms as a CRM tool (Kleinberg, 2008;Mangold and Faulds, 2009;Ogneva, 2010;Straley, 2009;Sterne, 2010;Hennig-Thurau et al., 2010;Heckadon, 2010;Ansari et al., 2011;Nitzan and Libai, 2011;Johnson, 2011). ...
... The finding of the criterion 'relationships' as being essential to CRM is in line with what Soares et al. (2012, p.48) have exploited in their research, namely that "... trust is positively related to social relationships in s[ocial] n[etworks] ..." The results of their study also underline how relevant offline proximity is to online relationships. The latter finding agrees with that of Ansari et al. (2011), who stated that offline proximity is essential to the formation of relationships. Heckadon (2010) and Hennig-Thurau et al. (2010) stated that online communities are essential within social networking sites, and that these communities play an important role within these platforms. ...
... The cited marketing work includes both behavioral and mathematical modeling work on the dynamics of social influence within social networks and online communities (e.g., Ansari et al., 2011;Bagozzi & Dholakia, 2002;Brown, Broderick, & Lee, 2007;Godes & Mayzlin, 2004;Trusov at al., 2010), which has been used by IS researchers to build theories on user interaction with social media platforms. There is a range of basic behavioral work cited on topics such as identity motivations (Oyserman, 2009), communications valence (Mizerski, 1982), and the relationship between consumer emotions and behavior (Soscia, 2007). ...
This paper gives a systematic research review at the boundary of the information systems (IS) and marketing disciplines. First, a historical overview of these disciplines is given to put the review into context. This is followed by a bibliographic analysis to select articles at the boundary of IS and marketing. Text analysis is then performed on the selected articles to group them into homogeneous research clusters, which are refined by selecting "distinct" articles that best represent the clusters. The citation asymmetries between IS and marketing are noted and an overall conceptual model is created that describes the "areas of collaboration" between IS and marketing. Forward looking suggestions are made on how academic researchers can better interface with industry and how academic research at the boundary of IS and marketing can be further developed.
... --InsertFigure 3about here -- While special cases of the Bonacich centrality measure have appeared in the recent seeding literature, previous research, to the best of our knowledge, determined the value of a priori, and no research has considered negative values. If = 0, Equation (10) corresponds to (out) degree centrality, which is the most frequently used centrality measure in research in marketing on social networks(Ansari, et al. 2011;Aral and Walker, 2011;Braun and Bonfrer, 2011; Goldenberget al. 2009;Hinz et al. 2011;Iyengar et al. 2011;Katona et al. 2011;Lee et al. 2010;Trusov et al. 2010;Tucker, 2008; 7 Element í µí± í µí±í µí± 2 of matrix í µí°´2µí°´2 counts the number of paths of length two between individuals i and j, and thus captures second order connections. If i and j are not directly connected (i.e., í µí± í µí±í µí± = 0), but have one common friend, then í µí± í µí±í µí± 2 = 1. ...
Many firms try to leverage consumers’ interactions on social platforms as part of their communication strategies. However, information on online social networks only propagates if it receives consumers’ attention. This paper proposes a seeding strategy to maximize information propagation while accounting for competition for attention. The theory of exchange networks serves as the framework for identifying the optimal seeding strategy and recommends seeding people that have many friends, who, in turn, have only a few friends. There is little competition for the attention of those seeds’ friends, and these friends are therefore responsive to the messages they receive. Using a game-theoretic model, we show that it is optimal to seed people with the highest Bonacich centrality. Importantly, in contrast to previous seeding literature that assumed a fixed and nonnegative connectivity parameter of the Bonacich measure, we demonstrate that this connectivity parameter is negative and needs to be estimated. Two independent empirical validations using a total of 34 social media campaigns on two different large online social networks show that the proposed seeding strategy can substantially increase a campaign’s reach. The second study uses the activity network of messages exchanged to confirm that the effects are driven by competition for attention.
This paper was accepted by Anandhi Bharadwaj, information systems.
