A framework for big data analytics in commercial social networks: A case study on sentiment analysis and fake review detection for marketing decision-making

User-generated content about brands is an important source of big data that can be transformed into valuable information. A huge number of items are reviewed and rated by consumers on a daily basis, and managers have a keen interest in real-time monitoring of this information to improve decision-making. The main challenge is to mine reliable textual consumer opinions, and automatically use them to rate the best products or brands. We propose a framework to automatically analyse these reviews, transforming negative and positive user opinions in a quantitative score. Sentiment analysis was employed to analyse online reviews on Amazon. The Fake Review Detection Framework—FRDF— detects and removes fake reviews using Natural Language Processing technology. The FRDF was tested on reviews of products from high-tech industries. Brands were rated according to consumer sentiment. The findings demonstrate that brand managers and consumers would find this tool useful, in combination with the 5-Star score, for more comprehensive decision-making. For instance, the FRDF ranks the best products by price alongside their respective sentiment value and the 5-Star score.     

Achieving superior organizational performance via big data predictive analytics: A dynamic capability view

The art of unwinding voluminous data expects the expertise in analyzing meaningful decisions out of the acquired information. To encounter new age challenges, practitioners are trying hard to shatter the constraints and work edge-to-edge to achieve higher performance (Market, Financial and Operational performance). It is evident that organizations desire to exploit maximum of their injected resources, but often fail to reap their actual potential. Developing resource-based capabilities stands out to be the most concerned aspect for the firms in recent times, and the same is studied by the previous scholars. In the dearth of literature, it is challenging to find out evidence which marks up the effect of strategic resources in the development of dynamic organizational capability. This study is a two-fold attempt to examine the relationship between organizational capabilities, i.e. big data predictive analytics while achieving superior organizational performance; also, examining the effect of control variables on superior organizational of performance. We tested our research hypotheses using cross-sectional data of 209 responses collected using pre-tested single-informant questionnaire. The results underpin criticality human factor while developing analytical capabilities dynamic in nature in the process of achieving superior performance.     

Building relationship innovation in global collaborative partnerships: big data analytics and traditional organizational powers

This study examines how relationship innovation can be developed in global collaborative partnerships (alliances, joint ventures, mergers, and acquisitions). The recently emerging theory of big data analytics linked with traditional organizational powers has attracted a growing interest, but surprisingly little research has been devoted to this important and complex topic. Therefore, after developing the theoretical foundations, our study empirically quantifies the links between the theoretical constructs based on the data collected from chief executive officers, managing directors, and heads of departments who work in contemporary global data‐and‐information driven collaborative partnerships. The results from structural equation modeling indicate that the relationship innovation depends on the power of big data analytics and non‐mediated powers (NMP, expert and referent). The power of big data analytics also mediates the correlation between NMP and relationship innovation. However, mediated powers (coercive and manipulative) negatively affect the power of big data analytics and relationship innovation. The interaction effects further depict that analytically powered partnerships have better relationship innovation compared with those which focus less on the analytical power. Consequently, the contributions of this study provide a deeper understanding of mechanisms of how modern collaborative partnerships can use big data analytics and traditional organizational powers to co‐create relationship innovation.     

An empirical case study on Indian consumers' sentiment towards electric vehicles: A big data analytics approach

Today, climate change due to global warming is a significant concern to all of us. India's rate of greenhouse gas emissions is increasing day by day, placing India in the top ten emitters in the world. Air pollution is one of the significant contributors to the greenhouse effect. Transportation contributes about 10% of the air pollution in India. The Indian government is taking steps to reduce air pollution by encouraging the use of electric vehicles. But, success depends on consumer's sentiment, perception and understanding towards Electric Vehicles (EV). This case study tried to capture the feeling, attitude, and emotions of Indian consumers' towards electric vehicles. The main objective of this study was to extract opinions valuable to prospective buyers (to know what is best for them), marketers (for determining what features should be advertised) and manufacturers (for deciding what features should be improved) using Deep Learning techniques (e.g Doc2Vec Algorithm, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN)). Due to the very nature of social media data, big data platform was chosen to analyze the sentiment towards EV. Deep Learning based techniques were preferred over traditional machine learning algorithms (Support Vector Machine, Logistic regression and Decision tree, etc.) due to its superior text mining capabilities. Two years data (2016 to 2018) were collected from different social media platform for this case study. The results showed the efficiency of deep learning algorithms and found CNN yield better results in-compare to others. The proposed optimal model will help consumers, designers and manufacturers in their decision-making capabilities to choose, design and manufacture EV.     

B2B analytics in the airline market: Harnessing the power of consumer big data

This paper utilizes market-level data to explore the relative performance of individual companies amongst defined competitors. We show the potential of using consumer clickstream data, an important type of big data, to create a new set of B2B analytical frameworks. In the markets where complex interactions between competitors, search intermediaries and consumers create a network, B2B relationships can be inferred from consumer search patterns, and can then be modeled to gauge the online performance. A commercial dataset from ComScore’s US panel of one million users is used to illustrate a new approach to measure and evaluate the online performance of competitors in the US airline market. The methodology and associated performance framework demonstrate the potential for new forms of market intelligence based on the visualization of market networks, online performance calculated from matrix algorithms, the measurement of the impact of search intermediaries, and the identification of latent relationships. This research makes theoretical and empirical contributions to the debate on the use of big data for B2B market analytics. B2B managers can use this approach to extend their network horizon from an egocentric to a network view of competition and map out their competitive landscape from the perspective of the customer.     

Organizational intention to adopt big data in the B2B context: An integrated view

Grounded in the diffusion of innovation theory (DOI), institutional theory, configuration theory, and technology-organization-environment (TOE) framework, the study proposes a model incorporating factors in technological, organizational and environmental contexts that may influence an organization's decision to adopt big data strategies. Using survey data collected from Chinese companies, eight factors in three categories are tested utilizing a structural equation modeling (SEM) and Fuzzy-set Qualitative Comparative Analysis (fsQCA). The empirical results show that the factors relative advantage, technological competence, technology resources, support from top management, competitive pressure, and the regulatory environment all have a significant impact on the organizational adoption of big data. These findings contribute to the development of a better understanding of precisely how the big data diffusion process across industries functions in B2B practice.     

A comparison of social media marketing between B2B,B2C and mixed business models

This paper explores the implicit assumption in the growing body of literature that social media usage is fundamentally different in business-to-business (B2B) companies than in the extant business-to-consumer (B2C) literature. Sashi's (2012) customer engagement cycle is utilized to compare organizational practices in relation to social media marketing in B2B, B2C, Mixed B2B/B2C and B2B2C business models. Utilizing 449 responses to an exploratory panel based survey instrument, we clearly identify differences in social media usage and its perceived importance as a communications channel. In particular we identify distinct differences in the relationship between social media importance and the perceived effectiveness of social media marketing across business models. Our results indicate that B2B social media usage is distinct from B2C, Mixed and B2B2C business model approaches. Specifically B2B organizational members perceive social media to have a lower overall effectiveness as a channel and identify it as less important for relationship oriented usage than other business models.     

Real-time big data processing for instantaneous marketing decisions: A problematization approach

The collection of big data from different sources such as the internet of things, social media and search engines has created significant opportunities for business-to-business (B2B) industrial marketing organizations to take an analytical view in developing programmatic marketing approaches for online display advertising. Cleansing, processing and analyzing of such large datasets create challenges for marketing organizations — particularly for real-time decision making and comparative implications. Importantly, there is limited research for such interplays. By utilizing a problematization approach, this paper contributes through the exploration of links between big data, programmatic marketing and real-time processing and relevant decision making for B2B industrial marketing organizations that depend on big data-driven marketing or big data-savvy managers. This exploration subsequently encompasses appropriate big data sources and effective batch and real-time processing linked with structured and unstructured datasets that influence relative processing techniques. Consequently, along with directions for future research, the paper develops interdisciplinary dialogues that overlay computer-engineering frameworks such as Apache Storm and Hadoop within B2B marketing viewpoints and their implications for contemporary marketing practices.     

Tensions and ties in social media networks: Towards a model of understanding business relationship development and business performance enhancement through the use of LinkedIn

The era of social media networks has created significant opportunities for business relationship development yet there exists a paucity of research in this area. To address this, this paper identifies four key tensions within the current literature: relational versus transactional exchanges, emergent versus strategic social media network development, the pace of social media network formation versus the development of trust, and the notions of sharing and reciprocity versus competitive advantage. This study draws on the principles of netnography, incorporating data from 554 LinkedIn group interactions and 12 interviews with professionals in one global industry to provide insight into business relationship development stemming from one social media network. Significant contributions to theoretical and practical knowledge are made through the recognition of tensions in the literature, the application of the notion of Granovetter's ties to a contemporary context and the novel use of netnography. Furthermore, the resultant model conceptualises the use of social media networking in building networks and relationships which lead to new business and enhance business performance.     

You have not been archiving emails for no reason! Using big data analytics to cluster B2B interest in products and services and link clusters to financial performance

The potential of big data analytics when it comes to gaining business insights, such as market trends and consumer preferences, has captured the interest of both scholars and business practitioners. However, the extant literature has so far provided limited empirical evidence to demonstrate how big data analytics can create business value. To address this research gap, this paper followed a novel big data analytical approach that involved analysing email archives about product/services demand clusters in a B2B setting. We analysed 621 k emails exchanged between 2009 and 2018. We identified a number of discussion clusters that were considered proxies for the interest buyers expressed in the products/services on offer. These clusters and associated discussion trends were linked to the company's revenues and financial performance, showing good predictive power. In doing this, we have demonstrated how widely available data, such as emails, which all companies have, can be used to underpin new methods for the early identification and monitoring of product demand trends, informing marketing strategies.     

Pursuing consumer empowerment in the age of big data: A comprehensive regulatory framework for data brokers

Data brokers play a central role in the big data ecosystem, compiling personal data from numerous sources and subsequently selling them to businesses worldwide. Such practices, however, generally occur clandestinely without consumers' knowledge or consent and can harm consumers in a discriminatory manner. In recent years, US policymakers have been urged to regulate data brokers by promoting individual choice but have failed, whereas the European Union has introduced broad data privacy guarantees for consumers and a data protection regime that limits data brokers' activities if consumers' consent has not been obtained for specific purposes. Because of the power imbalance between consumers and data brokers, the author argues that the European Union's coherent data protection framework serves as an effective model for regulating data brokers.     

Role conflict and ambiguity confronting transnational business networkers: Contrasting social stigma and relational risks for Chinese and Western boundary spanners

Our research explores the role conflict, ambiguity and resulting social stigma and relational risks that transnational B2B networkers face when facilitating the formation of new exchange dyads between firms from distinct and distant cultures, i.e. Chinese and Western. We integrate literature from institution theory and role theory to establish the comparative framework. This allows us to explore: (1) what stigma and relational risks guanxi brokers assume when facilitating transnational business connections; (2) if Western brokers encounter similar stigma and negative consequences that threaten their professional standing and the formation process; and, (3) the lessons from both that transnational firms can learn and incorporate into best practices. We conclude our paper with managerial implications and suggestions for further research.     

Brand innovation and social media: Knowledge acquisition from social media,market orientation,and the moderating role of social media strategic capability

The study examines the relationships between knowledge acquisition from social media, two forms of market orientation (proactive and reactive), social media strategic capability, and brand innovation strategy in the context of China's online technology industry. Analysis of 357 online technology ventures, created during the past 6 years, suggests that brand innovation is affected by both knowledge acquisition from social media and market orientation. Social media strategic capability positively affects brand innovation and acts as a moderator between knowledge acquisition, market orientation, and brand innovation. It further enhances both types of market orientations in achieving brand innovation, suggesting that on social media, customer's needs, both expressed and latent (or unexpressed), can be identified more comprehensively than that of the traditional setting. Hence, the context of social media provides a different set of rules for competition and strategic behavior, in which online technology ventures should note. Implications are useful to improve the current understanding of social media brand innovation strategy, here in China's dynamic social media scene.     

Videotex for news and business data: Comparison of user response to two information retrieval applications

An empirical comparison of user response to two information services offered by a prototype videotex system indicated that the business (ie farm market data) service was rated as more efficient and that users browsed more in that service than in the consumer (ie news) service. Further, both efficiency ratings and amount of browsing were more strongly correlated with amount of service use, ratings of service value and ratings of dependence upon the service for the business service than for the consumer service. Browsing thus seems to be a utitarian surveillance behaviour rather than a communication play behaviour in this setting.     

A big data driven framework for demand-driven forecasting with effects of marketing-mix variables

This study aims to investigate the contributions of promotional marketing activities, historical demand and other factors to predict, and develop a big data-driven fuzzy classifier-based framework, also called “demand-driven forecasting,” that can shape, sense and respond to real customer demands. The availability of timely information about future customer needs is a key success factor for any business. For profit maximization, manufacturers want to sense demand signals and shape future demands using price, sales, promotion and others economic factors so that they can fulfil customer's orders immediately. However, most demand forecasting systems offer limited insight to manufacturers as they fail to capture contemporary market trends, product seasonality and the impact of forecasting on the magnitude of the bullwhip effect. This paper aims to improve the accuracy of demand forecasts. In order to achieve this, a back-propagation neural network-based model is trained by fuzzy inputs and compared with benchmark forecasting methods on a time series data, by using historical demand and sales data in combination with advertising effectiveness, expenditure, promotions, and marketing events data. A statistical analysis is conducted, and the experiments show that the method used in the proposed framework outperforms in optimality, efficiency and other statistical metrics. Finally, some invaluable insights for managers are presented to improve the forecast accuracy of fuzzy neural networks, develop marketing plans for products and discuss their implications in several fields.     

The coexistence of transaction and relational marketing: Insights from the Chinese business context

Transaction and relational approaches to marketing have been promoted as alternative forms but recent evidence suggests that they may coexist. We explore this in the context of China. If firms operating in a highly relational society, such as China, exhibit transactional behavior, these two approaches must be compatible. We argue that Chinese values give rise to business practices consistent with both transactional and relational perspectives. These are summarized in six propositions for research. Theory building and managerial practice could benefit from thinking in terms of how elements from the two perspectives can be brought together rather than remain as alternatives.     

The use of social media in sales: Individual and organizational antecedents,and the role of customer engagement in social media

There is recognition that social media can benefit personal selling and sales management, especially in the B2B context. This research draws on interactional psychology theory to propose and test a model of usage of social media in sales, analyzing individual, organizational, and customer-related factors. We find that organizational competence and commitment with social media are key determinants of social media usage in sales, as well as individual commitment. Customer engagement with social media also predicts social media usage in sales, both directly and (mostly) through the individual and organizational factors analyzed, especially organizational competence and commitment. Finally, we find evidence of synergistic effects between individual competence and commitment, which is not found at the organizational level. We conduct multiple regression analysis of data obtained by surveying 220 sales executives in the United States.     

Understanding liability of foreignness in an Asian business context: A study of the Korean asset management industry

This study provides an in-depth understanding of the liability of foreignness (LOF) in an Asian business context. Based on previous literature, we distinguish the two distinct cost components that comprise LOF: The cost of foreignness and the cost of multinationality. Whereas the former refers to the costs incurred by foreign firms when they develop location-specific advantages in a host country environment, the latter refers to the costs associated with an MNC’s multinational operation, more specifically, transferring firm-specific advantages from the home country (or elsewhere) and adapting them to a particular host country context. Based on this distinction, we investigate whether and how persistently each of these costs exists in an Asian business environment. Our data on the Korean asset management industry support the presence of both costs, resulting in lower performance increase of foreign firms relative to local ones from utilizing location- and firm-specific advantages, respectively. Furthermore, in our study setting, compared to the cost of foreignness, the cost of multinationality persists longer in the market, suggesting that the latter is more difficult and takes a longer time for MNCs to mitigate than the former. Our results provide important insights into detailed aspects of strategic challenges confronted by MNCs in the Asian business context from which they can derive effective strategic responses.     

Differences in large and small firm responses to environmental context: strategic implications from a comparative analysis of business formations

Despite growing recognition of some strategic advantages held by small firms, little comparative research has been performed on the advantages and disadvantages accruing to firm size. In order to delineate the differential responses of small and large businesses to their environmental context, we perform a comparative analysis of the impact of industry structural characteristics on the formation of large and small businesses in a large sample of U.S. manufacturing industries from 1977 to 1987. The results suggest that small businesses possess certain resources that allow them to overcome some barriers which create greater difficulties for their larger counterparts, as well as allow small businesses to exploit certain industry opportunities more readily than larger ones. © 1998 John Wiley & Sons, Ltd.