Impact of Social Media on the Firm’s Knowledge Exploration and Knowledge Exploitation: The Role of Business Analytics Talent

Social media is one of the most disruptive technologies in executing a firm’s digital business transformation strategies. Does the firm’s ability to use social media affect its proficiency in exploring and exploiting knowledge? What should be the role of business analytics talent in this equation? We study theoretically and empirically these cutting-edge research questions. Our proposed research model argues that social media capability enables the development of knowledge exploration and knowledge exploitation, and business analytics talent exerts a positive reinforcing role in the impact of social media on knowledge exploration. We empirically tested the proposed research model with a secondary dataset from a sample of US firms using PLS path modeling. After running a robustness test by estimating eight alternatives/competing models, the empirical analysis revealed that social media capability is positively related to knowledge exploration and knowledge exploitation, but with a stronger effect on knowledge exploration. Moreover, business analytics talent plays a positive moderator role in the relationship between social media capability and knowledge exploration. This study contributes to the IS research by (1) introducing, developing, and operationalizing the concepts of social media capability and business analytics talent; and (2) theoretically arguing and empirically showing the pivotal role of social media capability in exploring new knowledge and the complementary role of business analytics talent. Our study also provides several critical lessons learned for top executives and proposes promising future IS research avenues.European Regional Development Fund (European Union)Spanish Government ECO201784138-P FPU14/01930 FPU13/01643Junta de Andalucia A-SEJ-154-UGR18Endowed Chair of Digital Business Transformation at Rennes School of BusinessSlovenian Research Agency - Slovenia P5-041

Business Value of Big Data Analytics:A Systems-Theoretic Approach and Empirical Test

Although big data analytics have been widely considered a key driver of marketing and innovation processes, whether and how big data analytics create business value has not been fully understood and empirically validated at a large scale. Taking social media analytics as an example, this paper is among the first attempts to theoretically explain and empirically test the market performance impact of big data analytics. Drawing on the systems theory, we explain how and why social media analytics create super-additive value through the synergies in functional complementarity between social media diversity for gathering big data from diverse social media channels and big data analytics for analyzing the gathered big data. Furthermore, we deepen our theorizing by considering the difference between small and medium enterprises (SMEs) and large firms in the required integration effort that enables the synergies of social media diversity and big data analytics. In line with this theorizing, we empirically test the synergistic effect of social media diversity and big data analytics by using a recent large-scale survey data set from 18,816 firms in Italy. We find that social media diversity and big data analytics have a positive interaction effect on market performance, which is more salient for SMEs than for large firms

Exploiting Time Series Analysis in Twitter to Measure a Campaign Process Performance

© 2017 IEEE. While there are several metrics to measure business process performance, recently there is an additional requirement from businesses to evaluate business processes based on their impact on users. In this work, we evaluate business process performance using social media analytics. We view a marketing campaign as a business process and we evaluate its performance based on its impact on the Twitter. We propose a new way to calculate the \u27follow\u27 relationship in Twitter based on the users\u27 reaction to the marketing campaign process activities and we use time series and sentiment analysis for defining and measuring performance. We re-build the Twitter graph based on users\u27 reactions to the marketing activities in time and we are using community detection algorithms to identify the size of the \u27follow\u27 community and thus we define metrics to calculate the impact of the marketing/campaign process. We evaluate our approach using a dataset for a given politician. We re-construct the campaign process as a set of activities on specific topics (promotions) in time using LDA. Our results show that social media analytics can be used as a valid metric for assessing business processes performance

Mining for Social Media: Usage Patterns of Small Businesses

Background: Information can now be rapidly exchanged due to social media. Due to its openness, Twitter has generated massive amounts of data. In this paper, we apply data mining and analytics to extract the usage patterns of social media by small businesses. Objectives: The aim of this paper is to describe with an example how data mining can be applied to social media. This paper further examines the impact of social media on small businesses. The Twitter posts related to small businesses are analyzed in detail. Methods/Approach: The patterns of social media usage by small businesses are observed using IBM Watson Analytics. In this paper, we particularly analyze tweets on Twitter for the hashtag #smallbusiness. Results: It is found that the number of females posting topics related to small business on Twitter is greater than the number of males. It is also found that the number of negative posts in Twitter is relatively low. Conclusions: Small firms are beginning to understand the importance of social media to realize their business goals. For future research, further analysis can be performed on the date and time the tweets were posted

Social Media Analytics and Information Privacy Decisions: Impact of User Intimate Knowledge and Co-ownership Perceptions

Social media analytics has been recognized as a distinct research field in the analytics subdomain that is developed by processing social media content to generate important business knowledge. Understanding the factors that influence privacy decisions around its use is important as it is often perceived to be opaque and mismanaged. Social media users have been reported to have low intimate knowledge and co-ownership perception of social media analytics and its information privacy decisions. This deficiency leads them to perceive privacy violations if firms make privacy decisions that conflict with their expectations. Such perceived privacy violations often lead to business disruptions caused by user rebellions, regulatory interventions, firm reputation damage, and other business continuity threats. Existing research had developed theoretical frameworks for multi-level information privacy management and called for empirical testing of which constructs would increase user self-efficacy in negotiating with firms for joint social media analytics decision making. A response to this call was studied by measuring the constructs in the literature that lead to normative social media analytics and its information privacy decisions. The study model was developed by combining the relevant constructs from the theory of psychological ownership in organizations and the theory of multilevel information privacy. From psychological ownership theory, the impact that intimate knowledge had on co-ownership perception of social media analytics was added. From the theory of multi-level information privacy, the impact of co-ownership perception on the antecedents of information privacy decisions: the social identity assumed, and information privacy norms used were examined. In addition, the moderating role of the cost and benefits components of the privacy calculus on the relationship between information privacy norms and expected information privacy decisions was measured. A quantitative research approach was used to measure these factors. A web-based survey was developed using survey items obtained from prior studies that measured these constructs with only minor wording changes made. A pilot-study of 34 participants was conducted to test and finalize the instrument. The survey was distributed to adult social media users in the United States of America on a crowdsourcing marketplace using a commercial online survey service. 372 responses were accepted and analyzed. The partial least squares structural equation modeling method was used to assess the model and analyze the data using the Smart partial least squares 3 statistical software package. An increase in intimate knowledge of social media analytics led to higher co-ownership perception among social media users. Higher levels of co-ownership perception led to higher expectation of adoption of a salient social identity and higher expected information privacy norms. In addition, higher levels of expectation of social information privacy norm use led to normative privacy decisions. Higher levels of benefit estimation in the privacy calculus negatively moderated the relationship between social norms and privacy decision making. Co-ownership perception did not have a significant effect on the cost estimation in social media analytics privacy calculus. Similarly, the cost estimation in the privacy calculus did not have a significant effect on the relationship between information privacy norm adoption and the expectation of a normative information privacy decision. The findings of the study are a notable information systems literature contribution in both theory and practice. The study is one of the few to further develop multilevel information privacy theory by adding the intimate knowledge construct. The study model is a contribution to literature since its one of first to combine and validate elements of psychological ownership in organization theory to the theory of multilevel information privacy in order to understand what social media users expect when social media analytics information privacy decisions are made. The study also contributes by suggesting approaches practitioners can use to collaboratively manage their social media analytics information privacy decisions which was previously perceived to be opaque and under examined. Practical suggestions social media firms could use to decrease negative user affectations and engender deeper information privacy collaboration with users as they seek benefit from social media analytics were offered

Factors affecting social media use by entrepreneurs and the impact of this use on the opportunity recognition process

Social media is believed to play an essential role in supporting entrepreneurial business and opportunity recognition. However, little is known about the factors that drive social media use and how social media capabilities impact entrepreneurial opportunity recognition. In exploring the role of social media to understand the potential role of social media use on entrepreneurial opportunity recognition, the study was based on the Technological-Organization-Environmental (TOE) and the Opportunity Recognition Frameworks. A mixedmethod study was conducted with data collected from a developed economy (Australia) and a developing country (Nigeria). An initial research model was developed based on the extant review of literature on social media use and entrepreneur opportunity recognition. Firstly, qualitative data were collected via interviews with 14 entrepreneurs, which identified eight factors under four broad categories (technology, environment, individual and social media platform factors) that influence entrepreneur social media use. Also, five social media capabilities were identified (networking, searching, observing, experimenting, and social media data analytics) to drive entrepreneurial opportunity recognition. Comparing the qualitative data with themes developed from published literature, the initial research model was revised. In the second stage, a survey of 568 entrepreneurs was used to validate the model and its associated relationships. The analysis suggests that four general factors influence social media use; platform perception, absorptive capacity, platform abuse and external pressure. In addition, the use of social media was found to influence opportunity recognition through four of the five identified capabilities: searching, observing, experimenting, and data analytics. However, the findings indicate differences on how social media capability drives opportunity recognition amongst entrepreneur in Australia and Nigeria, which can be explained based on their individualist and collectivist culture respectively. Interestingly, the multi-group analysis revealed that the influence of social media capabilities on opportunity recognition might vary depending on the entrepreneur's gender and the age of their business. The theoretical contribution and practical implications of the findings to social media companies, entrepreneurs, and policymakers were discussed. The study limitation includes being a cross sectional study, focusing on small businesses and evaluating two countries

The first wave impact of the COVID-19 pandemic on the Nasdaq Helsinki stock exchange: Weak signal detection with managerial implications

The global pandemic caused by the coronavirus disease (COVID-19) came mostly as a surprise and had a major effect on the global economy. This type of major events that can bring societies to nearly a total standstill are difficult to predict but have a significant impact on business activities. Nevertheless, weak signals might be possible to detect beforehand to enable preparation for the impact, both globally and locally. This study analyses the impact of the first wave of the COVID-19 pandemic on the Nasdaq Helsinki stock exchange by utilising large-scale media analytics. This entails gaining data through media monitoring over the entire duration of the pandemic by applying black-box algorithms and advanced analytics on real cases. The data analysis is carried out to understand the impact of a such global event in general, while aiming to learn from the potential weak signals to enable future market intelligence to prepare for similar events. A social media firestorm scale, similar to the Richter scale for earthquakes or Sapphir-Simpson scale for hurricanes, is utilised to support the analysis and assist in explaining the phenomenon. The results indicate that pandemics and their impact on markets can be studied as a subset of a media firestorms that produce a sharkfin type of pattern in analytics. The findings indicate that early signals from such events are possible to detect by means of media monitoring, and that the stock exchange behaviour is affected. The implications include highlighting the importance of weak signal detection from abundant data to have the possibility to instigate preventive actions and prepare for such events to avoid maximum negative business impact. The early reaction to this type of events requires a very streamlined connection between market intelligence and different business activities

Meaning-sensitive noisy text analytics in the low data regime

Digital connectivity is revolutionising people’s quality of life. As broadband and mobile services become faster and more prevalent globally than before, people have started to frequently express their wants and desires on social media platforms. Thus, deriving insights from text data has become a popular approach, both in the industry and academia, to provide social media analytics solutions across a range of disciplines, including consumer behaviour, sales, sports and sociology. Businesses can harness the data shared on social networks to improve their organisations’ strategic business decisions by leveraging advanced Natural Language Processing (NLP) techniques, such as context-aware representations. Specifically, SportsHosts, our industry partner, will be able to launch digital marketing solutions that optimise audience targeting and personalisation using NLP-powered solutions. However, social media data are often noisy and diverse, making the task very challenging. Further, real-world NLP tasks often suffer from insufficient labelled data due to the costly and time-consuming nature of manual annotation. Nevertheless, businesses are keen on maximising the return on investment by boosting the performance of these NLP models in the real world, particularly with social media data. In this thesis, we make several contributions to address these challenges. Firstly, we propose to improve the NLP model’s ability to comprehend noisy text in a low data regime by leveraging prior knowledge from pre-trained language models. Secondly, we analyse the impact of text augmentation and the quality of synthetic sentences in a context-aware NLP setting and propose a meaning-sensitive text augmentation technique using a Masked Language Model. Thirdly, we offer a cost-efficient text data annotation methodology and an end-to-end framework to deploy efficient and effective social media analytics solutions in the real world.Doctor of Philosoph

Measuring The Impact of Social Media Marketing on Individuals

Three problems associated with the use of social media in marketing include: 1. The difficulty in tracking, managing, and analyzing the traffic that comes from different social media networks. Google Analytics is one of the data analytic tools that deals with traffic efficiently. It recognizes traffic sources and categorizes them to give the advertiser insights into oncoming traffic to the company’s website. It provides comprehensive statistics about traffic, which can be useful for advertisers to measure the performance of their marketing campaigns. 2. The inability to measure the success of marketing campaigns to increase sales. A/B Testing is a useful way to tell advertisers about the best methods to enhance their final results. It examines the functionalities of websites and advertising techniques during social marketing campaigns that lead to direct or indirect impacts, which can boost sales. 3. The lack of finding target audiences in social media. Social media’s API, such as Twitter Ads, provides many features that can generate new leads. It gives advertisers the ability to target social media users based on their demography, geography, behavior, and interest. In the business section, the paper covers the impact of social media influencers on their followers and how companies use those influencers within their marketing campaigns. This information can help businesses achieve their social media marketing goals by using these solutions and following measurable plans. Furthermore, the paper mentions some successful case studies that have used these solutions effectively