The interplay between users’ intraorganizational social media use and social capital

The wide acceptance of social media by the public has caused companies try to use intraorganizational social media to increase employee work performance. However, simply implementing a platform is insufficient for success. Companies must encourage employees to use social media for work-related purposes. This study divided the use of intraorganizational social media into social- and work-related use and proposed a model based on the theory of social capital to explore the effects of social-related use on work-related use. The model was tested using a survey of users of intraorganizational microblog systems in China. The results indicate the relationships among two types of intraorganizational use and the dimensions of social capital, and that social-related use fosters work-related use directly and indirectly by enhancing social capital. These results facilitate an understanding of the value of social activities conducted using intraorganizational social media in organizations.     

IT-enabled knowledge ambidexterity and innovation performance in small U.S. firms: The moderator role of social media capability

This study examines the impact of information technology (IT)-enabled knowledge ambidexterity on innovation performance, and the potential moderator role of social media capability on a sample composed of 100 small U.S. firms. The empirical analysis suggests that IT infrastructure enables the firm to explore new knowledge and exploit existing/new knowledge to innovate more and better. We also find that social media capability has a positive moderator role in this equation: IT infrastructure and social media capabilities work together to enable knowledge ambidexterity.     

How compulsive use of social media affects performance: insights from the UK by purpose of use

Positive outcomes of social networking use in both informal and non-educational settings have attracted significant research attention. These benefits include social capital formation, higher job performance and satisfaction, an increased sense of belonging, improved knowledge management skills, and enabling of life-long learning opportunities. Compulsive use of social networking, however, remains a major issue among the younger users, potentially leaving a long-lasting impact on the younger population. Concerns have been raised regarding links between the compulsive use of social media and individual academic, social and physical performance. This study explores the motivations for social networking use, their relations to compulsive use, and implications for academic, physical and social performance, in an effort to inform strategies for the appropriate adoption and utilisation of social networking technologies. The study employs a finite mixture approach to segmenting the sample, and results show that two distinct groups motivated by utilitarian and social objectives respectively drive compulsive use by British students. Unlike previous studies, the UK sample does not display differences in terms of age or gender. This finding highlights the trend of gender agnostic views of social platforms by developers. The paper concludes with a discussion of the implications for practice and future research.     

Do IT freelancers increase their entrepreneurial behavior and performance by using IT self-efficacy and social capital? Evidence from Bangladesh

This study applies entrepreneurship theory and social capital perspective to IT freelancing to facilitate a better understanding of it. Our model hypothesizes that both IT self-efficacy and social capital obtained through social media interactions contribute to IT freelancers’ entrepreneurial behavior and then this entrepreneurial behavior influences their performance. We collected primary data from one of the leading online freelancing marketplaces, oDesk.com (currently Upwork.com). Our empirical findings confirm the core assertion of our research model. The implications of these findings for research and practice are discussed. Directions for future research are also provided.     

The effect of social media use on work‐related learning

The increasing use of social media at work offers organizations new opportunities for employee learning on the job. This study investigated the relationship between social media use and learning activities undertaken by employees. It was expected that social media use relates to higher levels of learning and that this relationship is stronger when employees perceive higher levels of an organization culture that stimulates dialogue and inquiry. A total of 590 respondents of three multinationals based in the Netherlands completed a survey. As expected, results showed that the more often employees used social media in their job, the more often they participated in learning activities. Contrary to expectation, this relationship was not influenced by culture of dialogue and inquiry. Instead, the perception of a culture of dialogue and inquiry directly related to learning on the job. The findings suggest that organizations could consider stimulating the use of social media among employees to support work‐related learning.     

A decision support model for identification and prioritization of key performance indicators in the logistics industry

Performance measurement of logistics companies is based upon various performance indicators. Yet, in the logistics industry, there are several vaguenesses, such as deciding on key indicators and determining interrelationships between performance indicators. In order to resolve these vaguenesses, this paper first presents the stakeholder-informed Balanced Scorecard (BSC) model, by incorporating financial (e.g. cost) and non-financial (e.g. social media) performance indicators, with a comprehensive approach as a response to the major shortcomings of the generic BSC regarding the negligence of different stakeholders. Subsequently, since the indicators are not independent of each other, a robust multi-criteria decision making technique, the Analytic Network Process (ANP) method is implemented to analyze the interrelationships. The integration of these two techniques provides a novel way to evaluate logistics performance indicators from logisticians' perspective. This is a matter that has not been addressed in the logistics industry to date, and as such remains a gap that needs to be investigated. Therefore, the proposed model identifies key performance indicators as well as various stakeholders in the logistics industry, and analyzes the interrelationships among the indicators by using the ANP. Consequently, the results show that educated employee (15.61%) is the most important indicator for the competitiveness of logistics companies.     

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.     

Analyzing market performance via social media: a case study of a banking industry crisis

Analyzing market performance via social media has attracted a great deal of attention in the finance and machine-learning disciplines.However,the vast majority of research does not consider the enormous influence a crisis has on social media that further affects the relationship between social media and the stock market.This article aims to address these challenges by proposing a multistage dynamic analysis framework.In this framework,we use an authorship analysis technique and topic model method to identify stakeholder groups and topics related to a special firm.We analyze the activities of stakeholder groups and topics in different periods of a crisis to evaluate the crisis’s influence on various social media parameters.Then,we construct a stock regression model in each stage of crisis to analyze the relationships of changes among stakeholder groups/topics and stock behavior during a crisis.Finally,we discuss some interesting and significant results,which show that a crisis affects social media discussion topics and that different stakeholder groups/topics have distinct effects on stock market predictions during each stage of a crisis.     

How do social commerce-IT capabilities influence firm performance? Theory and empirical evidence

We theorized that the development of two contemporary social commerce-IT capabilities (social media and e-commerce) enables firms to engage online customers to improve their firm performance. We tested this theory by employing a secondary dataset on a sample of U.S. firms. The empirical analysis supported our theory in both examining the effects of social media and e-commerce as two individual capabilities and as two complementary capabilities. This study provides a unique organizational theory and empirical evidence on how social commerce-IT capabilities influence firm performance through the online engagement of customers.     

An ANN-based approach of interpreting user-generated comments from social media

The IT advancement facilitates growth of social media networks, which allow consumers to exchange information online. As a result, a vast amount of user-generated data is freely available via Internet. These data, in the raw format, are qualitative, unstructured and highly subjective thus they do not generate any direct value for the business. Given this potentially useful database it is beneficial to unlock knowledge it contains. This however is a challenge, which this study aims to address. This paper proposes an ANN-based approach to analyse user-generated comments from social media. The first mechanism of the approach is to map comments against predefined product attributes. The second mechanism is to generate input-output models which are used to statistically address the significant relationship between attributes and comment length. The last mechanism employs Artificial Neural Networks to formulate such a relationship, and determine the constitution of rich comments. The application of proposed approach is demonstrated with a case study, which reveals the effectiveness of the proposed approach for assessing product performance. Recommendations are provided and direction for future studies in social media data mining is marked.     

Understanding compulsive social media use: The premise of complementing self-conceptions mismatch with technology

Some anecdotal accounts and research reports have suggested that obsessive social media involvement could turn into a compulsive behavior among university students. Unfortunately, the research that sheds light on the possible conditional nature of that relationship is scarce at best. Therefore, this study tries to address this issue by developing a contingency-based model and tests it using data gathered from a sample of university students. The model postulates that compulsive social media use arises due to self-awareness factors, and together they in turn predict problematic learning outcomes. It also postulates that these relationships are moderated by the influence of technological factors. The results indicate that self-esteem has a significant negative influence on compulsive social media use and that interaction anxiousness has a significant positive influence on the same. The results also reveal that only compulsive social media use has a significant direct influence on problematic learning outcomes; and that social media complementarity plays a moderating role in the model. We discuss the implications of these findings for research and practice.     

A conceptual framework for developing solutions that organise social media information for emergency response teams

ABSTRACT

Social media have great power to spread information, and this is particularly noticeable when an emergency occurs. The extraction of accurate information from social media can offer an important resource for emergency management, both in terms of decision-making and increasing situational awareness. This paper describes a conceptual framework for the development of applications to treat messages from social media. It is designed to select, classify and prioritise, using parameters, messages containing information that is relevant to the emergency context. It allows a team to act on this information and to generate rescue actions that contribute to the emergency solution. It has a collaborative bias, providing perceptual, coordination and communication mechanisms. We also present an instantiation and the simulation of its use in the treatment of tweets (Twitter messages) about two emergencies: an earthquake in Mexico City (19/09/2017) and a California fire (December, 2017). The volume of messages is enormous, but most of them do not present significant value to the emergency response. We categorised those that contained relevant information. With only 2% of the tweets, it was possible to identify and prioritise messages with potential to aid in response and rescue operations.     

How to Archive an Event: Reflections on the Social Repository of Ireland

Reflecting on a feasibility study into archiving social media, this article traces how “events” are defined in various domains and contexts, and employs case studies to analyze key relationships between hashtags and events to provide a critical analysis of how archival events can be constructed out of social events. It provides an overview of the archival and curatorial considerations involved in defining and preserving a social media event, and outlines the technologies developed for the process of collecting, annotating, and preserving social media events. Overall, the article endeavors to reveal how pragmatic considerations, computational approaches and curatorial perspectives shape digital archives and historical narratives.     

Knowledge sharing in information system development teams: examining the impact of shared mental model from a social capital theory perspective

Shared mental model (SMM), a concept from psychology, is defined as a common thinking style developed when individuals perform similar tasks in a cohesive manner. In this article, we investigate the relationship between the three dimensions of social capital and SMM. We also examine whether SMM mediates the impact of social capital on knowledge sharing (KS) behaviour in information system development (ISD) teams. Social capital is defined as the resource of social relationships owned by individuals. It is useful for explaining human behaviour in social networks. The data collected represent 492 ISD professionals in 118 teams from 18 middle-sized enterprises. The results of this study indicate that social capital theory is useful for explaining the antecedents of SMM, and SMM is positively related to KS and team performance. This research also emphasises the importance of developing SMM in a team.     

How social-media-enabled co-creation between customers and the firm drives business value? The perspective of organizational learning and social Capital

Contemporary business organizations are increasingly turning their attention to value co-creation using social media between individual customers and business organizations in the process of new product development (NPD). However, little is known about the mechanisms underlying social-media-based customer-firm co-creation and their implications for business value in NPD. To address this knowledge gap, this study develops a model from the perspective of organizational learning and social capital to examine how the social-media-based customer-firm co-creation mechanism conceptualized as the structural, cognitive, and relational dimension of social capital influences the first-order knowledge outcome (knowledge transfer effectiveness) and second-order dynamic capability outcome (absorptive capacity), and how these co-creation outcomes ultimately influence organizational performance. The model is tested using survey data from 149 Chinese mobile application developers. The results indicate that social-media-based structural, cognitive, and relational linkage, in particular the structural linkage, is an important co-creation mechanism to improve organizational performance. Knowledge transfer effectiveness and absorptive capacity have significant mediating effects in this co-creation mechanism-outcomes-performance framework. Further, the moderating effects of social media use level on the relationships between co-creation mechanism and outcomes are largely supported. The study contributes to theory and practice by shedding light on the social-media-based customer-firm co-creation in NPD at a process level.     

A taxonomy and survey of big data in social media

Examining the particular value of each platform for big data would be difficult because of the variety of social media forms and sizes. Using social media to objectively and subjectively analyze large groups of individuals makes it the most effective tool for this task. There are numerous sources of big data within the organization. Social media can be identified by the interaction and communication it facilitates. Utilizing social media has become a daily occurrence in modern society. In addition, this frequent use generates data demonstrating the importance of researching the relationship between big data and social media. It is because so many internet users are also active on social media. We conducted a systematic literature review (SLR) to identify 42 articles published between 2018 and 2022 that examined the significance of big data in social media and upcoming issues in this field. We also discuss the potential benefits of utilizing big data in social media. Our analysis discovered open problems and future challenges, such as high-quality data, information accessibility, speed, natural language processing (NLP), and enhancing prediction approaches. As proven by our investigations of evaluation metrics for big data in social media, the distribution reveals that 24% is related to data-trace, 12% is related to execution time, 21% to accuracy, 6% to cost, 10% to recall, 11% to precision, 11% to F1-score, and 5% run time complexity.     

基于流形学习的社会化媒体网络数据分类

社会化媒体提供了海量的、大尺度的异质网络数据,如何对网络数据进行分类是一个亟待解决的新问题。基于潜在社会维模型,提出利用流形学习中的拉普拉斯特征映射算法进行社会维抽取。实验表明,在精确匹配率、微平均、宏平均等性能指标上,均优于基于模块度最大化的原有社会维模型。该算法能更好地获取用户的隐性联系,从而更好地分析网络用户行为。     

An affordance perspective of enterprise social media and organizational socialization

In response to the challenge of socializing new IT employees, some IT departments are exploring the incorporation of enterprise social media (hereinafter ESM) as an informal organizational socialization tool. Because this is a relatively new phenomenon, little is known about how ESM facilitate employee socialization. In order to contribute to our understanding of how ESM affects employee socialization, this paper invokes a case study to explore how one organization’s implementation of an ESM for its IT new hire program influenced the socialization process and outcomes. To delve deeply into how the ESM influences socialization, we draw upon technology affordance theory to uncover the various first and second-order affordances actualized by different actor groups and the various outcomes resulting from the affordances. We then identify five generative mechanisms – bureaucracy circumvention, executive perspective, personal development, name recognition, and morale booster – that explain how the actualization of different strands of affordances by various groups of users produces eight different outcomes. Our results provide insights into the different affordances made possible by ESM in the context of a new hire socialization program and how these affordances have repercussions beyond those experienced by the individuals using the ESM. The results have important implications for new hire socialization and technology affordance research.     

Enterprise social networking usage as a moderator of the relationship between work stressors and employee creativity: A multilevel study

Enterprise social networking (ESN) techniques have been widely adopted by organizations to provide a platform for public communication among employees. Based on the job demand–control–support model and communication visibility theory, this study investigates how the relationships between the perceived work stressors of employees (i.e., challenge and hindrance stressors) and their creativity are moderated by team task- and relationship-oriented ESN usage. We used the hierarchical linear model to test this multilevel model. Results of a survey of 260 employees in 72 groups indicate that two ESN usage types differentially moderate the relationship between work stressors and employee creativity. Specifically, task-oriented ESN usage positively moderates the effects of the two types of stressors on employee creativity, whereas relationship-oriented ESN usage negatively moderates the relationship between hindrance stressors and employee creativity. Theoretical and practical implications are also discussed.