Search results

This paper aims to explore how digital technologies have forced small- to medium-sized enterprises (SMEs) to reconsider and experiment with their business models (BMs) and how this contributes to their innovativeness and performance.

An empirical study has been conducted on 338 European SMEs actively using social media and big data to innovate their BMs. Four in-depth case studies of companies involved in BM innovation have also been carried out.

Findings show that the use of social media and big data in BMI is mainly driven by strategic and innovation-related internal motives. External technology turbulence plays a role too. BMI driven by social media and big data has a positive impact on business performance. Analysis of the case studies shows that BM is driven by big data rather than by social media.

Research into big data- and social media-driven BMs needs more insight into how components are affected and how SMEs are experimenting with adjusting their BMs, specifically in terms of human and organizational factors.

Findings of this study can be used by managers and top-level executives to better understand how firms experiment with BMI, what affects business model components and how implementation might affect BMI performance.

This paper is one of the first research contributions to analyse the impact of digitalization, specifically the impact of social media and big data on a large number of European SMEs.

In this study, the authors sought to investigate how the implicit social ties of both project owners and potential backers are associated with crowdfunding project success.

Drawing on social ties theory and factors that affect crowdfunding success, in this research, the authors developed a model to study how project owners' and potential backers' implicit social ties are associated with crowdfunding projects' degrees of success. The proposed model was empirically tested with crowdfunding data collected from Kickstarter and social media data collected from Twitter. The authors performed the test using an ordinary least squares (OLS) regression model with fixed effects.

The authors found that project owners' implicit social ties (specifically, their social media activities, degree centrality and betweenness centrality) are significantly and positively associated with crowdfunding projects' degrees of success. Meanwhile, potential project backers' implicit social ties (their social media activities and degree centrality) are negatively associated with crowdfunding projects' degrees of success. The authors also found that project size moderates the effects of project owners' social media activities on projects' degrees of success.

This work contributes to the literature on crowdfunding by investigating how the implicit social ties of both potential backers and project owners on social media are associated with crowdfunding project success. This study extends the previous research on social ties' roles in explaining crowdfunding project success by including implicit social ties, while the literature explored only explicit social ties.

The practice of frontline employees articulating their brand voice and posting work-related content on social media has emerged; however, employee brand equity (EBE) research has yet to be linked to employees’ social media activity. This paper aims to take a methods-based approach to better understand employees’ roles as influencers. As such, its objective is to operationalize and apply the three EBE dimensions – brand consistent behavior, brand endorsement and brand allegiance – using Instagram data.

This qualitative research uses a case study of employee influencers at SoulCycle, a leading North American fitness company and examines 100 Instagram images and 100 captions from these influential employees to assess the three EBE dimensions.

Brand consistent behavior (what employees do) was the most important EBE dimension indicating that employees’ social media activities align with their employer’s values. Brand allegiance (what employees intend to do in the future) whereby employees self-identify with their employer on social media, followed. Brand endorsement (what employees say) was the least influential of the three EBE dimensions, which may indicate a higher level of perceived authenticity from a consumer perspective.

This research makes three contributions. First, it presents a novel measure of EBE using public Instagram data. Second, it represents a unique expansion and an evolution of King et al.’s (2012) model. Third, it considers employees’ work-related content on social media to understand employees’ role as influencers and their co-creation of EBE, which is currently an under-represented perspective in the internal branding literature.

The purpose of this study is to investigate the various enablers of and constraints on employees' information sharing on an enterprise social media platform. It draws on two theoretical perspectives, communication privacy management theory and the technology affordance framework, as well as on empirical data in an attempt to paint a comprehensive picture of the factors shaping employees' decisions to share or not share information on enterprise social media.

This qualitative field study is based on semi-structured interviews and enterprise social media review data from a large Nordic media organization.

On an enterprise social media platform, privacy management principles shape employees' information-sharing decisions in relation to personal privacy boundaries, professional boundaries and assumed risks, online safety concerns and perceived audience. Additionally, the technological affordances of visibility, awareness, persistence and searchability shape employees' information sharing in varying and sometimes even contradictory ways. Finally, organizational factors, such as norms, tasks and media repertoires, are associated with employees' information-sharing decisions. Together, these three dimensions, personal, technological and organizational, form a model of the enablers of and constraints on employees' decisions to share information on enterprise social media.

This study extends the understanding of different factors shaping employees' decisions to share or not share information on enterprise social media. It extends the two applied theories by uniquely combining interpersonal privacy management principles with a technological affordance framework that focuses on the relationship between the user and the technology. This research also furthers the authors' knowledge of what privacy management principles mean in the organizational context. This study shows connections between the two theories and extends the understanding of technology affordances as not only action possibilities but also constraining factors. Additionally, by revealing what kinds of factors encourage and inhibit information sharing on enterprise social media, the results of this study support organizations in their efforts to manage information sharing on enterprise social media systems.

The study aims to investigate the time for social media posts and reviews in order to determine the best timing to ensure maximum outreach and interactions from users. The study intends to analytically investigate a company's Facebook and Instagram pages to get meaningful insights for effective post management.

“Great Deal Tires” company’s Facebook and Instagram business pages were taken as the case study and patterns and analytical insights for different posts using Facebook and Instagram analytics were identified. The study categorically selected posts from Facebook pages and Instagram pages that were posted at different times and assessed on their impact.

According to the study, Tuesday and Wednesday have higher engagement on the Great Deal Tires Facebook page, while Friday has higher engagement on Instagram. The study also provided valuable insights into post content and timing in order to increase the marketing impact of the posts.

The study provides an analytical framework for analyzing post and review timing on various company business pages, allowing marketers to initiate more user visits and interactions.

This study aims to examine adolescents’ social media environment connected to unhealthy food marketing. As social media have become a ubiquitous part of young people’s everyday lives, marketers have also shifted their focus to these channels. Literature on this phenomenon is still scarce and often takes a quite narrow view of the role of marketing in social media. Furthermore, the experiences of the adolescents are seldom considered.

Using a sociocultural approach and netnographic methodology, this study presents findings from a research project conducted in Finland. The data consist of both social media material and focus group interviews with adolescents.

The findings elaborate on unhealthy food marketing to adolescents in social media from two perspectives: sociocultural representations of unhealthy foods in social media marketing and social media influencers connecting with adolescents.

The study broadens and deepens the current understanding of unhealthy food marketing to adolescents taking place in social media. The study introduces a novel perspective to the topic by looking at it as a sociocultural phenomenon.

The purpose of this paper is to examine what types of task information media scholars need while gathering research data to create new knowledge.

The research design is qualitative and user-oriented. A total of 25 media scholars were interviewed about their research processes and interactions with their research data. The interviews were semi-structured, complemented by critical incident interviews. The analysis focused on the activity of gathering research data. A typology of information (task, domain and task-solving information) guided the analysis of information types related to data gathering, with further analysis focusing only on task information types.

Media scholars needed the following task information types while gathering research data to create new knowledge: (1) information about research data (aboutness of data, characteristics of data, metadata and secondary information about data), (2) information about sources of research data (characteristics of sources, local media landscapes) and (3) information about cases and their contexts (case information, contextual information). All the task information types should be considered when building data services and tools to support media scholars' work.

The paper increases understanding of the concept of task information in the context of gathering research data to create new knowledge and thereby informs the providers of research data services about the task information types that researchers need.

Stock price prediction is a hot topic and traditional prediction methods are usually based on statistical and econometric models. However, these models are difficult to deal with nonstationary time series data. With the rapid development of the internet and the increasing popularity of social media, online news and comments often reflect investors’ emotions and attitudes toward stocks, which contains a lot of important information for predicting stock price. This paper aims to develop a stock price prediction method by taking full advantage of social media data.

This study proposes a new prediction method based on deep learning technology, which integrates traditional stock financial index variables and social media text features as inputs of the prediction model. This study uses Doc2Vec to build long text feature vectors from social media and then reduce the dimensions of the text feature vectors by stacked auto-encoder to balance the dimensions between text feature variables and stock financial index variables. Meanwhile, based on wavelet transform, the time series data of stock price is decomposed to eliminate the random noise caused by stock market fluctuation. Finally, this study uses long short-term memory model to predict the stock price.

The experiment results show that the method performs better than all three benchmark models in all kinds of evaluation indicators and can effectively predict stock price.

In this paper, this study proposes a new stock price prediction model that incorporates traditional financial features and social media text features which are derived from social media based on deep learning technology.

Social media are increasingly being used in teaching and learning in higher education. This paper aims to explore multiple case studies to better understand how instructors decide to incorporate social media into learning.

This qualitative case study used the technology acceptance model (TAM) to explore five instructors' use of social media for teaching and learning, particularly the pedagogical reasons and goals driving their use of social media. Participant interviews, course documentation and social media observation data were collected to answer the research questions.

Findings suggest that an instructor's social media knowledge and awareness of instructional goals are important for the use of social media in learning. Three pedagogical objectives of the use of social media were found across five participants: collaborative learning, dialog and discussion, and authentic learning.

Previous studies have explored potential pedagogical uses of social media tools, however studies that attempt to understand how and why instructors decide to use particular social media tools are underreported.

During the COVID-19 pandemic, public sector organisations produced thousands of social media messages weekly answering citizens questions and informing the public on safety related matters. The purpose of this study was to investigate how the pandemic shaped social media listening in Finland's public sector organisations and how these organisations aligned their listening and strategic communication to address emerging questions, news (real and fake) and rumours during the pandemic.

Building on a theoretical background from strategic communication, organisational listening, digital marketing and public sector communication, qualitative interview data included communicators (N = 14) from all central Finnish public sector organisations in charge of COVID-19 communication. Findings were themed and analysed qualitatively to understand the level of alignment of strategic communication on social media.

The findings revealed that the pandemic had strained public sector organisations' communication capabilities, forcing them to align their processes and resources reactively to enable useful content and limit false/misleading content. The results confirmed that organisational listening remained somewhat unaligned. A dual role of public sector communication as speakers but increasingly as listeners was highlighted.

The study’s findings point to organisational listening on social media being a central requisite for public sector organisations overcoming a crisis.