Abstract

Although knowledge drives organizational growth, limited teamwork skills have hindered effective knowledge sharing and utilization across departments and individuals in higher education institutions (HEIs). Thus, this paper develops a pertinent knowledge-intensive teamworking model for HEIs. Educational background, leadership, and social media adoption were independent variables, while knowledge acquisition, development, application, revision, and knowledge-intensive teamwork were dependent variables. Using an online questionnaire, a quantitative investigation of 918 people across different HEIs tested hypotheses based on PLS-SEM findings. Results showed that educational background, leadership, and social media adoption are integral enablers in smoothing knowledge management operations, while the synergistic combination of process phases drives teamwork. Consequently, outcomes establish foundations for fostering a continuous learning culture, accountability, and cooperation in HEIs.

Introduction

Most management and technology research has focused on the technical efficiency of social media [1]. Information technology revolutionized education over three decades ago, and more recent research has examined various impacts on the workplace, such as training and communication [2,3]. Social media adoption in higher education institutions (HEIs) depends on strategic aims, resources, and cultural factors [4]. Furthermore, the adoption of technology by HEIs is contingent on organizational strategy and differs among HEIs in the same education system [5]. The use of social media in education has drawn interdisciplinary interest [6,7] as HEIs shift communication from one-way to interactive to regularly engage stakeholders [8]. Continual society-wide communication needs frequent updates across media, especially social platforms. Different methods can shape how HEIs utilize social media as key channels, enabling stakeholder conversations. Communication frequently starts on blogging platforms and spreads across media as users access multiple integrated channels, facilitating tailored, multi-way exchanges without time barriers.

The COVID-19 pandemic necessitated remote work and learning, disrupting traditional HEI teamwork models and requiring new virtual collaboration approaches [9]. This research extends knowledge management theory by examining how social media enables leadership, knowledge sharing, and co-development in virtual teams. Specifically, it provides empirical support for the knowledge management cycle model of Kang and Hau [10] by demonstrating how social media can facilitate key stages of knowledge acquisition, sharing, and revision in virtual settings. The findings reveal how social media-enabled leadership, through modeling knowledge-sharing practices and incentivizing participation, is critical for overcoming motivational barriers to collaborative knowledge activities online [11]. This paper advances the theoretical understanding of the interconnected relationships between social media adoption, leadership influence, and iterative knowledge improvement critical for virtual team effectiveness in higher education institutions. Moreover, a key contribution is providing empirical evidence on the affordances of different social media platforms for enabling specific collaborative activities in virtual HEI teams. The findings provide actionable guidance to HEI leaders on effective social media policies and professional development to optimize virtual teamwork. This research also contributes a new conceptual model of social media-enabled knowledge-intensive teamwork tailored to the HEI context.

Conclusion

The COVID-19 pandemic has significantly impacted higher education institutions, catalyzing a shift towards remote and online learning. In this context, social media has become an integral platform enabling knowledge-intensive teamwork, allowing seamless communication and collaboration regardless of location. Specifically, social media can promote the development of such teams in several key ways. Online discussions and forums can be leveraged to share information and resources, discuss ideas, brainstorm solutions, and provide feedback among team members. Video conferencing tools facilitate virtual meetings and presentations, helping members stay connected and engaged. Additionally, knowledge management systems allow the centralized sharing and accessing of documents while tracking progress – optimizing coordination. Consequently, social media facilitates effective knowledge-intensive teamwork in higher education after COVID-19 by providing ubiquitous channels for cooperation.

In conclusion, high-performing knowledge-based teams in higher education are predicated on an innovation-centric framework requiring integrated organizational and procedural alignment. Positive relationships among knowledge skill-building, leadership support, and cultural enablement underscore the need to embed holistically developmental, technological, and structural conditions that foster learning and innovative capacity at individual and team levels. Internally, sustained idea flows via cooperation enable continuous adaptation and improvement as contexts shift.

Abstract

This study examines the relationship between Bitcoin market dynamics and user activity on the r/cryptocurrency subreddit. The purpose of this research is to understand how social media activity correlates with Bitcoin price and trading volume, and to explore the sentiment and topical focus of Reddit discussions. We collected data on Bitcoin’s closing price and trading volume from January 2021 to December 2022, alongside the most popular posts and comments from the subreddit during the same period. Our analysis revealed significant correlations between Bitcoin market metrics and Reddit activity, with user discussions often reacting to market changes. Additionally, user activity on Reddit may indirectly influence the market through broader social and economic factors. Sentiment analysis showed that positive comments were more prevalent during price surges, while negative comments increased during downturns. Topic modeling identified four main discussion themes, which varied over time, particularly during market dips. These findings suggest that social media activity on Reddit can provide valuable insights into market trends and investor sentiment. Overall, our study highlights the influential role of online communities in shaping cryptocurrency market dynamics, offering potential tools for market prediction and regulation.

Introduction

The rapid growth and adoption of cryptocurrencies have attracted significant attention from investors, researchers, and policymakers. Among these digital assets, Bitcoin (BTC) [1] stands out due to its market dominance and influence on the broader cryptocurrency ecosystem. Understanding the factors that drive Bitcoin’s price and trading volume is crucial for stakeholders across various sectors. One such factor is the sentiment and activity within online communities, where discussions and opinions about cryptocurrencies are actively shared.

Social media platforms and online forums play a vital role in shaping the market dynamics of cryptocurrencies [2]. These platforms provide real-time information, opinions, and sentiment that can influence investor behavior and market trends. Several studies have explored the relationship between social media activity and cryptocurrency markets, highlighting the significance of sentiment analysis and user engagement in predicting price movements and trading volumes.

While previous studies have examined the relationship between social media sentiment and cryptocurrency markets, our research distinguishes itself by focusing on the specific community of r/cryptocurrency on Reddit. We leverage a combination of sentiment analysis and topic modeling on a comprehensive dataset of posts and comments, providing a detailed view of how online discussions evolve and correlate with Bitcoin market movements. By examining both sentiment and thematic content over time, our study offers nuanced insights into the interplay between social media activity and cryptocurrency market dynamics.

Results

4. Results
4.1. Bitcoin Market Data
Figure 1 illustrates the closing price of Bitcoin (BTC) over a two-year period, from 1 January 2021 to 31 December 2022. The data reveal significant volatility, characteristic of the cryptocurrency market. Notably, BTC reached an all-time high in November 2021, approaching USD 68,000, before undergoing a substantial correction throughout 2022. This decline reflects broader market trends, including macroeconomic factors and changes in investor sentiment. The closing price provides a critical foundation for understanding market dynamics and investor behavior during this period.

Abstract

     While there is a growing body of research on influencer marketing, it focuses almost exclusively on the consumer marketing context, and offers limited insights for business-to-business (B2B) organizations. To address this gap, the purpose of this study is to explore the use of influencer marketing in B2B markets. We draw empirical insights from 22 interviews with a) 12 senior marketing managers representing influencer marketing users in various B2B industries, ranging from industrial product manufacturers to professional service providers, and b) 10 experts from marketing agencies who advise other B2B firms on how to apply influencer marketing. The study findings identify the key elements of influencer marketing in B2B markets, and reveal four different strategies that B2B organizations use to operationalize the influencer marketing concept in practice. The study contributes to theory by providing the first empirical conceptualization of the B2B influencer marketing phenomenon. In doing so, the study widens the scope of influencer marketing to accommodate broader collaborations with influencers, which has significant implications across B2B and B2C contexts. For managers, this study offers alternative strategies to implement influencer marketing, which are associated with unique characteristics and serve different business goals.

Introduction

     Influencer marketing is gaining popularity as a key component of firms’ digital marketing strategy (Appel, Grewal, Hadi & Stephen, 2020; Li, Larimo, & Leonidou, 2020), manifested in the market growth of global influencer marketing from $1.7 billion (USD) in 2016 to $13.8 billion in 2021 (Statista, 2021). While influencer marketing is often associated with consumer brands, multiple industry reports indicate that it is gaining momentum in the business-to-business (B2B) industries, too (e.g., Morgan, 2020; Hyder, 2019; Shea, 2018; TopRank Marketing, 2020). The increasing interest in B2B influencer marketing is being driven by contemporary B2B buying behavior, where buyers have become more self-directed in their purchasing processes, and complete a significant number of purchase-related tasks before contacting potential vendors (Järvinen & Taiminen, 2016; Steward, Narus, Roehm, & Ritz, 2019; Terho, Mero, Siutla, & Jaakkola, 2022). In turn, the independent online research relies heavily on digital content, and B2B buyers trust in content produced by third-party experts and peers (Shea, 2018). Given the significant role of trust in forming B2B relationships, influencer marketing provides a credible means to influence the self-directed B2B buying processes.

      Industry case studies show that influencer marketing can provide substantial benefits to B2B firms. For example, SAP, a software company, created the TechUnknown podcast, where an influencer host discussed various topics around technology with industry experts and SAP’s internal executives, resulting in a 66% increase in their podcast downloads. Furthermore, Cherwell Software shifted its business focus from IT service management to supporting companies with digital transformation, and used influencer marketing to build credibility for its new focus. The firm partnered with technology experts and achieved a 342% better click-through rate on influencer content, compared with the performance of its past campaigns (TopRank Marketing, 2020). However, while 74% of B2B marketers see the potential of influencer marketing to drive business benefits, only 19% are running systematic influencer marketing programs (Hyder, 2019). In fact, 60% of B2B firms report they do not have the skills or knowledge to implement influencer marketing programs, and one of the key reasons is that the current knowledge and best practices in influencer marketing come from consumer marketing contexts, which are not directly transferable to B2B contexts (TopRank Marketing, 2020).

Conclusions

6.1. Contributions and theoretical implications
Influencer marketing is a rapidly emerging, yet thus far relatively underexplored management practice in B2B markets. While there is a growing body of research on influencer marketing in general (Hudders et al., 2021; Sundermann & Raabe, 2019; Vrontis et al., 2021), it is almost exclusively focused on consumer markets, product-centric promotions in social media, and consumer attitudes and behaviors, providing only limited insights into the organizational processes and unique characteristics of influencer marketing in B2B markets. Against this background, this study provides two major contributions.

     First, it provides a novel and empirically grounded conceptualization of the influencer marketing concept in the B2B market context, outlining its key elements and boundaries, and positioning it relative to established B2B marketing concepts. The conceptualization broadens the scope of influencer marketing, from product endorsements distributed on social media via external individuals in exchange for tangible rewards to multi-channel content creation and delivery by internal and external influencers on the basis of their mutual interests. This provides new knowledge by clearly differentiating the B2B influencer marketing concept from consumer influencer marketing, and highlighting the unique characteristics of influencer marketing in the B2B context. These findings provide important and thus far missing insights into the influencer marketing literature, which has been focused on the B2C context (e.g., Hudders et al., 2021; Leung, Gu, & Palmatier, 2022; Vrontis et al., 2021) and help explain why influencer marketing insights from the consumer marketing context are not directly transferable to the B2B context.

Abstract

     Taking the digital marketing activities carried out by female-led microenterprises as its jumping-off point, this paper aims to accomplish two main goals: firstly, to analyze the effects of social media marketing (SMM) actions on social performance (SP) and, secondly, to determine whether the qualities of female microentrepreneurs have an impact on the SMM-SP relationship. Entrepreneurship affords many female a solid work-life balance. With scarce financial resources, they often turn to creative social media strategies to secure financial sustainability beyond the short term, as well as to enhance their medium- and long-term social performance. Our regression and moderation analysis of a sample of 127 microenterprises, the data for which was collected by online surveys administered to female microentrepreneurs, reveals SMM actions’ direct effect on social performance. Our findings also show that female microentrepreneurs’ human capital directly influences SP and moderates the effects of marketing strategies on SP. The paper ends by presenting relevant conclusions for both academics and marketing practitioners.

Introduction

     The 18.5 % drop in GDP during the second quarter of 2020 foregrounded entrepreneurship as an appealing alternative for people to improve their personal economy. Data provided by the Global Entrepreneurship Monitor (GEM) for 2020 underscore three key traits of this activity in Spain:

(1) The entrepreneurship rate in Spain was lower than in the rest of Europe. Whereas 10 % of citizens in the world’s most developed countries and 7.7 % of those in the EU-28 were involved in an entrepreneurial initiative, this figure stood at 6.4 % for Spain.

(2) Entrepreneurship has a high self-employment component. In Spain, the self-employment rate among entrepreneurs was 19 %, slightly higher than the EU-28 average of 17 %.

(3) Female entrepreneurship in Spain is growing steadily. While for the 2009–2010 period, the female TEA (Total early-stage Entrepreneurial Activity) was 3 %, during the 2019–2020 period it reached 6 %. Currently in Spain, eight women start businesses for every 10 men, thus exceeding the European average, where only six women are counted for every ten male entrepreneurs.

     Although these figures seem to suggest a narrowing gender gap, an entirely different picture comes into focus when we apply a socioeconomic perspective. Most notably, there is a severe gap in the proportion of consolidated businesses led by men (60 %) versus those led by women (40 %). That being said, men and women seem to be on equal footing when it comes to people hoping to create a business in the next three years (50.9 % of which are men and 49.1 %, women). The numbers even turn in women’s favor with respect to business abandonment, where men account for 52.6 % of such cases.

Conclusions

     The results obtained through our data analysis show that, among digital marketing strategies, those carried out on social media influence the performance of microenterprises, especially their social performance. In the current context, 2.0 technologies guarantee interactivity and quality between companies and consumers. Despite the multiple options available to companies to establish and consolidate relationships, social media appear to be the most efficient from a marketing standpoint, as people have integrated technology into their daily lives.

     The social use of such media has expanded and the number of people seeking emotional and hedonic benefits on them is increasing. People today turn to social media to stay in touch with friends and family, to partake in leisure activities or to give free rein to their creativity. For some, social media have even become a way to discover and engage in new experiences, a boundaryless place where they are free to be themselves. Social media offer users speed, convenience and, above all, security and anonymity, all qualities that encourage participation. Moreover, they greatly extend the reach of messages and content, thereby facilitating immediate and global dissemination. In short, it is easy to understand the reason for their success and why companies, aware of this, have sought to carry out SMM actions as a way to establish relationships with their customers.

Abstract

     While the strategic use of social media (SM) for enhancing firm performance has attracted much attention, little is known about it through the lens of business-to-business (B2B) small and medium-sized enterprises (SMEs). Building on the market-driven view and the dynamic capabilities view of competitive strategy, we examine SM use in a framework of market-sensing and customer-linking capabilities that influence firm performance. Our research model posits that market orientation stimulates SM use to enhance market-sensing capability thereby facilitating two customer-linking capabilities, namely customer relationship management and brand management, which collectively contribute to greater marketing performance and financial performance. Our research model is empirically tested using a survey of 143 UK B2B SMEs. The findings broadly support our theorization in which the strategic use of SM, an essential part of market-sensing capability, enhances customer-linking capabilities. Interestingly, although SM use influences brand management capability, its suggested influences on both customer relationship management capability and marketing performance occur only through the mediation of brand management capability. Both customer-linking capabilities positively influence marketing performance and in turn financial performance. Our findings provide novel conceptual and empirical advancement of how market-centric B2B SMEs strategically use SM to enhance their market-sensing and in turn customer-linking capabilities, and hence firm performance.

Introduction

     With the increased digitalization of marketing activities, marketing professionals are increasingly adopting social media (SM) as a strategic tool to effectively engage with customers and broader stakeholders (e.g. Chaker, Nowlin, Pivonka, Itani, and Agnihotri, 2022; Pardo, Pagani, and Savinien, 2022); develop new products (Du, Yalcinkaya, and Bstieler, 2016); increase brand awareness (Deng, Wang, Rod, and Ji, 2021); co-create value (Itani, Krush, Agnihotri, and Trainor, 2020); and improve firm performance (Cheng, Liu, Qi, and Wan, 2021). More and more researchers suggest that firms must have an SM presence since the strategic use of SM is increasingly seen to enhance firm competitiveness (e.g. Cartwright, Liu, and Raddats, 2021; Tiwary, Kumar, Sarraf, Kumar, and Rana, 2021).

     The likely gains from SM technologies are particularly evident in business markets where customers are more informed and less dependent on interactions with salespeople (Ancillai, Terho, Cardinali, and Pascucci, 2019). For example, leading large firms such as Adobe, IBM, and Maersk Line are increasingly using digital channels and SM to facilitate effective dialogue with customers (Ancillai et al., 2019). The growing evidence suggests that SM can be strategically used to facilitate an effective dialogue with customers (Kovac, 2016), create brand awareness, or differentiate the focal brand from the competition (e.g. Deng et al., 2021; Swani, Brown, and Mudambi, 2020), provide customer care, and/or improve firm performance, among others (e.g. Salo, 2017; Tiwary et al., 2021).

Conclusion

     Overall, our study examined the much-advocated and under-investigated strategic use of SM for enhanced B2B marketing strategy. We opted to focus on the B2B SME context where it argued that due to the resource-constrained operating environment experienced by SMEs, the use of SM platforms will be particularly beneficial due to easiness of implementation, low-cost accessibility, and cost-efficiency. We promised our inquiry on the dynamic capabilities view and Day’s (1994) seminal framework of capabilities in market-driven firms and theorized that the strategic use of SM provides a meaningful link between market orientation and CLC. Overall, our research model captures how the strategic use of SM strengthens MSC to effectively seize market opportunities through (re)developing two key CLC that drive enhanced marketing performance and in turn financial performance. Overall, the strategic use of SM remains a fertile ground for further inquiry in B2B marketing strategy context.

Abstract

     Mental health research through data-driven methods has been hindered by a lack of standard typology and scarcity of adequate data. In this study, we leverage the clinical articulation of depression to build a typology for social media texts for detecting the severity of depression. It emulates the standard clinical assessment procedure Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and Patient Health Questionnaire (PHQ-9) to encompass subtle indications of depressive disorders from tweets. Along with the typology, we present a new dataset of 40191 tweets labeled by expert annotators. Each tweet is labeled as ‘non-depressed’ or ‘depressed’. Moreover, three severity levels are considered for ‘depressed’ tweets: (1) mild, (2) moderate, and (3) severe. An associated confidence score is provided with each label to validate the quality of annotation. We examine the quality of the dataset via representing summary statistics while setting strong baseline results using attention-based models like BERT and DistilBERT. Finally, we extensively address the limitations of the study to provide directions for further research.

Introduction

     Analyzing the presence of mood and psychological disorders through behavioral and linguistic cues from social media data remains a critical area of interdisciplinary research. In addition to these disorders, the last decade has seen exponentially increasing attempts to assess related symptomatology such as depressive disorders, self-harm, and severity of mental illness using non-clinical data (Bucci et al., 2019). Social media platforms and other online discussion forums have been particularly appealing to the research community for various research purposes (e.g., population-level mental health monitoring (Conway & O’Connor, 2016), personal traits detection (Marouf et al., 2020), cyberbullying spotting (Bozyiğit et al., 2021), etc.) because of the massive scale of data. This massive data flow has resulted from increasing rates of internet access and people spontaneously sharing their suffering, pain, and struggle anonymously on these platforms (Ofek et al., 2015). Recognizing the early symptoms of depressive disorder through a person’s language use can prevent many disastrous outcomes like self-harm, suicide, etc., and even help deploy effective treatment in proper time. Moreover, the outbreak of the COVID-19 pandemic is likely to have devastating impacts on the mental health of millions of individuals as lockdown in the affected areas has reported high rises in the incident rates of mood disorder, including acute stress disorder, post-traumatic stress disorder, generalized anxiety disorder, and overall sub-clinical mental health deterioration (Singh et al., 2020). The scope of mental health deterioration during the COVID-19 pandemic and the comprehensive nature of diagnosing depressive disorders have provided an unprecedented need to infer the mental states of individuals from all-inclusive resources. Recent studies have revealed that valuable insights into the impact of the pandemic on population-level mental health can be inferred from posts or comments on social media (Low et al., 2020).

Conclusions and future work

     This work introduced a new typology for diagnosing depression severities from social media texts, as well as a unique dataset of labeled tweets with a confidence score for each label. The dataset was constructed based on strong ground truths and clinical validation, and it is expected to help alleviate the scarcity of mental health data to some extent. The description of the process and challenges in creating such a dataset may motivate researchers to collect similar corpora of this scale from other social media and discussion forums. The experimental results indicated that existing state-of-the-art models often fail to understand the contextual undertone of the data samples. Developing a model that is capable of comprehending the subdued relationship and differences among depression severities can result in an even better understanding of human cognition. Moreover, analysis of the classification performance indicates that there is no distinct division of keywords among different depression severities. The same keyword might be used differently to express different emotions, rather it is more important to understand the context of the tweet to diagnose the severity of depression. Broader implications of this research may include personalizing and directing preventative and awareness messages by health professionals to the users in need.

Abstract

     Mental health research through data-driven methods has been hindered by a lack of standard typology and scarcity of adequate data. In this study, we leverage the clinical articulation of depression to build a typology for social media texts for detecting the severity of depression. It emulates the standard clinical assessment procedure Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and Patient Health Questionnaire (PHQ-9) to encompass subtle indications of depressive disorders from tweets. Along with the typology, we present a new dataset of 40191 tweets labeled by expert annotators. Each tweet is labeled as ‘non-depressed’ or ‘depressed’. Moreover, three severity levels are considered for ‘depressed’ tweets: (1) mild, (2) moderate, and (3) severe. An associated confidence score is provided with each label to validate the quality of annotation. We examine the quality of the dataset via representing summary statistics while setting strong baseline results using attention-based models like BERT and DistilBERT. Finally, we extensively address the limitations of the study to provide directions for further research.

Introduction

     Analyzing the presence of mood and psychological disorders through behavioral and linguistic cues from social media data remains a critical area of interdisciplinary research. In addition to these disorders, the last decade has seen exponentially increasing attempts to assess related symptomatology such as depressive disorders, self-harm, and severity of mental illness using non-clinical data (Bucci et al., 2019). Social media platforms and other online discussion forums have been particularly appealing to the research community for various research purposes (e.g., population-level mental health monitoring (Conway & O’Connor, 2016), personal traits detection (Marouf et al., 2020), cyberbullying spotting (Bozyiğit et al., 2021), etc.) because of the massive scale of data. This massive data flow has resulted from increasing rates of internet access and people spontaneously sharing their suffering, pain, and struggle anonymously on these platforms (Ofek et al., 2015). Recognizing the early symptoms of depressive disorder through a person’s language use can prevent many disastrous outcomes like self-harm, suicide, etc., and even help deploy effective treatment in proper time. Moreover, the outbreak of the COVID-19 pandemic is likely to have devastating impacts on the mental health of millions of individuals as lockdown in the affected areas has reported high rises in the incident rates of mood disorder, including acute stress disorder, post-traumatic stress disorder, generalized anxiety disorder, and overall sub-clinical mental health deterioration (Singh et al., 2020). The scope of mental health deterioration during the COVID-19 pandemic and the comprehensive nature of diagnosing depressive disorders have provided an unprecedented need to infer the mental states of individuals from all-inclusive resources. Recent studies have revealed that valuable insights into the impact of the pandemic on population-level mental health can be inferred from posts or comments on social media (Low et al., 2020).

Conclusions and future work

     This work introduced a new typology for diagnosing depression severities from social media texts, as well as a unique dataset of labeled tweets with a confidence score for each label. The dataset was constructed based on strong ground truths and clinical validation, and it is expected to help alleviate the scarcity of mental health data to some extent. The description of the process and challenges in creating such a dataset may motivate researchers to collect similar corpora of this scale from other social media and discussion forums. The experimental results indicated that existing state-of-the-art models often fail to understand the contextual undertone of the data samples. Developing a model that is capable of comprehending the subdued relationship and differences among depression severities can result in an even better understanding of human cognition. Moreover, analysis of the classification performance indicates that there is no distinct division of keywords among different depression severities. The same keyword might be used differently to express different emotions, rather it is more important to understand the context of the tweet to diagnose the severity of depression. Broader implications of this research may include personalizing and directing preventative and awareness messages by health professionals to the users in need.

Highlights

Abstract

Keywords

1. Introduction

2. Related work

3. Relevance of the personal statements for depression detection

4. Adapting DPP-EXPEI to the depression detection task

5. Results and discussion

6. Conclusions and future work

Declaration of competing interest

Acknowledgments

References

Abstract

Depression is a common and very important health issue with serious effects in the daily life of people. Recently, several researchers have explored the analysis of user-generated data in social media to detect and diagnose signs of this mental disorder in individuals. In this regard, we tackled the depression detection task in social media considering the idea that terms located in phrases exposing personal statements (i.e., phrases characterized by the use of singular first person pronouns) have a special value for revealing signs of depression. First, we assessed the value of the personal statements for depression detection in social media. Second, we adapted an automatic approach that emphasizes the personal statements by means of a feature selection method and a term weighting scheme. Finally, we addressed the task in hand as an early detection problem, where the aim is to detect traces of depression with as much anticipation as possible. For evaluating these ideas, benchmark Reddit data for depression detection was used. The obtained results indicate that the personal statements have high relevance for revealing traces of depression. Furthermore, the results on early scenarios demonstrated that the proposed approach achieves high competitiveness compared with state-of-the-art methods, while maintaining its simplicity and interpretability.

1. Introduction

Depression is one of the most common mental health illnesses that affects seriously the daily life of people. It can produce a variety of emotional and physical problems that can diminish the activities of an individual provoking negative effects on her/his surrounding personal context, work [1] or school [2], and even basic human needs such as sleeping [3] and eating [4]. Severe cases can lead to self-harm and suicide [5]. Although fortunately, depression is a treatable disorder, it is often undetected due to several reasons such as the patient’s own inability to recognise the problem or because of the social stigma associated with mental illnesses. Recently, the rise in the use of social media has opened new opportunities for detecting depression [6–8]. In these platforms, people freely share and express their thoughts and feelings. Furthermore, often these media are used by depressed users to gather information about their illness or to discuss about their problems and symptoms.

Based on the idea that language is a powerful indicator of personality, social or emotional status, as well as mental health [9], several research works have leveraged the content generated by social media users as a rich source of knowledge to study, infer, and track users with mental illnesses such as depression. Particularly, it has been demonstrated that individuals having depression manifest changes in their language and behaviour (e.g., greater negative emotion and high selfattentional focus) [10]. In this regard, the development of methods for automatic depression detection on social media has gained a special interest in the computational linguistics research community. Such a challenging task has been generally tackled as a text classification problem, considering a wide variety of text representations and classification models, and concluding that the thematic and stylistic aspects are different between depressed and mental healthy people [11].

Abstract

     Social distancing practices and staying at home have increased the time people spend on social media with the purpose of exchanging and consuming information about completing their routine practices safely. The Covid-19 pandemic has disrupted in-person banking operations and increased the physical threat for both retail bankers and customers. Consequently, the world has moved toward internet banking with the purpose to continue routine transactions for paying bills, purchasing groceries, and shopping of brands. The present study aims to explore how the fearful environment of Covid-19 pandemic increased the social practices of internet banking and what challenges are faced by different customers during their use of internet banking. By understanding these experiences, system developers and marketers can improve their internet banking apps to address the needs and expectations of different customers. The study has opted for a relativist ontological position and social constructionist epistemological position to understand different realities in the same context and how customers experience internet banking in a fearful environment. The study used multiple qualitative research methods: Gibbs reflection cycle, semi-structured interviews with internet banking users, and focus group interviews with executives of public and private sector banks. Social actors through social media played a role with respect to understanding the nature of uncertainty during Covid-19 pandemic that changed customers’ behavior from traditional banking to internet banking. Information was shared through social media to avoid ATMs, debit and credit card exchange with cashiers, and cash exchange as these are no longer safe options for both bankers and customers. The major theoretical contribution of this study is to merge social practice theory and affordance of technology theory in the context of internet banking adoption. This study has discussed practical implications for marketers and system developers of retail banks.

Introduction

     Existing research on internet banking suggests that internet banking is relatively new in developing countries compared to the developed world (Abbasi et al., 2017; Rahi et al., 2019). For example, out of 200 million population of Pakistan, only 3.1 million are registered internet users, which highlights a very low adoption rate of internet banking (Rahi et al., 2020). Prior research highlighted causes of low adoption rate of internet banking in Pakistan, such as weak legal system, high poverty, corruption, high cybercrime, low trust (Akhlaq and Ahmed, 2013), low literacy rate, disheartening experiences, low internet accessibility and high electricity shortfall, high resistance to adoption of new technologies (Abbasi et al., 2017), low security, and high uncertainty (Rahi et al., 2019, 2020). Other studies reported that technology adoption and acceptance is low in Pakistan due to low financial performance and privacy risks (Anwar et al., 2020; Khan et al., 2018). It was found that low knowledge and awareness can negatively influence intention to use telebanking; therefore, more training programs are required to educate customers in developing countries, such as Jordan (Alalwan et al., 2016, 2018; Chauhan et al., 2019). In Malaysia, approximately 50% of the population have adopted internet banking, which is relatively low compared to developed countries, such as the USA (Amin, 2016; Yuen et al., 2015). In Tunisia, high fallibilities and vulnerabilities, such as economic and political instability as well as low accessibility to internet banking, have negatively influenced customer attitudes and adoption rate (Chaouali et al., 2017). Most of the existing literature on internet banking in Pakistan highlights the causes of low internet banking adoption (Anwar et al., 2020; Khan et al., 2018; Rahi et al., 2019, 2020), but this study aims to understand how the fearful environment of COVID-19 pandemic forced people to adopt internet banking as a social practice as well as the challenges customers faced during their use of internet banking.

Contribution

     First, the extensively used TAM provided understanding about customer perception and usefulness of internet banking system (Alsajjan and Dennis, 2010; Chauhan et al., 2019; Marakarkandy et al., 2017; Mohammadi, 2015). However, the TAM did not aid understanding of how uncertain situations can play a role in the adoption of the social practices of internet banking through social interactions and social wisdom via social media platforms. Second, the studies on original TAMs and extension of TAMs (e.g. Chauhan et al., 2019; Munoz-Leiva et al., 2017; Kumar et al., 2011; Lai, 2017) did not consider the external environment (i.e. fear appeal and health risks of COVID-19) as a pushing factor of internet banking adoption. Third, although there are many theories, such as theory of reasoned action and theory of planned behavior (Asongu et al., 2018; Alsajjan and Dennis, 2010), social cognitive theory (Boateng et al., 2016), perceived risk theory (Roy et al., 2017), and models such as Unified theory of acceptance and use of technology model (UTAUT) (Choudrie et al., 2018; Rahi et al., 2019), UTAUT2, and information system success models (Baabdullah et al., 2019; Tarhini et al., 2016) for understanding the factors that can influence the adoption of internet banking, most of the existing literature does not explore the role of social media which can increase awareness, motivation, and competencies for internet banking adoption in an uncertain and fearful environment. Therefore, this study has provided the socially practiced affordance of technology (SPAOT) model that aids understanding regarding the meanings created about uncertainty through the social interactions of social actors using social media platforms, which can enhance the adoption of internet banking. For example, the COVID-19 pandemic has changed social practices from traditional banking to internet banking, especially in Pakistan, as there are a lot of physical (i.e. fear of illness as many people are not following social distancing practices), social, and psychological threats (low hygiene during ATM use, fear and stress about visiting bank branches) involved in traditional banking practices. The SPAOT model highlights that shared material through social media increased people’s awareness, motivation, and competencies to use internet banking in a country where there are issues of lower literacy, readability, electricity, and internet accessibility.

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Abstract

Purpose: Unprecedented communication features of social media noticeably reinforce the active role of consumers in the value co-creation (VCC) of offline and online brands including social media. From the consumer perspective, this study examines a contribution of VCC behavior to consumer-based brand equity (CBBE) and consequent intention to use social media based on Instagram as the most popular platform in Kazakhstan. Methodology: A web-based survey provided data from 550 Instagram users in Almaty city. Empirical analysis includes testing statistical assumptions using SPSS 23, conducting confirmatory factor analysis (CFA), and structural equation modeling (SEM) using Warp PLS 7.0. Findings: With the result of this study investigation, the paper develops the model that explains the effect of VCC on continuous usage intention to use social media through CBBE comprising brand associations, brand loyalty and brand perceived quality. Originality: Even though the importance of social media in the brand VCC process is widely recognized, a brand equity view of social media brands with users’ participation is under-investigated. However, brand equity’s importance for firm sustainability in terms of long-term business strategy is indisputable. This research enhances brand equity theory and VCC concept with the empirical data within the modern social media context. Practical implications: Owners and managers of social media brands can use the conceptual model to grow, maintain and assess their brands’ equities through their marketing efforts on the consumer motivation for brand VCC activities driving continuous usage of the brands.

1. Introduction

Social media proven to be one of the most active online platforms worldwide with more than 3.6 billion users in 2020 anticipated to hit about 4.4 billion people in five years [1]. One of the outstanding features of social media is the unprecedented facilitation of peerto-peer communications and seller–buyer relationships, which is a part of a marketing strategy to meet the long-term brand objectives, including a vital target of higher consumer purchase intention [2]. Another significant utility of social media is the unique way to promote sustainable customer behavior through a sustainable business strategy [3,4].

From the brand perspective, social media noticeably increase consumers’ voice and power to influence the creation, promotion, and consumption of current brands activating customer participation in brands’ value creation. Sales-Vivo et al. [5] demonstrate this role in the example of business-to-business activities, where value co-creation (VCC) is an essential antecedent of a manufacturer’s economic satisfaction. Social media have become the integrative resource contributor to VCC on par with customers and firms reviving VCC theory born in the offline business era [6]. This study explores the impact of consumers’ co-creation behavior on brand value building of social media as the modern digital media, which are widely used for achieving various business aims including sustainability as a growing prominent global trend.

Abstract

     Sales research has widely noted the growing importance of social media in contemporary B2B selling. Yet, scholars have noted that measurement issues represent a challenge for advancing the study of social media use in selling, since research is lacking on high-rigour measures that provide insights into how salespersons are leveraging social media in their work. Accordingly, this study develops and validates activity-based measures for social selling based on two empirical studies. We find support for all three social selling dimensions of insight generation, connecting and engagement. The findings also extend the current knowledge about the outcomes of social selling by showing that salesperson thought leadership and acquisition performance mediate the sales performance relationship. On the antecedents’ side, we show novel evidence that management plays a central role in boosting social selling among the sales force through social media strategy, social media sales tools and firm content support. In turn, sales technology orientation and social influence represent the key individual-level drivers.

Introduction

     Digital and social touchpoints play an increasingly important role in B2B customers’ buying (e.g. Diba, Vella, & Abratt, 2019; Itani, Agnihotri, & Dingus, 2017; Itani, Krush, Agnihotri, & Trainor, 2020). Social media has, accordingly, become a central means for B2B salespeople to identify leads, understand buyers, foster relationships, and even build a personal brand through the systematic presence and sharing of engaging content on digital platforms (Agnihotri, Mani, Chaker, Daugherty, & Kothandaraman, 2020; Ancillai, Terho, Cardinali, & Pascucci, 2019; Chaker, Nowlin, Pivonka, Itani, & Agnihotri, 2022). Many sales organizations have recognized social selling as a strategic approach to attain organic visibility among customers, build thought leadership, and drive revenues, especially when applied throughout the salesforce and other customer-facing employees (see Meire, Ballings, & Van den Poel, 2017; Rapp, Beitelspacher, Grewal, & Hughes, 2013). Thus, even traditional industrial firms are investing in social media strategies and supporting technological platforms to increase the effective use of social media among their personnel (e.g. Moncrief, 2017; Trainor, Andzulis, Rapp, & Agnihotri, 2014).

Discussion

Theoretical implications

     This study has built activity-based measures for B2B social selling and validated the construct within a nomological network, based on the MOA framework and earlier social media use-focused sales research. The findings offer three substantial new contributions to the extant research.

     First, this study contributes by developing activity-based measures for B2B social selling. We build on recent qualitative conceptualization research (Ancillai et al., 2019) and develop a salesperson social selling measure including three dimensions of i) insight generation, ii) connecting to relevant actors, and iii) engaging customers. The measurement validation provides evidence that all three dimensions contribute significantly to the higher-order social selling construct. This novel measure can be seen as a substantial contribution to extant sales research for two reasons. To begin with, scholars have recognized that measurement issues represent a challenge for advancing research on the role of social media in selling (Guenzi & Nijssen, 2020). Thus far, all high-rigour scales have focused on measuring the extent to which salespersons use social media in sales work (see Agnihotri et al., 2016). Extant research has lacked high-rigour measures that specify how salespersons actually leverage social media in their work. The developed measure overcomes this challenge by offering a conceptually rooted and activity-based approach for measuring social selling. In addition to providing a new measure, our study also provides insights into the validity of the extant ‘social media use in sales’ scale (Agnihotri et al., 2016). Specifically, our results indicate that this extant overall usage scale sufficiently captures the whole domain of social selling in a valid way (see Fig. 1). The developed formative social selling measure has high convergent validity with the reflective overall scale, although the reflective scale seems to emphasize the insight generation dimension in relation to two other dimensions.

Abstract

     Social media has been taken as an important marketing tool for business today and it has also become increasingly imperative for marketers of SMEs to strategically utilize to attain competitive advantage and excellent performance. However, scholarly research on the social media marketing strategy of SMEs is still scant. To fill this research gap, this study proposes a social media marketing strategy framework for SMEs using customer knowledge management. The research captures the knowledge about customer by text mining and knowledge from customer by the Kano model revealing the critical requirements that will be used for the development of the social media marketing strategy framework, referred to as knowledge for customer. The strategy framework consists of the five common components for SMEs to conduct social media marketing which are Context, Channel, Content, Communication and Continuity. The study offers valuable insight into social media marketing improvement and customer knowledge management in enterprises.

Introduction

     Social media provides opportunities for businesses to improve their competitive position through interactive and cocreative ways [1], and this has made social media widely adopted as a business tool [2]. Social media has been playing a significant role in marketing strategy nowadays [3]. Despite the high adoption rate of social media, the actual usage in enterprises often seems to be improvised and lacks a clear strategy [4]. And enterprises apply their social media marketing strategies guided by only intuition or trial and scant research has systematically integrated and expanded knowledge on social media marketing strategies [5] [6]. Especially in Small and Medium-sized Enterprises (SMEs), due to relatively insufficient human or financial resources compared to large companies [7], the social media workers with relatively lower competency lack the strategy to conduct work, leading to poor performance of social media marketing. SMEs’ usual lack of human capital restrains their internal drive of knowledge, Customer Knowledge Management (CKM) becomes a strategic approach in order to create customer value [8]. This study proposes a strategy framework that identifies and classifies the components of social media marketing to guide SMEs’ marketing activities by using customer knowledge management.

Findings

     The study develops a social media marketing strategy framework using customer knowledge management. The results would be organized into the three types of customer knowledge.

Knowledge about Customer

The knowledge about customer is the critical issues extracted by text mining from customer feedback of SMEs social media marketing, as shown in Table V