1. Introduction
Communication has a noteworthy role in sustainable development. It facilitates the development process and becomes a form of development. As the development paradigm changes, development communication steadily takes on different forms and roles. In the new paradigm, society is more seen as an active subject of development than a passive object, in contrast to the top-down approach in the old paradigm [1]. In sustainable development, participatory communication from the community is required, which focuses more on dialogue [2].
Development communication and social change are social processes based on dialogue using different instruments and methods to achieve the ultimate goals of sustainable development or change at different levels of society. Communication is the key to the process of social change. Why are they development communication and social change, instead of merely development communication? Development communication is to fulfill planned development, while social change communication is to accomplish unplanned development processes (self-sufficiency by the community) [2].
Development communication goes hand in hand with the development of human capacity and partnerships; at the personal, institutional, and organizational levels. This indicates increasing knowledge sharing and collaboration to strengthen institutions, organizations, and people based on their priorities and characteristics [2]. Development communication and social development must be seen not limited to access or dissemination of information, but as a process of participation and dialogue, which has a significant role in facilitating knowledge understanding and sharing [3]. Better access to information, knowledge, and education is the best stimulant for development [2].
Sharing knowledge is a form of participatory communication [1,2,3,4]. It serves as a strategic and methodological concept in development communication. Sharing knowledge is egalitarian, building discussion and dialogue among participants [2]. Knowledge sharing has become an important component that will accumulate knowledge in society to support development. It also opens opportunities for dialogue and discussion among development actors [4,5]. In addition, sharing knowledge allows a person to learn [6], interact [7,8], communicate [9], innovate, and collaborate [8,10,11,12,13,14,15].
Digital literacy is an essential pillar of the development of the digital era [16]. In this digital era, the development of information and communication technology has brought changes in life, such as people’s communication behavior [17]. Social networks and social media are very popular and can affect daily life, especially for the younger generation [18]. Young people even use social media on a daily basis [19]. ICTs are an appropriate catalyst for achieving these development goals and can help developing countries jump through the stages of development emphasized [20]. Moreover, new media, including social media, have presented media that can reach a wider audience, with easier and quicker responses [21].
Social media is interesting because it offers various advantages. First, it is proven effective for knowledge sharing activities [22]. Second, social media can reach a wider scope of audience and enable users to respond faster [23,24,25,26,27] and break isolation in learning [12]. Third, it also opens opportunities for dialogue and interaction for its users [26,27,28,29,30,31,32]. Finally, social media applications can enable updating of the latest information from various sources [18,33], participate actively [8], and encourage and facilitate knowledge sharing [9,10,34,35,36,37,38,39]. Further, using social media to share knowledge will improve performance [40,41].
ICT, including social media, is a tool for the development communication and social change activities [2]. This technology is important to accelerate development and equalize opportunities. The successful use of social media to share knowledge depends on the readiness of the infrastructure, individual capacities, policies, and the environment. This readiness will be realized when there is no digital divide. From various sources, the digital divide includes the rural-urban gap, among demographic characteristics (age, education, gender, income). The digital divide will lead to a decline in the quality of education, lagging behind new trends and information, suboptimal income, less conducive rural development, uneven access to digital technology, new waves of migration, and differences in employment opportunities between rural and urban areas.
Many studies on knowledge sharing through social media have been carried out, most of which are related to business communication (marketing and customer satisfaction). These studies include [34,42,43,44,45,46,47,48,49,50,51,52,53,54,55]. Literature studies related to the use of social media to share knowledge have been performed. Ahmed et al.’s systematic review [56] in 2019 aims to recognize issues around social media in the context of knowledge sharing. This research has thoroughly described the activities of using social media to share knowledge, the context of sharing knowledge on each social media, the theories used in the research, the main challenges in making use of social media to share knowledge, as well as the limitations and gaps in research related to social media for sharing knowledge.
Mladenovic & Krajina [57] have explored the potential of using social media in sharing knowledge by individuals and identified future research opportunities. This research limits the examination on the use of social media for knowledge sharing by employees. The literature database comes from Scopus, Web of science, and EBSCO. The review was conducted on the number of articles per year, research methods, types of knowledge, fields of study, level of analysis, social media platforms, the country of the author’s institution, and future research opportunities.
To complement and provide new facts in diverse contexts, this systematic study aims to evaluate the use of social media to share knowledge in the context of development communication and social change. The phrases ‘use of social media for development communication and social change’ and ‘use of social media for knowledge sharing’ are used interchangeably in this paper. To enrich the research, this paper adds other reviews that have not been conducted in previous studies.
This study also seeks to suggest gaps and opportunities for further research on topics similar to literature research. We begin this manuscript by presenting the research background, and then continuing with the materials and methods, the results of the study and discussion, conclusions and implications, and finally the limitations of the literature study. The results of the studies and discussion include research developments from year to year, social media studied, various research locations, research subjects, theories, variables, and research methods. This research is a systematic literature review in which article networking was carried out with the PRISMA protocol and data were analyzed with Ms. Excel of 2021 version and NVIVO 12 plus.
2. Materials and Methods
The Systematic Literature Review (SLR) method was applied in this study. This study aims to conduct a literature review of knowledge sharing through social media with another aim to summarize previous research as well as to describe the novelty to advance research in this field. Further objectives are described in the following research questions:
RQ1:. How is the profile of the literature research (year, method, social media studied, research location, research subject, development sector, development communication participants, and social change)?
RQ2:. What are the types of research on the use of social media for development communication and social change?
RQ3:. What and how are the theories and variables used in research on the use of social media for development communication and social change?
Data analysis was performed with Ms. Excel of 2021 version and NVivo 12 Plus to obtain an overview of the results. The data collection was carried out using the PRISMA flowchart (Figure 1) including four stages, namely identification, abstract screening, manuscript eligibility, and article selection. In the identification stage, the researchers conducted a review using several different database sources to obtain complete and broader data, as well as enable to find of relevant studies [58]. Literature identification was carried out from reliable sources, namely Web of Science “
In the second process, manuscript screening was carried out. This stage utilized Ms. Excel of 2021 version. We eliminated repeated articles in the scanning process and obtained 1174 articles out of 1730 articles selected from the identification process. Third, we screened 1174 articles by title and abstract based on several inclusion and exclusion criteria. The inclusion criteria for the selected articles are as follows:
(1). original research
(2). original research published in 2017–2021
(3). research published in international journals in English
(4). articles examined the use of social media to share knowledge for development communication and social change
(5). full texts
This screening process obtained 113 full texts. Fourth, we conducted a feasibility test by reading all articles and selecting the articles based on the inclusion and exclusion criteria. Finally, we attained 57 articles. From n = 57 selected articles, we mapped the literature, including article title, year of publication, author, research objective, social media, research variables, theory, research location, method, sample population, research subject, data analysis, research results, sector development, development participants, and the journal index on the article (Figure 2).
This section describes the research profile of social media use for the development communication and social change. The profile includes the year, method, social media researched, research location, research subject, development sector, as well as the participants of development communication and social change.
3. Results and Discussion
3.1. Literature Research Profile
3.1.1. Year and Method
The development of research on ‘the use of social media for development communication and social change’ tends to increase each year. The decline in 2021 was more due to the fact that data collection was carried out in the middle of 2021 so it did not cover all research in 2021. This study will continuously be a trend and more interesting, especially considering that digital technology development is expanding rapidly. The sort of development, including social media, is a challenge for individual and organizational social behavior. The unique behavior of development communication and social change participants in all sectors attributed to the presence of digital technology is both noteworthy and challenging (Figure 3).
From year to year, most of the literature research has taken advantage of a quantitative approach, while the studies applying qualitative and mixed-method approaches were limited. In total, 60% of the research used a quantitative approach, 32% chose a qualitative approach, and 8% adopted a mixed-method approach. Research with qualitative and mixed method approaches potentially adds novel facts and a better understanding of the phenomenon of using social media to share knowledge in the context of development communication and social change. More details on this are explained in the next sub-discussion.
3.1.2. Social Media Researched
Facebook ranks first as the most researched social media, followed by Twitter, WhatsApp, Instagram, and YouTube. The social media that were most investigated recently were Twitter (2017), Facebook and Twitter (2018), Facebook (2019), Facebook (2020), and Facebook (2021). This is in line with the experts’ opinion that Facebook is one of the most dominant and widely studied social media platforms [7,59,60,61]. The Facebook platform is an ideal social media for sharing knowledge and learning tools [62,63,64].
Twitter is a microblogging social networking site and one of the most remarkable social media platforms [65,66]. It is a medium for real-time discourse on global events [67], a collaborative space [68], as well as disclosure and mobilization of support [69]. WhatsApp is widely accepted by almost every level of society. This social media platform is gaining recognition as a tool for sharing valuable information within society [59]. Having an online discussion group on WhatsApp enables the function of a social network [70], making it not only share knowledge but also seek knowledge [38].
YouTube can improve student engagement by discussing and exchanging ideas [71,72]. The high number of visitors suggests that YouTube is often used to facilitate the sharing of ideas, images, comments, and other forms of knowledge [72,73]. Through YouTube, participants feel a sense of togetherness, sense of belonging, and identity through their interactions with others [74]. Instagram becomes an important social media platform for sharing ideas and knowledge [75,76]. This platform does not rely on social interactions but is also used as a mass medium, in which the closeness among participants that is felt is seen as in the parasocial interactions [76].
Figure 4 shows that YouTube, Instagram, and WhatsApp are not the most researched social media, and studies on TikTok have not been recorded. Future research can explore more with knowledge sharing studies on the YouTube, Instagram, and WhatsApp platforms. This is interesting considering that Hootsuite 2022 mentions the top six most used social media in the world as of October 2022 in a row, namely Facebook (2.9 billion), YouTube (2.5 billion), WhatsApp (2 billion), Instagram (1.4 billion), WeChat (1.3 billion), TikTok (1 billion). On the contrary, it can be seen in Figure 4 that Twitter ranks 2nd in terms of the most studied media social platform, but the fact has confirmed that it was only used by 544 million users in 2022 (Figure 5).
3.1.3. Research Locations and Subjects
The top five research locations that have been extensively researched are China, the United States, Global, Scotland, and England, respectively. The literature research was carried out across five continents (Figure 6), namely the Asian Continent, including China, India, Iran, Korea, Malaysia, Pakistan, Israel, Taiwan, and Saudi Arabia; the European continent consisting of Denmark, England, Scotland, Norway, Sweden, Spain, and Turkey; the African continent comprising Ghana, Nigeria, Tanzania, and Egypt; the American continent covering the United States and South Carolina; and the Australian continent including Australia. Previous literature research has not explored much of the use of social media for development communication and social change in developing countries. Further research still needs to examine the behavior of sharing knowledge through social media in locations that have not been widely explored. We recommend paying close attention to developing compared with developed countries, given the differences in the development context between the two types of countries.
Figure 6 displays the research subjects or individuals/groups/organizations examined in the research on the use of social media for development communication and social change. The most researched subject is the public (e.g., social media users, online communities, and members of social media groups), with a percentage of 26%. The next most examined subject is academics (lecturers, teachers, students, and education institutions) reaching 17%, and employees achieving 14%. Research subjects that have not been widely explored include agricultural extension workers (1%), scientists (2%), experts (2%), doctors (2%), and farmers (3%).
This study will be increasingly developed and sharp with the presence of novel phenomena by exploring the subjects rarely researched such as farmers, agricultural extension workers, scientists, experts, and others. The use of social media to share farmers’ or extension officers’ knowledge will provide a unique phenomenon considering that the agricultural and rural world may experience a digital divide.
The digital divide will have an impact on reducing the quality of education, lagging behind new trends and information, non-maximum income, less conducive rural development, uneven access to digital technology, new waves of migration, and differences in job opportunities between rural and urban areas. The gaps include, among others, rural-urban disparities and among demographic characteristics [77,78,79,80,81,82,83]. This becomes a challenge for the development of the agricultural sector. The behavior of sharing knowledge on social media by scientists, experts, and doctors will also produce distinctive facts. Interesting facts that will arise are such as how to share knowledge on social media about secrets or tacit knowledge by these professionals.
3.1.4. Development Sector and Participants in Development Communication and Social Change
This paper reviews the development sector and social change serving as the contexts for using social media to share knowledge. Figure 7 presents that health and education are the most studied development sectors, each of which reaches 26%. Social change is an informal sector that occurs in society, unplanned, and non-strategic. Sharing health-related knowledge has been revealed to be important for chronic disease prevention, and increasing public health literacy as a form of early intervention and additional therapy [84]. WHO has identified education and knowledge sharing as top priorities [72].
Development sectors that have received little attention to be examined include culinary (2%), environment (2%), tourism (2%), sports (2%), politics (2%), and agriculture (5%). These sectors which have not been widely studied need to be further explored to sharpen and enrich the phenomena in the study of the use of social media for knowledge sharing in these sectors.
Figure 7 demonstrates the participants in development communication and social change engaging in knowledge sharing behavior on social media. The development participants are different from the research subjects presented in Figure 6. The research subjects are particular or other participants in the development communication and social change. For example, Lei’s research [71] in 2021 examined the students’ behavior of sharing knowledge on social media, but the ones being asked to give responses were the teachers. In other words, the research subjects were the teachers, while the participants in development communication were students.
Many literature studies (33%) have examined the public as the actors in sharing knowledge on social media. Furthermore, 22% of research examines knowledge sharing through social media by academics. Employees become the actors in sharing knowledge on social media in 15% of research.
3.2. Types of Research on the Use of Social Media for Development Communication and Social Change
This paper maps out several types of research based on motivation, data collection techniques, and the role of the variable of ‘use of social media for knowledge sharing’. Based on the motivation, as presented in Table 2, the research is categorized into Type 1 and Type 2 [52].
Type 1 positions social media as a tool for sharing knowledge, more focusing on the factors encouraging the use of technology (social media) and less centering on the knowledge sharing process. Type 2 makes social media a scenario for sharing knowledge and focuses on the knowledge sharing process. A total of 23% (13/57) of the studies fall into the Type 1 category and 77% (44/57) of them belong to the Type 2 category.
3.2.1. Type 1 and Type 2 Research
In order to have a better understanding of Type 1 research, we present some examples of research by providing the objectives and hypotheses/research questions. Table 1 demonstrates several studies belonging to Type 1, including those performed by [85,86,87]. Type 1 research focuses on the use of social media technology as a means of sharing knowledge and/or the factors that encourage or influence an individual to use social media technology to share knowledge in the context of development communication and social change. Type 1 research aims, for instance, to measure the level of social media use by construction professionals for knowledge sharing, to investigate the determinants of social media use for knowledge sharing [85], and to provide empirical evidence on the positive effects of social media use for knowledge sharing and innovation performance [86], and examine sources of self-efficacy in using social media for knowledge sharing and its effects [87].
Type 1 research hypotheses, for example, are the use of social media to share knowledge is positively related to innovation performance [86], and facilitating conditions have a significant effect on the intention to use social media to share knowledge [85]. Not all Type 1 research establishes a hypothesis. Alshahrani and Pennington [87] do not propose a hypothesis because it uses qualitative methods. The research questions include (1) what sources of self-efficacy researchers rely on regarding the use of social media to share knowledge, and (2) how those sources of social media influence use. Type X research is dominated by a qualitative approach rather than a quantitative or mixed method. Research with other approaches can be explored to get the acuity of this study.
Type 2 research positions social media as a scenario in the knowledge sharing process. This type of research focuses on the knowledge sharing process in social media. This research includes the research by [88,89,90,91,92]. The objectives of this research are to figure out the knowledge sharing process on social media and/or to explore the factors encouraging/influencing individuals to share knowledge on social media. For instance, previous studies examine the visibility of communication on knowledge sharing [91,92], ICT [88], mutual benefit, and social presence [89,90]. Type 2 research hypothesizes that, for example, message transparency has a noteworthy effect on sharing knowledge on social media [91], reputation is positively related to sharing knowledge on social media [92], attitude is positively related to creating and sharing tags on social media [89]. In contrast to Type 1 research, Type 2 is dominated by a quantitative approach. Exploration of other approaches is needed to find new phenomena to enrich this study.
Table 2 shows that the majority (77%) of studies on the use of social media for development communication research are classified as Type 2. Type 1 research is explored by 23% of the literature research. Type 1 research focuses on social media platforms as a knowledge sharing tool. This type of research evaluates the effectiveness of a social media platform, including comparing it to other platforms, to improve the functions of the platform. This is not only necessary but also interesting, especially since the development of digital media is increasing rapidly. Exploring the role of social media platforms in all development sectors is necessary to build a culture of knowledge sharing, considering that all sectors face challenges from the disruption era.
3.2.2. Type A and Type B Research
We identify the following discovery categories as type A and type B. These types are mapped based on the data collection techniques. The literature review has found that 35 (62%) research articles fall into the type A category, while 15 (26%) articles belong to type B research. There are 12% of research articles (7/57) that fall into both types, type A and type B.
Type A research investigates the responses of respondents/informants to knowledge sharing activities on social media. This type is characterized by the use of survey (questionnaire) and/or in-depth interview data-collecting methods. These individual responses from the survey and interview are recorded by the researchers to identify the responses, patterns, processes, and interactions of individuals towards the activities of using social media for knowledge sharing. Type A research applies interviews [38,69,93,94] and survey techniques [61,62,84,85,88,95,96].
Research that is included in type B is the research that obtains data on ‘the use of social media for knowledge sharing’ by directly capturing the process of knowledge sharing on social media. This type of research collects data by taking them within a specified time limit. Data collection activities in the type B research articles are, for example, recording dialogues that occur in WhatsApp discussion groups [59,70], identifying knowledge sharing content in an event discussed on Twitter [65,67,97], and analyzing knowledge sharing activity on Facebook social media [60,97].
Most of the literature studies are classified as Type A (62%), while the rest are Type B (26%). In addition, as presented in Table 3, both types are dominated by quantitative approaches. Opportunities are still wide open for Type B research, specifically research that collects data on the use of social media for knowledge sharing directly from social media platforms, studying the process of dialogue communication that occurs. This type of study does not focus on surveying individual responses to the behavior of using social media to share knowledge.
3.2.3. Type X, Type Y, and Type Z Research
Subsequently, we map the type based on the role of the use of social media for knowledge sharing as a variable. Based on this category, the studies we have reviewed consist of Type X, Type Y, and Type Z. Table 2 shows that 7% (5/57) of the studies are Type X, 39% (22/57) are classified as Type Y, and 54 % (31/57) are included in Type Z.
Type X research investigates the use of social media for knowledge sharing and the impacts resulting from this behavior. Type X places the use of social media for knowledge sharing as an independent variable. Discussion of this type of research usually reinforces the importance and role of using social media for knowledge sharing [59,86,94,98]. The purpose of the study is to examine the variables serving as the impacts or results of using social media for knowledge sharing, such as innovation performance [86], dialogue and knowledge creation [94], as well as effectiveness and accuracy [59,98].
Type X research hypothesis, including the use of social media to share knowledge, has a positive effect on innovation performance [86]. Type X research that employs a qualitative approach does not establish hypotheses, but we can observe the hypotheses from the research questions or research results. For example, Alghamdi’s research [94] question is “Does the use of SM help increase their understanding and engagement with science?”. Udem’s study [59] has found that sharing information using WhatsApp impacts professional information.
Type X research that focuses on the benefits or impacts of behavior using social media for knowledge sharing is limited. To add novel facts, more exploration is needed for this type of research. Type X research is also dominated by a quantitative approach, and therefore, exploring Type X research using other approaches (qualitative or mixed method) will help build theory.
Type Y research is research that examines the factors that encourage/influence someone to use technology (social media) for knowledge sharing or motivate/affect someone to share knowledge on social media. The phrase ‘use of social media to share knowledge’ acts as the dependent variable (Variable Y). The purpose of the type Y research is to examine the uses and determinants of using social media to share knowledge; for example, investigating the frequency, nature, and determinants of the use of social media to share construction professional knowledge [85], investigating how various incentives, types of managerial control mechanisms, and work arrangements interact to understand the successful use of social media for knowledge sharing [90], and developing a theoretical model to investigate the factors that motivate individuals in sharing healthcare knowledge in WeChat social media [84].
The type Y research hypotheses include, among others, Performance Expectancy (PE) has a significant positive effect on the intention to use (IU) social media for knowledge sharing, social influence has a significant effect on the intention to use social media to share knowledge [85]; altruism has a positive effect on employee’s intention to create and share tags, perceived ease of use is related to attitudes towards making and sharing tags [89]; ICT has a significant influence on knowledge sharing on social media [88]; the presence of a supportive moderator increases knowledge sharing on social media, and the presence of social feedback cues increases knowledge sharing on social media [90].
Our Type Z research is divided into two. Type Z1 is the research that investigates the use of social media for knowledge sharing and the driving factors as well as the resulting impacts. Type Z0 is the research that does not examine both but merely focuses on investigating the use of social media for knowledge sharing. The previous studies included in Type Z0 research have been conducted by [38,72,93,99,100] may be; while those categorized into Type Z1 research have been performed by [62,91,92].
The objectives of Type Z0 research are identifying the challenges faced by doctors in using social media in medical and healthcare settings [93], characterizing the nature of public responses to anti-smoking campaigns [99], and investigating the outcomes that researchers expect from using social media to share knowledge [73]. Type Z0 research tends to use a qualitative approach and does not set a hypothesis.
Type Z0 research results reveal, for example, the challenges faced by doctors in using social media to share health knowledge along with an overview of these challenges [93], showing how WhatsApp is used not only to seek but also to share knowledge that supports official business activities [38], and describing exposure to tobacco prevention health messages on social media [99].
The goals of Type Z1 research, among others, are to find out how communication visibility can affect knowledge management and creativity and how regulatory focus can moderate the relationship [91], explore work motivation and communication visibility provided by social media that affect knowledge sharing and employee work efficiency [92], and examine the effects of trust and perceived usefulness on students’ knowledge sharing via Facebook and on students’ academic performance and recognition [62]. Type Z1 research tends to use a quantitative or mixed method approach and establishes a hypothesis on one of the approaches used in the research.
Type Z1 research has a tendency to propose hypotheses that examine the driving factors as well as the impact of the use of social media for knowledge sharing. The hypotheses of type Z1 research, for example, are message transparency is positively related to knowledge sharing, knowledge sharing is positively related to employee creativity [91]; message transparency is positively related to employee knowledge sharing, knowledge sharing is positively related to employee work efficiency [92]; trust has a positive effect on sharing knowledge through Facebook, and sharing knowledge has a positive effect on student academic achievement [62].
Studies on the use of social media to share knowledge are frequently carried out to investigate the behavior of using social media and the determinants/drivers of this behavior (Type Y). There are still very few studies on the consequences/impacts of the behavior of using social media for knowledge sharing. For this reason, type X research will add new facts related to knowledge sharing and social media.
3.3. Theories and Variables in Research on the Use of Social Media for Development Communication and Social Change
There are 42 theories used in research on the use of social media for the development communication and social change. Table 3 presents that one literature study uses one or more theories (61%) and some do not mention the theory employed (39%). The theories used in research are those close to communication, psychology or social psychology, behavior, management, sociology, and computer technology. The behavior of sharing knowledge through social media is perceived as a system of communication behavior making use of a set of information technologies. This system is seen relating to factors that correlate with information technology, as well as the social and relational context. For this reason, social variables also appear in this type of research.
Table 3Theories, variables, and methods of research on the use of social media for development communication and social change.
| Theory/Model & Variable | Type 1 | Type 2 | Type A | Type B | Type X | Type Y | Type Z | Method | ||
|---|---|---|---|---|---|---|---|---|---|---|
| Quantitative | Qualitative | Mixed-Method | ||||||||
| Affordance Theory (n = 1) | ||||||||||
| knowledge framing (KFB), knowledge targeting (KTB), knowledge creating (KCB), functionality (SMF), intensity (SMI), preference (SMP) | - | v | v | - | - | v | - | [21] | - | - |
| Agency Theory (n = 1) | ||||||||||
| importance of knowledge sharing, paid to share knowledge, social cues, supportive moderator, policing moderator, knowledge sharing using SM | - | v | v | - | - | v | - | [90] | - | - |
| Communication Visibility Theory (n = 1) | ||||||||||
| message transparency, network translucence, knowledge sharing on SM, knowledge hiding on SM, creativity, promotion, prevention focus | - | v | v | - | - | - | v | [91] | - | - |
| Communicative Ecology Theory (CET) (n = 1) | ||||||||||
| perceived usefulness, trust, health status, expertise, involvement, interestingness, emotionality, institution-based trust, source credibility, knowledge sharing on social media, positivity, health concern, a propensity to trust | - | v | v | - | - | v | - | [84] | - | - |
| Community of Practice Theory (n = 1) | ||||||||||
| hard work, improving thinking, effective practice, knowledge sharing behavior through social media, knowledge gain, professional development, emotionality, knowledge contributing, creating knowledge, competence, domain, commitment, community | - | v | - | v | - | - | v | - | - | [74] |
| Constructivist Grounded Theory (n = 1) | ||||||||||
| information monitoring, information organizing, information behavior, information experience | - | v | v | v | - | v | - | - | [68] | - |
| Contingency Theory (n = 1) | ||||||||||
| importance of knowledge sharing, paid to share knowledge, social cues, supportive moderator, policing moderator, knowledge sharing using SM | - | v | v | - | - | v | - | [90] | - | - |
| Dynamic Theory of Knowledge (n = 1) | ||||||||||
| orientation of social media, the role of knowledge sharing in social media, privacy, confidentiality, source credibility, interaction quality, information, overload, lack of internet, access | v | - | v | - | - | - | v | - | [37] | - |
| Goffman’s Theory of Social Interaction (n = 1) | ||||||||||
| social relation, self-representational interest, organization set-up, organizational rules, content type, characteristics of the network, interaction patterns | - | v | v | v | - | v | - | [101] | - | - |
| Innovation Resistance Theory (n = 1) | ||||||||||
| usage barriers, value barriers, physical risks, trust risks, security belief barriers, mutual benefit belief barriers, image barriers | - | v | v | - | - | - | v | [102] | - | - |
| Knowledge Sharing in Organization (n = 1) | ||||||||||
| memory, impersonal nature of information, perception, time pressure, perceptions of inequality, laziness, trust, overload, affordance, free riding, awareness, preference for knowledge, knowledge collecting, belief that one’s own knowledge is not useful, incentive, knowledge retrieving, knowledge contributing, knowledge sharing | - | v | v | - | - | - | v | - | [35] | - |
| Micro-Sociological Perspective Erving Goffman’s (n = 1) | ||||||||||
| perceptions, ideas, perceived knowledge, Goffman’s concepts, performance on social media, use of social media for knowledge sharing | - | v | - | v | - | - | v | - | [76] | - |
| Online Community Value Model (n = 1) | ||||||||||
| attitude, perception, social element type, cultural element type, the structure of online communities, intellectual element type, political element-type | - | v | v | v | - | - | v | - | [103] | - |
| Online Knowledge Management Theory (n = 1) | ||||||||||
| altruism, relationship, reciprocal benefit, intention, attitude | - | v | - | v | - | - | v | [104] | - | - |
| Organization Citizenship Behavior (n = 1) | ||||||||||
| altruism, intention to use SM for KS, reciprocal benefit, expected relationship, social norms, social identity, online self-presentation, we-intention, social capital, social support, informational support, affectionate support, social companionship, social interaction, trust, shared vision, and language, use social media for knowledge sharing | - | v | v | - | - | v | - | [61] | - | - |
| Reactance Theory (n = 1) | ||||||||||
| the tone of the comment, nature of the contribution, agreement with the prevention message, mention of a government agency, policy/regulation, promotion/spam, format (content) | - | v | - | v | - | - | v | [99] | - | - |
| Regulatory Focus Theory (n = 1) | ||||||||||
| message transparency, network translucence, knowledge sharing on SM, knowledge hiding on SM, creativity, promotion, prevention focus | - | v | v | - | - | - | v | [91] | - | - |
| Self-Determination Theory (n = 1) | ||||||||||
| norm of reciprocity, reputation, relationship, altruism, trust, knowledge sharing, the knowledge-collecting behavior of members (COLLECT), community promotion (CP) | - | v | v | - | - | - | v | [105] | - | - |
| Self-Presentation Theory (n = 1) | ||||||||||
| altruism, intention to use SM for KS, reciprocal benefit, expected relationship, social norms, social identity, online self-presentation, we-intention, social capital, social support, informational support, affectionate support, social companionship, social interaction, trust, shared vision, and language, use social media for knowledge sharing | - | v | v | - | - | v | - | [61] | - | - |
| Self Motivation Theory (n = 1) | ||||||||||
| knowledge framing (KFB), knowledge targeting (KTB), knowledge creation (KCB), functionally (SMF), intensity (SMI), preference (SMP) | - | v | v | - | - | v | - | [21] | - | - |
| Social Capital Theory (n = 1) | ||||||||||
| altruism, intention to use SM for KS, reciprocal benefit, expected relationship, social norms, social identity, online self-presentation, we-intention, social capital, social support, informational support, affectionate support, social companionship, social interaction, trust, shared vision, and language, use social media for knowledge sharing | - | v | v | - | - | v | - | [61] | - | - |
| Social Cognitive Theory (n = 5) | ||||||||||
| self-efficacy, emotional arousal, vicarious experiences, verbal persuasion, personal mastery experiences | v | - | v | - | - | v | - | - | [87] | - |
| response rate, demographic, type of social media platform, self-efficacy, use of social media for KS | v | - | v | - | - | v | - | - | [73] | - |
| use social media for knowledge sharing, outcome expectation | v | - | v | - | - | - | v | - | [100] | - |
| altruism, intention to use SM for KS, reciprocal benefit, expected relationship, social norms, social identity, online self-presentation, we-intention, social capital, social support, informational support, affectionate support, social companionship, social interaction, trust, shared vision, and language, use social media for knowledge sharing | - | v | v | - | - | v | - | [61] | - | - |
| expectation, behavioral capability, social and structural impediments, observational learning, self-efficacy | v | - | v | - | - | - | - | [106] | - | - |
| Social Exchange Theory (SET) (n = 3) | ||||||||||
| altruism, intention to use SM for KS, reciprocal benefit, expected relationship, social norms, social identity, online self-presentation, we-intention, social capital, social support, informational support, affectionate support, social companionship, social interaction, trust, shared vision, and language, use social media for knowledge sharing | - | v | v | - | - | v | - | [61] | - | - |
| levels of communication, altruism, academic performance, reputation, trust, knowledge sharing on social media, reciprocal benefit | - | v | v | - | - | - | v | [107] | - | - |
| reciprocity, relationship, reputation, normative commitment, knowledge sharing intention, continuance commitment, affective commitment, commitment | - | v | v | - | - | v | - | [34] | - | - |
| Social Identity Theory (n = 3) | ||||||||||
| topic content, type of SM platform, engagement, knowledge sharing in SM, social support, perception of content, avoidance of sharing, inactive discussion, perceived usefulness of content | - | v | v | v | - | - | v | - | [108] | - |
| facilitating condition, KSSE, knowledge sharing willingness, creativity, friendship, social skill, create useful knowledge self-efficacy, belief, web-specific-self-efficacy (WSSE), online identity, knowledge-creation self-efficacy (KCSE), knowledge sharing intention, knowledge sharing on social media | - | v | v | - | - | v | - | [7] | - | - |
| social trust, social identity, reputation, shared language, indirect exchange indirect KS on SM), direct exchange (direct KS on SM) | - | v | v | - | - | v | - | [109] | - | - |
| Social Network Theory (n = 3) | ||||||||||
| orientation of social media, the role of knowledge sharing in social media, privacy, confidentiality, source credibility, interaction quality, information, overload, lack of internet, access | v | - | v | - | - | - | v | - | [37] | - |
| social trust, social identity, reputation, shared language, indirect exchange indirect KS on SM), direct exchange (direct KS on SM) | - | v | v | - | - | v | - | [109] | - | - |
| social relation, self-representational interest, organizational set-up, organizational rules, content type, characteristics of the network, interaction patterns | - | v | v | v | - | v | - | [101] | - | - |
| Technological Frames of Reference (TFR) (n = 1) | ||||||||||
| nature of technology, technology strategy, technology in use, participation, role, and capability, decision making, use of social media | - | v | - | v | - | - | v | - | [110] | - |
| Technology Acceptance Model (TAM) (n = 1) | ||||||||||
| organizational recognition, perceived ease of use (PEOU), pro-sharing norms, usability, perceived usefulness, perceived social presence, behavioral intention, attitudes, altruism, reciprocal benefit, management support, create and share tags, knowledge sharing | - | v | v | - | - | v | - | [89] | - | - |
| Big Five Inventory (BFI-S) Assessment (n = 1) | ||||||||||
| trust, neuroticism, knowledge sharing on social media, knowledge sharing behavior on social media, subjective well-being, personality traits, agreeableness, conscientiousness, openness, extraversion | - | v | v | - | - | v | - | [111] | - | - |
| The Big Five Personality Traits (n = 1) | ||||||||||
| trust, neuroticism, knowledge sharing on social media, knowledge sharing behavior on social media, subjective well-being, personality traits, agreeableness, conscientiousness, openness, extraversion | - | v | v | - | - | v | - | [111] | - | - |
| The Communication Visibility Theory (n = 1) | ||||||||||
| metaknowledge, work efficiency, reputation, social networking, message transparency, network translucence, knowledge sharing | - | v | v | - | - | - | v | [92] | - | - |
| The Elaboration Likelihood Model (An Information-Processing Theory) (n = 1) | ||||||||||
| knowledge sharing on social media, personal characteristics, interpersonal interactions, user expertise, willingness, knowledge adoption willingness, knowledge sharing willingness, institution-based trust, content credibility, source credibility | - | v | v | - | - | v | - | [95] | - | - |
| The Social Presence and Media Richness Theory (n = 1) | ||||||||||
| presence/self-disclosure, platform design, work processes, metaknowledge, ambient awareness, the use of social media for knowledge sharing, composition nature of the group | v | - | v | - | - | - | - | - | [38] | - |
| The Socialisation - Externalisation - Combination - Internalisation (SECI) Model (n = 1) | ||||||||||
| learning (SML), expertise, problem-solving, innovating, the initiation of informal and professional discussion, fostering collective intelligence; the visibility of tacit and personal knowledge, accessibility of tacit and personal knowledge, the investment in time and effort required for knowledge sharing | - | v | v | - | - | - | v | [112] | - | - |
| The Theory of Ba (n = 1) | ||||||||||
| learning (SML), expertise, problem-solving, innovating, the initiation of informal and professional discussion, fostering collective intelligence; the visibility of tacit and personal knowledge, accessibility of tacit and personal knowledge, the investment in time and effort required for knowledge sharing | - | v | v | - | - | - | v | [112] | - | - |
| The Theory of Reciprocity (n = 1) | ||||||||||
| social trust, social identity, reputation, shared language, indirect exchange indirect KS on SM), direct exchange (direct KS on SM) | - | v | v | - | - | v | - | [109] | - | - |
| The Trust Transfer Theory (n = 1) | ||||||||||
| knowledge sharing on social media, personal characteristics, interpersonal interactions, user expertise, willingness, knowledge adoption willingness, knowledge sharing willingness, institution-based trust, content credibility, source credibility | - | v | v | - | - | v | - | [95] | - | - |
| Theory On Parental Practices (n = 1) | ||||||||||
| expectation, behavioral capability, social and structural impediments, observational learning, self-efficacy | v | - | v | - | - | - | - | [106] | - | - |
| Theory of Planned Behaviour (TPB) (n = 1) | ||||||||||
| facilitating condition, KSSE, knowledge sharing willingness, creativity, friendship, social skill, create useful knowledge self-efficacy, belief, web-specific-self-efficacy (WSSE), online identity, knowledge-creation self-efficacy (KCSE), knowledge sharing intention, knowledge sharing on social media | - | v | v | - | - | v | - | [7] | - | - |
| The Zone of Proximal Development (ZPD) (n = 1) | ||||||||||
| use of social media for knowledge sharing (using social media to teach, using social media to create scientific dialogue), create scientific discourse, engagement | v | - | v | - | - | - | - | - | [94] | - |
| Unified Theory of Acceptance and Use of Technology (UTAUT) (n = 1) | ||||||||||
| social influence, actual use (the use of social media for knowledge sharing), trust, learning, hedonic motivation, effort expectancy, facilitating conditions, KSSE, performance expectancy, intention | v | - | v | - | - | v | - | [85] | - | - |
| Valance–Instrumentality–Expectancy Theory (VIE) (n = 1) | ||||||||||
| intention to use social media for knowledge sharing, importance of knowledge exchange (IKE), perceived usefulness of social media (PUS), experience using social media (EUS), knowledge seeker, knowledge contributor | v | - | v | - | - | v | - | [96] | - | - |
| Work Motivation Theory (n = 1) | ||||||||||
| metaknowledge, work efficiency, reputation, social networking, message transparency, network translucence, knowledge sharing | - | v | v | - | - | - | v | [92] | - | - |
| No Mention (n = 22) | ||||||||||
| the use of social media for knowledge sharing (SMT use for acquisition of costumer information, SMT use for acquisition of competitor information, SMT use for knowledge sharing), innovation performance | v | - | v | - | v | - | - | [86] | - | - |
| extent of knowledge sharing in SM (Twitter), content framing, information need, wider interaction, speed of response, collaboration, use social media for KS, providing inspiration, extra stream information, the job more interesting | v | - | v | v | - | v | - | - | [66] | - |
| content category, user category, use social media (twitter) for KS | - | v | - | v | - | - | v | - | [65] | - |
| use social media for knowledge sharing, engagement, interaction, comment | v | - | v | - | - | - | v | - | [113] | - |
| demographic, time pressure, sharing experience/view, seeking information/opinion, knowledge sharing on social media, emotional exchange, moderator posts, vacination decision, vacination clinic and cost | - | v | - | v | - | - | v | [70] | - | - |
| demographic, experience, type of discussion | - | v | v | v | - | - | v | [114] | - | - |
| virtual environment, interest, engagement, technique of pedagogy, interactions, drawbacks, use of social media | - | v | v | - | - | - | v | - | [71] | - |
| effectiveness of learning, engagement, enjoyment | v | - | v | - | - | - | v | - | - | [115] |
| ICT, knowledge sharing on social media | - | v | v | - | - | v | - | [88] | - | - |
| frequency of use, preference (SMP), content, effectiveness of SM for KS | - | v | - | v | - | - | v | - | [116] | - |
| professional information sharing | - | v | - | v | v | - | - | [59] | - | - |
| institutional, reciprocity, e-WOM quality, mutual trust, perceived online attachment (POAM), perceived online relationship commitment (PORC), perceived ease of use (PEOU), perceived usefulness (PU), knowledge sharing, online knowledge sharing behavior | - | v | v | - | - | v | - | [117] | - | - |
| characteristics content, user characteristics, attitude, time | - | v | - | v | - | v | - | [67] | - | - |
| content credibility; type of rumour; source type; content type; mentions prevention or early detection/screening exams | - | v | - | v | - | - | v | - | [118] | - |
| cues to action, self-efficacy, perceived benefits, engagement | - | v | - | v | - | v | - | [119] | - | - |
| engagement, satisfaction | - | v | v | - | - | - | v | [72] | - | - |
| impression, reach, engagement, knowledge sharing on SM (campaign) | - | v | - | v | - | - | v | - | - | [97] |
| motivation, social media controversies, subjects matter, law and policy, language, emoticons, debate process | - | v | - | v | - | - | v | [63] | - | - |
| presence, creative ethics, flavor disclosure, process disclosure, recipe disclosure | - | v | v | v | v | - | - | - | - | [98] |
| engagement, reach, sentiment of comment, content category | - | v | - | v | - | v | - | [60] | - | - |
| engagement, online dialogue, dialogue strategies | - | v | v | - | - | - | v | - | [69] | - |
| engagement, themes online discussion, component of scientific thinking, topic content | - | v | - | v | - | - | v | - | - | [64] |
3.3.1. The Theories Used in Research on the Use of Social Media for Development Communication and Social Change
The mostly applied theory to research the use of social media for the development communication and social change is Social Cognitive Theory (SCT), followed by a combination of Social Exchange Theory (SET), Social Identity Theory (SIT), and Social Network Theory (SNT).
SCT is a popular Bandura theory, especially in the field of education. This theory focuses on individual factors that encourage someone to behave [100]. SCT provides a suitable guide for behavior change. This theory operates at the interpersonal level with an emphasis on strengthening self-efficacy through reciprocal determinism, interactions between humans and the environment, and observational learning [106].
This theory is used to sharpen the concept of the variables studied. For example, Alshahrani’s studies in [73,87] applied SCT to hone the concept of ‘self-efficacy’ as the main research variable. Furthermore, the interview guide and questionnaire were developed from the SCT theoretical lens. SCT can also serve to develop theoretical frameworks. For example, Alshahrani [100] utilizes this theory to expand the integrative theoretical framework of ‘outcome expectancy’ by exploring the relative amount of influence of knowledge sharing on social media.
Along with SIT and SNT, SET ranks as the second most widely used theory in the research we have reviewed. SET becomes a popular theory of social behavior that focuses on interactions and relationships. Littlejohn and Foss [120] mention SET as a combination of anthropological, economic, sociological, and psychological concepts. SET can form a research framework by integrating with other theories. Hsu’s [61] research integrates reciprocal benefits, altruism, and expected relations as determinants of intention to share knowledge on social media; by integrating SET, SCT, and Organization Citizenship Behavior (OCB).
SET was also used in Luo’s [34] research that integrated the commitment model with SET into a research framework. This study concludes that user commitment mediates the effect factors of the benefits of social exchange (relationship, reputation, reciprocity) for knowledge exchange. Moghavvemi’s [62] research uses SET to build a research model on Facebook.
SIT was developed by Tajfel and Turner in 1985. This theory reveals that a person tends to categorize himself/herself according to religious affiliation, age, organizational membership, and gender [109]. SIT suggests that a person develops strategies to achieve and maintain a positive identity within a social group. Positive identity can be strengthened by discussions, sharing experiences, and interventions [108].
Research applying SIT has been conducted by [7,108,109]. Kim Lee [7] investigates the effect of online identity on Facebook on knowledge-sharing behavior. Individuals with higher social skills, closer friendships, and greater creativity are more likely to encourage knowledge-sharing. Online identities constructed through social skills, friendship, and creativity lead to strong intentions to share knowledge. Mkhize [109] scrutinizes the contribution of social identity to both direct and indirect exchanges of knowledge.
SNT believes that social network is a vital element in building knowledge. Social media is the most innovative technology owing to the presence of the internet. Global attention has been paid more to social media than to traditional social networks [93]. The social network is important, both relating to the content exchanged on social media and the structure of content flow in social networks on social media. SNT seeks to explain about how are the offline and online relationships between a set of actors in knowledge-sharing activities, why these relationships occur, and how these relationships affect actors [101,109].
Munthali 2021 investigates the structure of the communication network on the platform to find out how actors are connected to each other and what is the resulting level of decentralization. This study explicates the objects studied with SNT. This research is one of the studies concluding the differences in interconnectivity between two different platforms. The network density of the DFAD platform is higher than that of the Plantwise platform.
Table 3 presents that the theories referred to by Type 1 and Type 2 research are relatively diverse. SET and SIT appear to be merely referenced by Type 2 research. On the other hand, SCT and SNT are employed by both Type 1 and Type 2 research. However, SCT is more dominantly applied in Type 1 research while SNT is more often used in Type 2 studies.
The four dominant theories (SCT, SET, SIT, SNT) are widely used in previous studies, including those on the use of social media to share knowledge because of the strengths or advantages that these theories offer. Littlejohn [121] states that a theory is said to be good/strong if it meets one or all of the criteria, which include the theoretical scope, appropriateness, heuristic value, validity, parsimony, and openness. The theoretical scope is a theory that can cover a broad domain, is inclusive and accommodating, and can be applied to highly different situations. Appropriateness means whether the epistemological, ontological, and axiological assumptions in the theory are suitable for the theoretical questions discussed and the research methods used. Heuristic value is an indicator that covers the extent to which a theory can generate original ideas by exploring new situations. Validity denotes that a theory is said to be valid if it is useful. The concepts and relationships determined by the theory are strictly appropriate and observable. Parsimony means that if there are two equally valid theories, the theory with the simplest logical explanation is better than the other. If a theory can explain behavior with only one variable, it is better than a theory that incorporates many variables. Openness is a theory that recognizes diversity and is able to encourage dialogue with other perspectives.
For example, SCT emphasizes self-efficacy as a determining factor for individuals to imitate behavior (based on knowledge-sharing content) on social media. Table 3 demonstrates that self-efficacy is used in many studies (5) with the theories used (5). SET proposes reciprocal benefit as a determinant of behavior. The reciprocal benefit variable has been employed in 4 literature studies with 7 selected theoretical studies. This confirms that SCT and SET have a broad theoretical scope, meaning that they are used in various fields of science (development sectors), such as health, education, construction industry, and tourism.
Table 3 demonstrates the theories that have not been extensively explored to sharpen and develop studies on the use of social media for the development communication and social change. Exploration of several theories such as, Unified Theory of Acceptance and Use of Technology (UTAUT), Technology Acceptance Model (TAM), SCT, and Knowledge Management will enrich relevant studies and sharpen phenomena. For example, the UTAUT model is suitable for exploring the use of social media for knowledge-sharing in the context of development communication and social change.
Regarding the use of information system technology, UTAUT is a comprehensive technology adoption model with high explanatory power. UTAUT is used extensively in the literature because it considers individual differences when applied to ICT adoption. The UTAUT theory consists of four factors impacting technology user acceptance. The four factors are performance expectation (PE), effort expectation (EE), social influence (SI), and facilitation condition (FC). Performance expectation (PE) is the first factor. Although UTAUT has been validated in previous studies on similar subjects, neither the original model (UTAUT) nor its previous extensions have been evaluated or validated for the use of social media for knowledge-sharing by constructive professionals. The labels used for constructions describe the essence of the constructions and are meant to be independent of any particular theoretical perspective [122].
The use of UTAUT has not yet been widespread for social media-based knowledge-sharing research in the context of development communication and social change. Because this research is minor and relatively new, it requires a more thorough exploration of the UTAUT model. This exploration will provide a wealth of knowledge, sharpen theories, and accomplish practical needs.
Table 3 presents that most of the studies on the use of social media for development communication and social change are carried out using a quantitative approach. Observing the four theories that dominate the literature research, we will describe the research approaches applied. Research with Social Cognitive Theory is conducted using a quantitative approach [61,106] and a qualitative approach [73,87,100] with a percentage of 40% and 60%, respectively. A total of 100% of studies have been conducted by applying Social Exchange Theory and 100% of the studies use a quantitative approach [34,61,62]. Studies with Social Identity Theory have been carried out using quantitative [7,109] and qualitative [108] approaches. A total of 67% of studies with Social Network Theory have been performed with a quantitative approach [101,109] and 33% with a qualitative approach [93].
Quantitative research in the social sciences is defined as the accuracy of the description of a variable and the accuracy of the relationship between one variable and another [123]. The results of research with a quantitative method allow statistical generalizations required to describe a very large population. Therefore, hypothesis testing is performed in a quantitative method. Hypothesis testing is carried out to determine the significance of the influence or relationship between variables/constructs [124].
In contrast, the results of quantitative studies lack a detailed understanding of statistical tests or the sizes of effects. Qualitative data can help build understanding from the results of quantitative research [125,126,127]. The quantitative method cannot provide an in-depth explanation of phenomena. The results of research with statistical calculations often make quantitative researchers only focus on numbers without explaining the significance of the numbers or providing a better understanding of the phenomena. The qualitative method provides a comprehensive description of the relationship [125,126,127].
Qualitative research tends to be more open by incorporating new evidence and problems [124]. Therefore, the data obtained are not only statistical data but also explicit statements from informants and other great deal of evidence that are qualitative and significant. The mixed method is also limited in the literature review. The studies conducted with this method are developing as alternatives because they are relatively new [127], instead of unsuitable. The assumption of this method is that a combination of qualitative and quantitative approaches will provide a more comprehensive understanding of the research problems than either method used separately. The mixed method is based on a pragmatic paradigm. The researchers view and use multiple approaches to collect and analyze data rather than merely applying one method, either quantitative or qualitative [127].
In a pragmatic view, the mixed method is more advantageous because the combined methods are oriented towards problem-solving without debating the use of both quantitative and qualitative paradigms and approaches. The use of this method will present a comprehensive exploration and at the same time solve real problems in the field. It is proven that opportunities for similar research with qualitative and mixed methods are wide open [127]. The use of either qualitative method or mixed method will provide new phenomena and better understanding by sharing knowledge through social media [128] in the context of development communication and social change. Limited understanding of the procedural, contextual and experiential aspects of the knowledge sharing process can be attributed to a lack of qualitative research [128]. Qualitative research is intended to explore meaning (significance), behavior, experiences, and opinions about the adoption of social networking applications as a tool for sharing knowledge [39,128].
This paper recommends further studies to explore more thoroughly the use of approaches that are different from the existing dominant approaches. For example, research with SCT dominated by a qualitative approach can further explore the use of the mixed method and quantitative approaches. The use of qualitative and mixed-method approaches for research with SET will further develop and sharpen theories. With a qualitative approach, it is possible to obtain constructs and a variety of contexts.
As we have explained that although serving as the theories that focus on the acceptance and use of information system technology, UTAUT and TAM are seldom applied. Table 3 shows that the studies with UTAUT and TAM were carried out using a quantitative approach. Quantitative methods for research with the TAM model [89] and UTAUT [85] are appropriate. Both models have well-established constructs and relationships between constructs and have been frequently examined in numerous studies. The quantitative method is applied to test the TAM and UTAUT models and to evaluate hypotheses.
On the other hand, with a qualitative approach, serendipity (i.e., an unexpected factor or opportunity that has major implications) is likely to be found. Qualitative research will enable deeper comprehension of the problems with what, how, and why questions [124]. With a qualitative method, we will have the opportunity to discover new constructions or new theoretical elaboration models, for example, from UTAUT or TAM.
With an inductive, bottom-up, and egalitarian approach; With a qualitative approach we can find new things to enrich the explanation of indicators to existing variables. Even with this method can be found new constructs as constituents of existing models, henceforth testing the found propositions. The constructs found from qualitative methods can come from other theoretical constructs of the model used to enrich the elaboration of the theory.
3.3.2. Variables Used in Research on the Use of Social Media for Communication of Development and Social Change
Table 3 shows that there are several variable equations used by many literature studies. That is, several variables have been studied with different theories or it can be said that the use of these variables is not strict for certain theories. Table 3 shows that there are variables studied in many literature studies. These variables include content (19%), engagement (18%), trust (16%), intention (14%), self-efficacy (9%), usefulness (9%), altruism (9%), relationship (9%), reputation (9%), reciprocal benefit (7%), facilitating condition (5%), attitude (5%), experience (5%), source credibility (5%), reciprocity (5%), demographic (5%).
Here we describe the top five variables studied by many literature studies, namely content, engagement, trust, intention, knowledge sharing, self-efficacy, usefulness, altruism, relationship, and reputation.
Content variables can include content category, type of category, topic content, subject matter, content credibility, content framing, and usefulness of content. The usefulness of the content has a positive effect on the sharing of healthcare knowledge on WeChat. A review of the usefulness of content is important for structuring ways to encourage someone to share knowledge. Shared content should attract interest and evoke stronger emotions [89]. Majmundar [99] examines the content of public responses to tobacco prevention campaigns on Twitter, Facebook, and Instagram. The study encourages the use of social media for dialogue on controversial health stubs such as smoking. An investigation of content attributes is important to look into the credibility of the content. This credibility is very important to maintain the relevance and accuracy of the content so as not to share the wrong knowledge. For example, attribute breast cancer-related content to be shared on social media [118]. Content credibility has a stronger relationship with willingness to adopt than with sharing willingness [95].
Engagement in research can be participants speaking up, asking more questions, being willing to take the initiative to practice, and the frequency of “retweets” and “Likes” [119]. The use of social media strengthens student engagement in sharing knowledge on social media [94]. The role of engagement in research on the use of social media for knowledge sharing as a variable influenced by the presence of peer messages, interesting content, and elements is also fun and increases the size of the group [108]. Variable engagement can be found more frequently in some qualitative studies on the use of social media for knowledge sharing [69,71,94,108,113].
Trusts have a major role to play in building relationships and collaborations between humanity. Trust is widely studied because this variable is a powerful motivating factor for individuals to share knowledge with others [62,85,105,111]. One is willing to share with others trusted. Individuals trust content that comes from people whose content they have seen [35].
The variable ‘intention’ is used by many studies because ‘intention’ is a powerful predictor of someone using social media to share knowledge in the context of communication of development and social change. Intention is the extent to which social media users believe that they will share their knowledge again on social media. Social media can provide us with information about the usefulness of knowledge shared through ‘Retweets’ and ‘Likes’ [61]. The intention to share knowledge significantly predicts knowledge [7,61,89]. The intention to share knowledge mediates the relationship between personal online identity and knowledge sharing [7].
The variables of altruism and relationship are closely related to interaction. Altruism and expected relationships determine the intention to share knowledge on social media based on Social Cognitive Theory, Social Exchange Theory, and Organization Citizenship Behavior [61]. Research using altruism as a variable includes [61,62,89,104,105]. Hsu’s research [61] shows that altruism has a significant effect on knowledge sharing intentions. Allam’s research [89] also resulted in altruism having a significant effect on employee intent on creating and dividing tags.
The relationship is the degree to which people believe that they have acquired and strengthened relationships and friendships built from long-term interactions. Relationships are significantly related to individual commitments in virtual communities [34,105]. Expected relations are factors that influence attitudes toward knowledge sharing and subsequently towards the acceptance and intention of using social media [61].
Reputation as a determinant of knowledge sharing behavior has been widely studied such as the research of [34,92,105,109], studying that reputation is a determinant of knowledge sharing behavior, but some studies investigate reputation as a consequence of knowledge sharing behavior. For example, Moghavvemi’s study [62] found that knowledge sharing behavior had a positive effect on student reputation [62].
Self-efficacy is an important component of Social Cognitive Theory that influences behavior and skills including the use of social media to share knowledge [87]. Self-efficacy sources are a reference in increasing the use of social media for knowledge sharing [87]. Self-efficacy (WSSE = Web Specific-Self Efficacy) effectively predicts both intention and knowledge sharing [7].
Usefulness is defined as the expediency of using social media to share knowledge related to financial, psychological, and cost [119]. Usefulness variables have different mentions of variable names in various research articles, such as ‘perceived benefits’ ‘perceived usefulness’ [89,117,119]. Usefulness is one of five positive antecedents in online knowledge-sharing behavior that are felt to have the strongest effect [96,117]. Improving perceived usability motivates individuals to spread Jin’s health knowledge in 2019 [84]. Usefulness has an impact on the exchange of knowledge in a conducive atmosphere [96].
We observed that the variables used consisted of individual internal factors, relational, and individual external factors. Internal factors of the individual, namely the determining factors that come from within the individual, are more psychological, for example, intention, self-efficacy, usefulness, experience, attitude, and demographic. Relational factors are the shaping factors that exist because they relate to others, including engagement, trust, altruism, reciprocal benefit, relationship, reciprocity, and reputation. The behavior of sharing knowledge on social media is a communication and relational behavior, involving two or more people. There are knowledge givers and knowledge recipients who can play alternating roles. This is different from playing an online game. Online games are not always relational and involve others in playing games let alone individual online games. External factors of individuals are factors that come from outside the individual and not as a result of relating to others, for example, content category, facilitating conditions, source credibility, and transparency of messages.
3.3.3. Pro and Contrary to Previous Research Variables
This paper will also present the results of our review of 17 dominant variables in literature research related to the results of the hypothesis test of these variables. Table 4 shows the dominant variables and hypothesis test results that include significant and insignificant ones. These results will give an idea of which variables are still debatable regarding the test results.
Table 4 shows the dominant variables and how quantitative research hypothesis test results or explanations of qualitative research. Hypothesis test results include significant and insignificant. This paper will show what are the dominant variables that have hypothesis test results that are contradictory to previous studies. The information will be accompanied by an overview of why the results show so. The dominant variables in literature research that show different hypothesis results are altruism, attitude, content, experience, intention, reciprocity, self-efficacy, and trust.
Altruism is a variable recognized by many studies as an important factor that encourages individuals to share knowledge on social media [61,89]. On the other hand, Moghavvemi’s research [62] is different from previous studies. Moghavvemi’s study [62] placed altruism as a moderator variable. Altruism moderates reciprocal benefits as a result, students with high levels of altruism are more open to sharing knowledge without preconditions than those with low altruism.
Allam Hesham’s research [89] found that reciprocal benefits have an insignificant effect on employee attitudes in creating and sharing tags on Facebook, Twitter, and Instagram. Regular employees will create and share tags without expecting mutual benefits from others. The activity of creating and sharing tags they consider to be work that does not require much effort and they are happy to do so [89]. This is in contrast to previous studies that have found that reciprocal benefits are a factor in encouraging individuals to share knowledge, for example, [129]. These results are also different from [62,61] studies which found that reciprocal benefits affect attitudes.
Chatterjee’s research [96] found that the experience of using social media was insignificantly associated with knowledge-sharing behavior on social media, and the resulting path coefficient was too low (0.06).
Allam Hesham’s research [89] found that intention has an insignificant relationship with factors that influence it such as perceived managerial influence and organizational recognition. Employee perception of managerial support has no significant effect on the intention of creating and sharing tags (tagged content). This means that the behavior of employees sharing tags will not be affected by managerial support, they do it for personal purposes considering that it benefits an employee. Likewise, there is no significant relationship between organizational recognition and employee future tag creation and sharing behavior. That is, an employee will intend to share the tag with others not because of the direct rewards of the organization, but rather because he participates in the social community or because he just wants to help others find useful information [89].
In many Social Exchange Theory studies, reciprocity has a positive effect on affective commitment, such as [130]. Luo’s research [34] found reciprocity had no significant effect on the continued commitment of knowledge contributors in the virtual community. Continuous commitment is expected to have a significant effect on the intention of sharing knowledge in the virtual community [34].
WSSE is no longer significant when the intention to share knowledge is controlled [7]. Self-efficacy will have a significant effect on voluntary behavior intentions not controlled/required.
The mixed results of the relationships/influences between the above variables require further study to reconfirm. Follow-up studies will sharpen the relationships between variables and may even provide a new understanding of the conditions and context of the study site.
4. Conclusions and Implications
This paper has explored that the studies of the use of social media for knowledge sharing in the context of development communication and social change are significant. The results of the study provide a review and convey limitations and research gaps related to the use of social media to share knowledge in the context of development communication and social change.
The results of the study have revealed that the quantitative approach dominates the literature research, and therefore, more exploration with a different approach is required. Research with qualitative and mixed method approaches will add new phenomena, develop theoretical constructs, and improve understanding of the phenomena of using social media to share knowledge. This study also shows that literature research is dominated by studies on the health and education sectors, the public as research subjects, developed countries as the research locations, and Facebook as the social media studied. Relevant studies that are rarely carried out focusing on, among others, the agricultural, culinary, and tourism sectors will potentially strengthen novel, noteworthy, and practically useful phenomena. Furthermore, the selection of research subjects, which are rarely scrutinized, such as agricultural extension officers, farmers, scientists, experts, and doctors will provide distinctive facts about their behavior for this study. Further exploration of developing countries will also enrich the phenomena and offer practice benefits. Future research is necessary to investigate media platforms that are world trends, other than the most researched social media, such as YouTube, WhatsApp, Instagram, and TikTok. The studies on social media platforms are important to examine the effectiveness and improve the performance of social media platform technology.
This study has found that various types of literature research are categorized into several types based on motivation (Type 1 and Type 2), data collection techniques (Type A and Type B), and the role of the variable of ‘the use of social media for knowledge sharing’ (Type X, Type Y, and Type Z). Type 2 research (using social media as a scenario in research and focusing on the process of sharing knowledge) dominates the literature of this study, while Type 1 research, which focuses on the use of social media technology and the use of social media as a tool for sharing knowledge, is limited in numbers. Moreover, most of the reviewed studies fall into Type A, examining the behavior of using social media to share knowledge by investigating the responses of respondents/informants to knowledge sharing activities on social media. Type B studies, examining the behavior of using social media to share knowledge by capturing the social media under study to directly investigate the process of sharing knowledge on social media, are still limited. Diverse research types will facilitate further studies in determining the direction of research focus, making it easier to determine research objectives and understand the research context. The results of the study have disclosed theories widely applied in most literature studies, including Social Cognitive Theory (SCT), Social Exchange Theory (SET), Social Identity Theory (SIT), and Social Network Theory (SNT). As theories that focus on the acceptance and use of information systems technology, UTAUT and TAM are seldom used in the literature. Similar studies need to explore phenomena with suitable theories that are still infrequently employed. This will provide a wealth of knowledge, development of theory, and the fulfillment of practical needs. This study has also figured out differences in the results of the relationship test between variables. Future research can reconfirm this diversity of results.
This study has theoretical implications in the form of findings regarding the limitations and gaps in the studies linked to the use of social media to share knowledge in the context of development communication and social change. Further relevant studies can utilize the results of this study to develop studies on the use of social media to share knowledge in the context of development communication and social change. In addition, this study provides practical implications that the results of the study can serve as references for development and social change practitioners in building a culture of sharing knowledge on social media by development/social change participants in the respective sectors.
5. Research Limitations
This paper has thoroughly reviewed the literature studies but it has several limitations. First, studies on the use of social media for knowledge sharing have been carried out in many parts of the world, but the inclusion criteria in our study only accommodate the selection of articles written in English and thus reduce the number of articles obtained. Second, our literature review only selects original research and excludes the types of proceedings, reviews, e-books, book chapters, and/or reports as selected articles, which means that these criteria reduce the chances of selecting articles in this study. We have mapped and synthesized the theories and selected article variables. However, we realize the absence of testing or scrutinization of the theories that are more suitable to be applied and the criteria, such as the criteria used by [121]. Moreover, this study has not examined which variables give stronger influences.
Conceptualization, H.I.; methodology, H.I.; software, H.I.; validation, H.I., S.S., P.M. and D.G.; formal analysis, H.I.; investigation, H.I.; resources, H.I.; data curation, H.I.; writing—original draft preparation, H.I. and S.S.; writing—review and editing, H.I., S.S., P.M. and D.G.; visualization, H.I.; supervision, H.I.; project administration, H.I. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
Not applicable.
The author’s gratitude is conveyed to Universitas Sebelas Maret.
The authors declare no conflict of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 5. The world’s most-used social media platforms (it’s modified from We’re social and Hootsuite 2020–2022).
Keywords and number of article databases.
| Keyword | Source | ||
|---|---|---|---|
| WoS | PubMed | Scopus | |
| “knowledge sharing” AND (“social media” OR “new media”) | 247 | 14 | 186 |
| “knowledge transfer” AND (“social media” OR “new media”) | 79 | 0 | 45 |
| “knowledge exchange” AND (“social media” OR “new media”) | 45 | 0 | 32 |
| “knowledge flow” AND (“social media” OR “new media”) | 8 | 0 | 7 |
| “dialogue AND (“social media” OR “new media”) | 588 | 0 | 463 |
| “participatory communication” AND (“social media” OR “new media”) | 7 | 0 | 9 |
| Sub total | 975 | 14 | 741 |
| Total | 1730 | ||
Types of research on the use of social media for development communication and social change.
| Categories | Name | Description | n |
|---|---|---|---|
| Motivation |
type 1 | Research that focuses on the use of social media for knowledge sharing and or the factors that encourage individuals to use technology (social media) for knowledge sharing, social media is seen as a tool for knowledge sharing, focusing on technology (social media). | 13 |
| type 2 | Research that focuses on the process of sharing knowledge on social media and or the factors that encourage individuals to do knowledge sharing on social media social as a scenario for knowledge sharing, focusing on the process of knowledge sharing | 44 | |
| Data collection techniques | type A | Research that reports on ‘the use of social media for knowledge sharing’ by investigating individual responses to knowledge sharing through social media | 42 |
| type B | Research that reports on ‘the use of social media for knowledge sharing’ by capturing directly the process of knowledge sharing on social media | 20 | |
| Variable roles | type X | Research investigating ‘the use of social media for knowledge sharing’ and the resulting impacts, places ‘use of social media for knowledge sharing’ as a free variable | 4 |
| type Y | Research investigating ‘the use of social media for knowledge sharing’ and the factors that influence/shape, places ‘use of social media for knowledge sharing’ as a bound variable | 22 | |
| type Z | Research that investigates the ‘use of social media for knowledge sharing’ both along with the resulting impacts and factors that influence/shape (Z1), or only research that examines the ‘use of social media for knowledge sharing’ without including the resulting impacts or factors that influence/shape (Z0) | 31 |
Pro and contrary to previous research variables.
| Variable | Significant | Non-Significant |
|---|---|---|
| altruism | [ |
[ |
| attitude | [ |
[ |
| content | [ |
[ |
| demographic | [ |
- |
| engagement | [ |
- |
| experience | [ |
[ |
| facilitating condition | [ |
- |
| intention | [ |
[ |
| reciprocal benefit | [ |
[ |
| reciprocity | [ |
[ |
| relationship | [ |
- |
| reputation | [ |
- |
| self-efficacy | [ |
[ |
| source credibility | [ |
- |
| trust | [ |
[ |
| usefulness | [ |
- |
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; Sarwoprasodjo, Sarwititi 2 ; Muljono, Pudji 2 ; Gandasari, Dyah 3 1 Department of Communication Science and Community Development, Faculty of Human Ecology, IPB University, FEMA IPB Building Wing 1, 2nd Floor, Kamper Street, IPB Darmaga Campus, Bogor 16680, West Java, Indonesia; Study Program of Agricultural Extension and Communication, Faculty of Agriculture, Universitas Sebelas Maret, Ir. Sutami Street No 36A, Surakarta 57126, Central Java, Indonesia; Center for Farmer Protection and Empowerment Studies, Universitas Sebelas Maret, Ir Sutami Street No 36A, Surakarta 57126, Central Java, Indonesia
2 Department of Communication Science and Community Development, Faculty of Human Ecology, IPB University, FEMA IPB Building Wing 1, 2nd Floor, Kamper Street, IPB Darmaga Campus, Bogor 16680, West Java, Indonesia
3 Animal Husbandry and Animal Welfare Extension, Department of Animal Husbandry, Bogor Agricultural Development Polytechnic (Polbangtan Bogor), MoA. Cinagara Campus, Snakma Street, Pasir Buncir, Caringin, Bogor 16730, West Java, Indonesia