First Monday

Salient design features that your social media platform needs: The case of online communities of interest by Alireza Nili and Alistair Barros



Abstract
We discuss the importance of salient design features (which support purposeful action and relate to functional affordances) of customer-to-customer social media platforms that support communities of interest, focussed on open-ended data and user tasks, in contrast to business-to-customer platforms, focussed on relatively fixed data and transactions. We provide an exposition of their design features, shedding light on how they support tasks of searching and decision-making in which users elicit knowledge from their community, for targeted needs to needs which are less known and speculative. We also discuss that how our findings can be useful for designing social media platforms that support other types of online communities such as online communities of practice. Our research contributes to the ongoing discourse in the information systems field about the importance of identifying and articulating the salient design features of an information technology that is useful for theory and practice.

Contents

1. Introduction
2. Conceptual clarification and study context
3. Methodology
4. Findings
5. Discussion
6. Conclusion

 


 

Introduction

Social media platforms have been re-defining and transforming communication, collaboration, and creation manners among customers and businesses. They have disrupted several industries such as personal banking, real estate services, travel and tourism, education and insurance. Social networks support creation and exchange of user-generated content using Internet-based platforms (Kietzmann, et al., 2011; Kane, et al., 2014; Roberts and Piller, 2016). However, among several different types of social media platforms that have been launched, more than a decade ago, some (e.g., Twitter, Facebook, and Instagram) continue to exist and have had seismic growth in the number of their users and amount of information exchanged, while many other platforms have lacked a significant growth velocity, and have faltered and closed. The growing usage of successful social media platforms can be particularly attributed to the capabilities of those platforms to meet a user’s need for connecting them with other users and producing and sharing information with the users connections on the platform (Rauniar, et al., 2014; Calefato, et al., 2015). The extent of these capabilities depends on what salient design features (higher-level enablement) have been embedded in a particular social media platform (Kietzmann, et al., 2011; Kane, et al., 2014; Rauniar, et al., 2014; Nili, et al., 2020a). We identify and discuss the importance of salient design features of customer-to-customer social media platforms that support communities of interest, focussed on open-ended data and user’s tasks, which are distinct from conventional business-to-customer platforms focussed on relatively fixed data and transactions. Unlike general-purpose social media platforms such as Facebook and Twitter, we studied social media platforms are focussed on service delivery in which consumer services for user goals are discovered and executed. We shed light on design features of social media platforms for consumer services in the real-estate domain, in which peer-to-peer interactions play a pivotal role for the requisite tasks of property searching and decision-making to purchase. These involve users eliciting knowledge from their community, for known and targeted needs to needs which are less known and speculative.

“Many existing theories of user perceptions and attitudes towards technology suffer from ‘over investigation’ of user attitude and under investigation of the technologies that cause them. This can be the result of a pursuit of generalisability at the expense of accuracy and salience to practice” [1]. By identifying the salient design features of the social media platform, our research is a response to the ongoing discourse in the information systems field about the importance of identifying and articulating the salient design features of an information technology (IT) that is useful for both theory and practice (Niederman, et al., 2009; Tate and Evermann, 2009; Grover and Lyytinen, 2015; Tate, et al., 2015) and particularly to the concern that theories and models of user behaviour often do not truly cover these design features (Hirschheim and Klein, 2003; Seddon and Lyytinen, 2008; Tate and Everman, 2009; Grover and Lyytinen, 2015). Authors such as Tate and Evermann (2009), Tate, et al. (2015), Grover and Lyytinen (2015) and Nili, et al. (2020a) specifically argue that identifying and articulating salient design features of an IT is important because they contribute to forming user beliefs (e.g., perceived usefulness and utility) and intention to use the IT, which are antecedents of a user behaviour, particularly adoption of the technology.

With regards to achieving the goal of our research study, we had the options of exploring salient design features of a group of existing social media platforms and designing a social media platform to inductively identify and elaborate on salient design features. We chose both options with a focus on the second option. The second option reflected the lack of support in current social media platforms for a high degree of interaction among users and integration with service applications (in our case real-estate platforms). This option required commitment in terms of time and effort by researchers, however the process of designing the platform gave us the opportunity of inductively identifying detailed and as many relevant salient design features as possible. We designed a social media platform for supporting users in an online community of interest, who are seeking a property (e.g., a house) that suits their circumstances. The choice of real-estate reflects the open-ended and uncertain features of interest concerning property and surrounding communities and lifestyle conveniences (e.g., transportation, shopping, medical, school and entertainment services). The information for many of these features are not available in current real-estate platforms, which tend to reflect a ‘property listing’ paradigm where only basic attributes of properties are recorded (e.g., land size, number of rooms, historical prices). In this paper, we focus on our empirical research efforts. We also integrated our inductive findings with research literature to place the findings within the context of relevant research and to ensure comprehensiveness of our findings. Because of the focus of our research study, the case and context of the study (explained in the next section), we answer the question: “What are the salient design features of social media platforms that support communities of interest?”

In the rest of the paper, we first explain the context of our study. Second, we explain the theoretical foundation based on which we clarify the concept of ‘salient design features’. Third, we explain the research methodology, particularly the data collection and analysis methods we employed. Fourth, we explain the findings of our research study. Fifth, we discuss the implications of the findings for theory and practice and explain how the findings can be useful for designing social media platforms that support other types of communities such as communities of practice. The paper ends with the conclusion.

 

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2. Conceptual clarification and study context

Among the numerous studies of social media, there are a few studies (e.g., boyd and Ellison, 2008; Treem and Leonardi, 2012; Kane, et al., 2014; Parchoma, 2014; Karahanna, et al., 2018) that are relevant to the topic of our study in general. However even these few studies have mostly focused on high-level affordances of social media (e.g., constructs such as self-presentation, information sharing, socialisation, collaboration, interactivity, visibility, editability, persistence, and association) or have provided some examples of very specific features (e.g., liking, listening to music, uploading songs or video, and tagging microblogs). We have not identified any study that focuses on the topic we discuss in our research study (identifying and elaborating on salient design features of social media platforms for service applications), leaving us with the option of conducting an inductive empirical research (which we explain in the methodology section) to identify and elaborate on the design features. We provide a conceptual clarification of ‘salient design features’ using the concept of functional affordances and then explain the context of our empirical research study.

2.1. Salient design feature: Conceptual clarification

We use the affordance theory (Gibson, 1979; Hartson, 2003) as the theoretical basis to clarify what we mean by a salient design feature. An affordance is what is allowed or ‘afforded’ by an object in an environment (Gibson, 1979; Hartson, 2003). Strong, et al. [2] define an affordance as: “the potential for behaviours associated with achieving an immediate concrete outcome and arising from the relation between an artefact and a goal-oriented actor or actors.” Artifact affordances can be used to develop abstractions of information technology design features in a theoretically and practically well-grounded way (Tate and Evermann, 2009). For example, Burton-Jones and Volkoff (2017) identified affordances of an electronic healthcare record system and the outcomes of those affordances that result in effective use of the system.

Hartson (2003) argues that four types of affordances need to be considered for designing an IT: physical affordance (or real affordance), cognitive affordance (or perceived affordance), sensory affordance, and functional affordance. He explains that a physical affordance is defined as a design feature that enables, helps, or supports doing something easily (e.g., appropriate size and an easy-to-access location of a button on the screen helps users to click on it easily). A cognitive affordance is a design feature that enables, helps, or supports thinking or knowing about something. For example, a precise button label supports users to understand the purpose of the button. A sensory affordance is a design feature that enables, helps, or supports users cognitive and physical actions (e.g., feeling and/or sensing sounds or colour changes when a user clicks on a link or button). A functional affordance is a higher-level enablement that ties usage to usefulness and enables, helps, or supports doing something useful in a specific domain, such as accomplishing a task at work. “The terms cognitive affordance and physical affordance refer to parallel and equally important usability concepts... [and] sensory affordance plays a supporting role for them... [and] the concept of physical affordance carries a mandatory component of utility or purposeful action (functional affordance)” [3].

Salient design features of a social media platform are those features of the platform that support purposeful action and therefore relate to functional affordances of the IT, as such they support higher-level enablement such as connecting a user to other users, supporting the user to share information with connections on the platform, and managing one’s own privacy. Salient design features are not very low-level features (e.g., specific settings, security questions, and tabs) that do not provide rich insights that is useful for wide audience and are not at a very high level of abstraction that seldom contribute to scholarly research. For the purpose of this paper, we specifically focus on the concept of salient design features.

2.2. The context of the study

Different social media platforms support different types of communities, such as communities of interest, communities of practice, communities of consumption and communities of discourse. An online community of interest is a network of people who use a social media platform to share the same interests and join discussions to help each other, share their ideas or opinion to contribute to solving a problem or improve a condition. Unlike the members in a community of practice who are practitioners (such as software engineers, electrical engineers, surgeons, or nurses) working together on a particular project or task, the members in a community of interest from open and untrusted settings and from a diverse range of demographics — but having same interests in an area. A community of interest could even include members from several different communities, particularly communities of practice, who have the same interests in a broader area (Fischer, 2001; Stanoevska-Slabeva and Schmid, 2001; Begel, et al., 2013). Therefore, a community of interest is often broader than many other communities, suggesting the need to consider a broad range of design features for the platform. Organisations sponsor communities of interest by investing in designing social media platforms that support these communities in the hope of developing greater interactions among users and faster information sharing.

In the context of the real-estate domain, the process of seeking and purchasing property is complex, and the platform should be able to help reduce user uncertainty by supporting users in obtaining, generating and sharing information related to property (e.g., location and pricing), its surrounding (e.g., people living in the neighbourhood) and other environmental factors such as information on commute and crime rate in a suburb. Figure 1 provides a depiction of the socially enabled real-estate platform, which was the subject of our study. The central part of the figure shows a form illustrating property search using a map and features of interest. The different sources of data available through the platform are depicted as overlays with the form.

 

A depiction of the socially enabled real-estate platform that supports property seekers community of interest
 
Figure 1: A depiction of the socially enabled real-estate platform that supports property seekers community of interest.

 

Data concerning these features of interest are drawn from external systems, for example, proprietary specific real-estate systems providing general listing information about a property, government systems providing information about land use and surround spatial datasets, census systems providing demographic information about properties, and school and other community Web applications. The different features of interest can be selected for properties being sold in areas being searched by users.

Community sentiments can be viewed concerning the features of interest (e.g., Facebook forums for neighbourhood groups). The integration of different social media platforms and the relevant forums for properties being viewed are important because users carry different degrees of certainty in regard to the commitment to purchase property, and what aspects of properties and, inherently, lifestyle requirements, they seek. These include surrounding community features whose perceptions of quality are subjective for different people (e.g., proximity to work and other locations, traffic flow, transportation services, demographics, school quality, shopping range and price attractions like incidences of sales, entertainment, and quality of trades such as electricians). As such, users gain insights into their needs and what they should be looking for, based on insights they gain from others. Because of the purpose of the platform, complexity of the problem, the wide range of users involved in the community, and the wide range of salient design features that are needed to support the users, the platform is expected to be a good example of social media platforms that are designed for communities of interest.

The platform also links to partners, such as real-estate agents, banking service providers, and tradespeople, so that trusted agents can be solicited for expert advice and services (e.g., connecting to experts who can help with the process of purchasing a house and connecting to banks that have customer accounts in the surrounding area under display). Various platform capabilities are shown at the bottom of Figure 1 (which were concrete requirements used to ascertain the salient platform features, as discussed in section 4).

 

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3. Methodology

The overall process of data collection, clarifying the research problem, data analysis, designing and evaluation of the platform was conducted in three major phases and took three years. We used discussion sessions and design workshops to collect data from our research participants, and followed the open coding and axial coding steps of grounded theory method (GTM: Strauss and Corbin, 1998) to inductively analyse the data and identify the functional affordances from the user perspective. The two steps empower researchers to be open to unexpected and new findings (Glaser and Strauss, 1967). This process also ensures that “we are not speaking for our participants but rather are enabling them to speak in voices that are clearly understood and representative” [4]. We did not use the third step of GTM (selective coding) because the step aims to support identifying relationships (e.g., cause and effect relationships) between factors (salient design features in the case of our research study). We note that while the qualitative data collection and analysis was carried out iteratively and over an extended period of time, the study is not longitudinal, as we did not aim to study change over time. Also, we integrated our inductive findings with research literature to place the findings within the context of relevant research and to ensure comprehensiveness of our findings.

Moreover, we compared the salient design features we report in the findings with the salient design features of 10 social media platforms [5] that support online communities of interest and are generally known as successful platforms in Australia and/or globally. Three of these platforms are specifically for the purpose of supporting people who are prospectively interested in buying or selling property or simply evaluating existing property. The rest enable users to create their own communities of interest for any topic and at any scale. After exploring the platforms, we did not identify any salient design feature that our findings do not cover. Once the platform was designed and commercialised, we monitored user comments, the majority of which (over 80 percent) showing positive comments and how easily and quickly users started to use the platform in relation to property queries. We focus on the process of our empirical data collection during the design process and how we inductively analysed the data to identify and report the design features.

3.1. Data collection

In the first phase of data collection, we conducted two discussion sessions with property seekers and sellers in a large state of Australia. At this stage, our primary purpose for conducting the discussion sessions was to understand the problem that the platform needs to help with resolving, rather than a detailed process of data collection. We advertised our research study and identified our participants through social media, word of mouth and snowball sampling. Each session with property seekers or sellers included a diverse range of participants in terms of age, gender, financial status, occupation, and experience of seeking for and selling property. The sessions were conducted in an open-ended manner and took one and half hours. The discussion sessions together were conducted over a period of two months. From the discussions, it became clear to us that there is a large group of Australians from a variety of demographics who are seeking a property, usually a house, that suits their circumstances such as financial, family circumstances, community attractive features (e.g., quality of schooling available, retail diversity) and environmental factors (e.g., modes and efficiency of transportation, travel time to work and other points of interest). Seeking a property is complex and people experience a high level of uncertainty. Data about properties and surrounding features of interest are available, e.g., the advertised price, pricing history, land size, land tenure and zoning, dwelling aspects like room sizes and utilities, demographics, crime rates, surrounding schools and shops etc. At the same time, uncertainty exists about the accuracy and quality of the features, e.g., the quality of schools, and at the same time gaps of information exist, such as the experienced quality of schools, managing traffic and local shopping, medical and entertainment experiences.

We realised that there is a need for a social media platform to reduce this uncertainty by helping users obtain, generate and share information related to properties, such as information about location and commute, prices, neighbourhood, and other environmental factors such as information on flooding and crime rate in a suburb. Moreover, we recognised the available data drawn from systems and user generated through social media forums could be combined and integrated into the user property information experience, e.g., integrating online school community forums with the display of surrounding schools. Unlike the current real estate platforms that focus on advertising property listings and rely on small and static sources for supporting property search needs, our platform aims to support users to generate and share information with members in a community of interest, supporting them to identify and select the right property.

In the second phase, we conducted one two-and-half-hour design workshop (equivalent to a more practice-oriented brainstorming session) with a different group of property seekers to identify and clarify what salient design features our IT artifact should include. We followed the same process of participant recruitment that we used in the previous phase. A suitable participant was considered as any person who is interested in directly or potentially seeking and purchasing a property in Australia. Twenty property seekers attended the workshop. Table 1 presents information about participant demographics. In the workshop we provided two scenarios to the participants: one centred around a young couple who are recently married and have limited budget and are more flexible about circumstances such as commute from home to work; and the other centred around a couple who have more or less opposite life circumstances than the first couple, such as less concern about budget and less flexibility about life circumstances including commute time and other life aspects such suitability of the area (e.g., school for children, market and shopping centre, crime rate, and flooding information). Participants were then encouraged to consider their own life circumstances (e.g., marital status, income, and preferences for commute) at the final 45 minutes of the group discussions at the workshop. The insights focussed on what knowledge and insights users would like to gain from, including learning from others. In addition to our note taking during the participant discussions, we voice recorded the session to ensure that no important and relevant point or idea is missed.

 

Table 1: Demographics of the participants in the design workshops.
ParticipantAge bracket
[18–27], [28–37], [38–47], [48–57], [58–67], [68–77]
Gender
Male, Female, or Prefer Not to Say
Employment
Employed, Unemployed, or Student
Experience of seeking for or selling property?
Yes or No
P1[38–47]MaleEmployedYes
P2[38–47]FemaleEmployedYes
P3[18–27]MaleEmployedYes
P4[58–67]FemaleEmployedYes
P5[38–47]MaleEmployedYes
P6[38–47]MaleEmployedYes
P7[18–27]FemaleUnemployedYes
P8[38–47]FemaleEmployedYes
P9[58–67]MaleEmployedYes
P10[18–27]FemaleStudentNo
P11[18–27]FemaleEmployedNo
P12[38–47]MaleEmployedYes
P13[18–27]MaleEmployedYes
P14[58–67]MaleEmployedYes
P15[38–47]FemaleEmployedYes
P16[18–27]FemaleUnemployedYes
P17[38–47]MaleEmployedNo
P18[18–27]FemaleEmployedYes
P19[58–67]MaleUnemployedYes
P20[38–47]FemaleStudentNo
P21[18–27]MaleStudentYes
P22[58–67]FemaleStudentYes
P23[18–27]MaleStudentYes
P24[28–37]FemaleStudentNo
P25[18–27]FemaleEmployedYes
P26[28–37]MaleEmployedYes
P27[18–27]MaleEmployedYes
P28[38–47]MaleEmployedYes
P29[18–27]FemaleUnemployedNo
P30[48–57]MaleUnemployedYes
P31[18–27]FemaleStudentYes
P32[18–27]FemaleStudentYes
P33[38–47]MaleStudentYes
P34[18–27]MaleStudentNo
P35[18–27]FemaleStudentYes
P36[48–57]MaleEmployedYes
P37[18–27]FemaleEmployedNo
P38[38–47]FemaleEmployedYes
P39[18–27]MaleUnemployedYes
P40[58–67]FemaleUnemployedYes

 

In the third phase of data collection, we conducted a second two-and-half-hour workshop with 20 different property seekers (please see Table 1 for information about participant demographics). We followed the same process of participant recruitment that we used in the previous phases. Our purpose of conducting this workshop was to confirm, revise or refine the findings and insights we gained from the previous phase. In this workshop we demonstrated the platform that we had iteratively designed based on the insights we gained from the previous workshop and gave each participant the chance to explore its various design features. Next, participants were asked to use the platform for the same scenarios that we had employed in the previous phase as well as their own life circumstances. We then conducted a short follow-up chat session with each of the two participants who believed that they can provide further details about their viewpoints.

3.2. Data analysis

In order to analyse the data, we reviewed the participants’ comments several times to extract segments where the participants mentioned their thoughts and ideas. These segments were collated into a separate table and coded. The coding process was as follows: once each of the quotes were isolated and collated into a table, we used open coding to code each chunk of text. A first cut code book was generated. Sixty-five codes were identified at this stage. Next, we did some consolidation and data reduction in the coding, carefully comparing text chunks and definitions for similarity. This iterative process of inductive open coding resulted in identifying 20 codes.

We also integrated our inductive coding with research literature to place the findings within the context of relevant research (Glaser, 1965). We identified a number of relevant papers to the topic of our research, particularly boyd and Ellison (2008), Kane, et al. (2014), Glogowska, et al. (2016) and Karahanna, et al. (2018). Where suitable definitions were found from literature that matched our inductive coding, the terms and definitions were adopted for the final coding. In order to ensure the reliability of the coding, the data was coded in three different cycles. Two coders coded the data independently. Disagreements between coders were discussed and resolved in three meetings, achieving 100 percent agreement among the coders (Nili, et al., 2020b) in the third meeting.

Next step was consolidation and categorisation of codes by considering the similarity and differences of their underlying ideas, and further analysed each code and relevant participant comments to determine whether it focused on salient design features. This process required us to revisit and, where necessary, modify the results iteratively (Miles, et al., 2014; Nili, et al., 2017). Throughout, we used note-taking and diagrams to keep track of and refine our ideas (Miles, et al., 2014; Nili, et al., 2017). The final outcome was identifying seven salient design features, which we elaborate on below.

 

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4. Findings

As the user comments show, the extent of capabilities of a social media platform, such as sharing information with connections as well as supporting users to manage their privacy and evaluate quality of information, depend on what salient design features have been embedded in the design of the platform. We elaborate on the seven salient design features that we identified from our research data.

4.1. Design features for managing ties

Our participants were very keen to understand how they can establish and maintain connections with other users of the social media platform. Wasserman and Faust (1994) and Kane, et al. (2014) call these connections ‘ties’. Within these ties, users could be called Contacts, Friends, Followers, Fans, etc. The participants particularly emphasised the importance of the strength of ties that the system supports (i.e., the frequency and depth of interactions between two nodes). Mardsen and Campbell (1984) categorise ties into two major types including strong ties and weak ties. With this regard, [Participant 4 (P4)] who has a marketing and public relation background suggested “the system should have features that can support people in determining the strength of ties and their mutual influence through analysis of interaction data” for social network analysis [6] (e.g., showing messages about who are the most important connections and showing them as priority contacts). “Whether ties need to be verified” (i.e., the notion of tie symmetry; Kane, et al., 2014) was a comment from (P2), who believes that this feature could affect the amount and type of information a user shares (e.g., personal information such as the exact address a person is going to buy a house and live in). For example, Facebook users can maintain both Friends (symmetric) and Fans (asymmetric) ties. All participants had similar comments to the participant’s comment.

Moreover, participants expected that the system should support establishing or strengthening a broad range of relational ties through helping them create and/or join online groups who have a similar goal or interest (see Kane, et al., 2014, for information on the notion of proximity). “I’m not sure what I know or don’t know ... being in a group of people who live in that neighbourhood can help for sure” (P7) was a comment related to this notion. Gaining insights from those who are not strongly connected to users or their groups is beneficial as it can help reduce bias: “knowing whether I should be buying or renting, where I should look and what I should look for definitely benefits from the wider community insights” (P10). However, the quality of ties is an issue for the wider and unknown connections. “Whether people (users) are able to remove a negative (undesirable) contact or connection” (P9) (i.e., the notion of affect on social media; Kane, et al., 2014; Resor, et al., 2021) was another important aspect of the platform’s tie related features. For example, users of Twitter can block certain followers, and users of Facebook can ignore undesirable connections and hide information from a group of users.

All participants were concerned about whether there will be a large enough number of people who use the system, enabling them to establish helpful connections with them. Designers of social media platforms can determine the number of ties that can be supported by the platform (unlimited or many such as the number of followers on Twitter or very few connections such as platforms for small group project collaboration). From all participants’ comments it was clear to us that features of social media that support users in establishing and managing their ties are directly related to establishing and maintaining their relationships with enough number of people (critical mass on social media; Rauniar, et al., 2014) that they need to interact with. For example, a participant stated “I’m concerned about my future neighbours when I purchase a house. If these are available [participant pointing out to other members discussing the features above] I can then continue talking to them or block them, I think there are going to be enough people that I can get helpful information from” (P18). More generally, the propensity for relational capital was evident from user interactions across strong and weak ties.

In comparison to existing social media platforms, priorities can be made for recommendations for connections presented to users when performing certain tasks. For example, in property search and discovery tasks, users having shared contexts with relevant properties and surrounding features can be listed for recommended connections. Another example, user connections exhibiting strong ties in relation to properties and surrounding features could be recommended when specific actions are sought, such as verifying prices for properties.

4.2. Design features for managing privacy

The majority of participants completely agreed with P14 who mentioned that privacy concerns are often about “abuse of personal and financial information by other users or people who manage the [social media] platform”. The participants mentioned that the platform should include various design features supporting privacy settings to reduce their concerns related to privacy and to build user trust, including features such as: “who can see my stuff?” (P21) for posts, “strict message filters” (P22), “limit old posts” option (P27), “blocking a particular follower or user and whether anyone or an approved list of users can see all of the user’s posts” (P32), and “who can see my activities” (P38). The level of user self-disclosure is related to the user’s expectations of support from others and privacy concerns related to abuse of personal information (Trepte, et al., 2020) by other users, the business who directly manages the platform, and third parties. To fully and freely participate in social-media related activities, a user should feel free from worries related to breach of information privacy (Kietzmann, et al., 2011; Kane, et al., 2014; Cirucci, 2015; Trepte, et al., 2020). For example, most participants mentioned comments such as “certainly, the security features that the platform provides can help me manage my privacy. Such a platform, with these features, is secure for these days that privacy of information is becoming increasingly important” (P26). In comparison to existing social media platforms, privacy preserving constraints that are supported could consider property contexts. For example, when displaying a property, one supported design subcapabilty is to list the queries that others have asked about a property or surrounding feature. However, users can be given the option to have specific searches blocked from public viewing, to avoid privacy compromises.

4.3. Design features for managing digital profile

Twelve users were interested to know how their profile will look like and what features the system provide them with this regard (e.g., “how can I or others be identified? Should be [through] our profile pages?” (P8)). Digital profiles can represent several types of users, including an individual user, an organisation, a group (e.g., a department within an organisation or a small group of individuals who have similar interests and goal), and a role (e.g., manager, administrator, and community moderator) (Kane, et al., 2014). A digital profile also organises user information in a single place that is accessible by others. Digital profiles generally allow users to contribute content in different formats and ways such as text, multimedia (including pictures, audio, and video), and hypermedia such as tags and links to Web pages (Kane, et al., 2014; Couldry, et al., 2016). The majority of participants mentioned comments such as “it has features that users can manage their profiles. It connects people’s profiles to who they are. I can know who is who here. Makes me feel I am willing to share information with them” (P33). The information on a user’s profile (produced and shared by the user or by other users) can also contribute to other users’ perception of the identity of that user. The extent to which a user believes that the design features of a user profile represents an actual off-line identity can contribute to the user’s perception of network transparency (Kane, et al., 2014). Given the consumer services context of the platform, active profiles of service preferences and service history were also identified as being useful for streamlined property searching, auto-filling of form fields, linking to relevant groups for different areas and properties.

4.4. Design features for network structure

Several participants also asked us questions like “are there features that can help me know that I and others are connected to which ones (users)?” (P12). People are often poor at identifying the structure of their own and others’ personal networks (Krackhardt and Kilduff, 1999; Marineau, et al., 2013; Kane, et al., 2014). Many social media platforms can help users identify such a network structure by providing features that show direct connections (e.g., key metrics such as the number of ties and mutual friends; Kane, et al., 2014). Features that support a transparent (i.e., clear and easy to understand) visualisation of connections in their own and others’ networks can improve people’s capabilities for visualising their networks which may not be present in their traditional social networks (Kane, et al., 2014). For example, users may use this information to connect with an influential member and may also use the information to avoid establishing connections which are perceived as socially undesirable or negative. Moreover, social media’s computer-aided networking features can use this information to recommend connections with another user or group of users (e.g., recommendations such as “people you may know” on LinkedIn) (Kane, et al., 2014). Some participants provided comments similar to this comment: “If there are features that enable me see the people I’m connected to, how many they (contacts) are, and anything — anything that gives me a clear picture about my contacts or others’ contacts can help me understand my network” (P3). All other participants agreed with the idea. Once again the spatial contexts of properties and surrounding contexts can be used to contextualise network connections, e.g., people connected not only because they have interacted together because of queries about a school or other community group, but also because they have relational ties in regards to the wider context, e.g., they are parents of students in a school, or they are members of a neighbourhood group.

4.5. Design features related to service structure

Most participants highlighted the fact that a more enriched information experience for property search and decision-making required complementary sources of data, drawn from available systems via (application programming interface: API) services as well as user sentiments from online communities. However, they reported that the information needed to be carefully coalesced to provide coherent insights. “It’s important to combine information from systems as well as the community in an effective way as property involves a whole range of aspects that need to be considered. With social media platforms like Facebook and Twitter, you can lose sight of the important data that’s obtained from existing systems. With use of existing systems, you don’t have the connection to the opinions of others. For property, you need both” (P11). Participants identified that services providing different data about property and related features could be integrated with available social media posts. A compelling example highlighted this was: “I’d love to know which electricians, plumbers and other trades folk around the property have used, and be able to view their profiles. I’d do that by talking to people that I know in an area. Why not being able to do it online?” (P1). Such a requirement can be supported by correlating the property’s location with that of other properties, property to trade use, trade to social media forums, and trade to trade platform profiles and ratings. Moreover, having a pan/zoom overlay of services related to properties, providing data for property features which are correlated to community forums provides a more coherent information experience for various navigational and detailed views of properties. In effect, the service structure of the platforms are socially enabled. This form of integration can lead to new innovations for user search and decision-making tasks, leveraging crowd-sourcing (Ziaimatin, et al., 2020; Makasi, et al., 2020). For example, based on user voting through data viewed from property profiles, the community could rank the ’best property finds’ in different areas, or property pricing could be elicited through the community. This was highlighted through one participant’s response: “Having the connection to online communities, you could ask what the community would be prepared to pay for the property. Knowing the right price to sell or buy is the Achilles heel of property” (P15).

4.6. Design features for evaluating information quality

From the focus groups data it became clear to us that from the user’s perspective, the information received from other users may vary in terms of trustworthiness (i.e., reliability), relevancy of the response to the user’s enquiry, accuracy (how exact and correct the information is) and completeness (comprehensiveness). For example, a user stated: “so, how can we know which comment is right and which one is wrong? which one is exact? ... it (the system) should help recognising these sort of stuff” (P6). Moreover, from the comments such as “many people provide too much information ... There should be something (features) that enable people to assess it (assess quality of information)” (P17), we realised that receiving information from multiple users may cause information overload for the users, which can make evaluation of the quality (conceptualised as fitness for use) of information even harder for them.

We identified three major types of design features that the system should have to support users in evaluating quality of information, including: features that enable users to give ‘feedback’ (e.g., in the form of a comment, like or dislike) on one or more specific aspects of information; mechanisms (e.g., number of shares or recommendations) that show the ‘reputation’ of information users produce or share; and features that enable users to know other users’ ‘reactions’ to the content (e.g., ‘like’ or ‘dislike’, ‘agree’ or ‘disagree’, and emotions such as happy, sad or angry about a comment) [7]. All participants strongly agreed with the idea from a participant who expressed “features that support evaluating quality information make a great difference. You can distinguish and benefit from good and useful information among the huge amount of information” (P30). Literature (e.g., Glogowska, et al., 2016; Nili, et al., 2019) also suggests that these features contribute to the user’s evaluation of the overall quality (fitness for use) of information.

4.7. Design features related to visual appeal

Several participants pointed out that visual appeal of the platform is also important for them. For example, one participant stated: “for me honestly it is important that there should be features that are visually attractive” (P24). The participants particularly mentioned that it would be interesting if the platform allows a pictorial way (e.g., emoji) of expressing one’s feelings about others’ comments or posts. A participant also mentioned “these features can help me reduce my anxiety because searching for a property can be a lengthy, serious and tiring process” (P36). Other workshop participants agreed with his comment and had similar opinions. Participants were also commenting on the importance of these features (features that support visual appeal of the platform) and its relationship with enjoying using the platform (e.g., “I think it is fun also. Yes, I’d like to use your system (the platform)” (P39)). Including such features in a social media platform has also been mentioned in management and business information systems literature (Stark and Crawford, 2015).

 

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5. Discussion

Salient design features of social media platforms that are dedicated to communities of interest enable capabilities of the platforms with regards to connecting users to each other, producing and sharing content, service structure, enabling users to manage their digital profile, ties, network structure, privacy, and self-evaluation of quality of the shared content. By identifying and explaining these design features, our research contributes to the ongoing discourse in the information systems field about the importance of identifying and articulating the salient design features of an information technology that is useful for both theory and practice and have not been covered by theories of user behaviour (Niederman, et al., 2009; Tate and Evermann, 2009; Grover and Lyytinen, 2015; Tate, et al., 2015). Information systems researchers such as Tate and Evermann (2009), Tate, et al. (2015), and Nili, et al. (2020a) specifically argue that identifying and articulating salient design features of an IT is important because they contribute to forming user beliefs (e.g. perceived usefulness and utility) and intention to use the IT, which are antecedents of a user behaviour, particularly adoption of the technology.

We shed light on salient design features of social media platforms for consumer services which support users to elicit knowledge from their community of interest, for known and targeted needs to needs which are less known and speculative. Such design features are particularly relevant and important, as gaining a more enriched information experience for information search and decision-making requires complementary sources of data such as user sentiments, local businesses’ Web sites, and available systems via API. For example, we identified and explained three major types of design features that a social media platform needs to have to support users in evaluating quality of information that they gain from various sources such as weak ties, strong ties, and local businesses’ Web sites in a community of interest. As another example, we explained that the features related to the service structure can make the platforms socially enabled (e.g., based on user voting through data viewed from property profiles, the community could rank the ‘best property finds’ in different areas, or property pricing could be elicited through the community).

We explained that among the numerous studies of social media, there are only a few studies that are relevant to the topic of our study in general, and explained that even these few studies have mostly focused on high-level affordances of social media (e.g., self-presentation, information sharing, socialisation, collaboration, interactivity, visibility, editability, persistence, and association). Some of these studies have also provided some examples of very low-level features (e.g., very specific features such as liking, listening to music, uploading songs or video, and tagging microblogs). We conducted an inductive empirical research through brainstorming sessions and design workshops to identify and articulate salient design features. Identifying and articulating the salient design features is encouraged in information systems research, as they are not very low-level features (e.g., specific settings, security questions, and tabs) that do not provide rich insights that is useful for wide audience and are not at a very high level of abstraction that seldom contribute to scholarly research. Future researchers may be interested in identifying and providing details related to various different low-level features for the salient design features (e.g., managing privacy) that we identified and explained in this work.

We do not claim generalisability of our findings (which focus on communities of interest) for all social media platforms that support different types of communities. However, we argue: first, because of the complexity of the problem, the wide range of salient design features that we identified, and the wide range of users involved in the community, our findings are applicable to a wide range of social media platforms that support communities of interest, even if some details could be different. When doing research and designing a platform that supports a different type of community, such as community of practice, researchers and practitioners may identify many similarities between the design features we articulated and the design features that need to consider for their platform. For example, a study on community of practice may result in identifying a less or more emphasis on the importance of features for managing ties. For a community of interest, both strong ties and weak ties are important to receive information from as many people as possible (a high critical mass in a user’s network is important), but strong ties may be much more important than weak ties in a community of practice where people tend to work with selected and fewer people. Therefore, verifying and selecting the most relevant users (symmetry) is less important for a community of interest, but they could be highly important for a community of practice. Because of the same reason, users in a community of practice may be less concerned about sharing personal information (e.g., about their work and prior personal experiences) and therefore strict design features for managing their own privacy (e.g., “who can see my stuff?” for posts, strict message filters, limiting old posts, and blocking users) may be less important for them. This also means that design features that support evaluation of quality of information (e.g., to support identifying relevant and valid information among huge amount of information shared by many users) may be more important for users in a community of interest than for the users in a community of practice. Finally, because there are selected (or fewer people) in a community of practice, we could also argue that design features that support users to view their network in a transparent way are less important, compared with their importance for a community of interest. We encourage future research on examining our findings for social media platforms that support other types of online communities.

Many influential theories and studies in social psychology (e.g., Cook, 1977; Narayan and Cassidy, 2001), economics, communications (e.g., Lin, 1999), and diffusion of innovations (e.g., Rogers, 1995; Agarwal and Prasad, 1998) support the notion that perceived critical mass (in this case, number of users on a social media platform) is an important factor for user acceptance of a new technology (Lou, et al., 2000; Rauniar, et al., 2014). Particularly, several studies in economics and communication fields (e.g., Domingos and Richardson, 2001; Cova and Salle, 2008) have supported the positive relationships between the number of people in a person’s network, overall number of users on the platform and the value of the network. Embedding the salient design features that we identified can increase critical mass on a platform. For example, considering the conceptualisation of information quality as fitness for use of information (Ge and Helfert, 2007; Glogowska, et al., 2016) and if the social media platform includes the features that support user’s perceived quality of information (discussed above), it is expected that user’s perceived quality of information on a social media platform has a direct impact on their perceived usefulness of the social media platform. As another example, stronger privacy settings help building user trust to the extent that is necessary to attract new users and encourage them to fully participate in social-media related activities (Benlian and Hess, 2011; Wang, et al., 2016). Future research can focus on how these design features can contribute to user perceptions about the platform and how those perceptions can contribute to a user behaviour such as adoption or effective use of the platform.

 

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6. Conclusion

“The ‘find us, friend us, and follow us’ slogan on milk containers is a suitable example for how ‘bought’ media (e.g., advertising) and ‘owned’ media (e.g., the brand, [service] or the product itself) can be integrated with social media (the ‘earned’ media) to seed and drive conversations, sharing, relationships, and so forth” [8]. We identified and articulated the salient design features of social media platforms for online communities of interest. We clarified that these design features are design-related enablers for helping users to connect to each other, self-manage their digital profile, ties, network structure, service structure, privacy and to help them evaluate quality of the shared content. We also briefly discussed how our findings can be useful for designing social media platforms that support other types of online communities such as online communities of practice. We discussed our contribution to theory and practice, our research limitations and suggestions for future research. We believe that our research findings are particularly useful for researchers and practitioners such as designers and business analysts who aim to support tasks of searching and decision-making in which users elicit knowledge from their community, for their known and targeted needs to needs which are less known and speculative. End of article

 

About the authors

Dr. Alireza Nili is a Lecturer in Service Science at the School of Information Systems at Queensland University of Technology (QUT) in Australia. He specialises in both behavioural information systems (customer decision making and use behaviour) and design information systems research and uses both qualitative and quantitative methods in his work. His research interests primarily focus on the design and evaluation of emerging Internet-based technologies for service co-creation. He has published in journals such as International Journal of Information Management, MIT Sloan Management Review, IEEE Software, First Monday, Communications of the ACM, Public Performance & Management Review, Communications of the Association for Information Systems, and Electronic Commerce Research. He has served roles such as Associate Editor at the International Conference on Information Systems (an A* conference) and European Conference on Information Systems and Track Chair at the Australasian Conference on Information Systems.
ORCID: https://orcid.org/0000-0003-1183-7626
Direct comments to: a [dot] nili [at] qut [dot] edu [dot] au

Prof. Alistair Barros is a Professor of Information Systems, Head of Service Science Group at the School of Information Systems at Queensland University of Technology. He has 33 years of ICT experience across industry, academic, industrial R&D and industry roles, including Global Research Leader and Chief Development Architect at SAP AG, the third largest software company worldwide. His focus is on the design, engineering and evolution of enterprise systems platforms in contemporary cyber-physical settings, supported by Cloud, Internet-of-Things and Blockchain infrastructure. Alistair has published more than 118 articles, which include six edited books, and 88 peer-reviewed journals, conference and book chapter articles. He also has 18 filed U.S. patents.
E-mail: alistair [dot] barros [at] qut [dot] edu [dot] au

 

Acknowledgements

We highly appreciate the support we received from our research participants for participating in the data collection phase. Also, we highly appreciate the help we received from Dr. Nick Russell with regards to the data collection phase, particularly facilitation of the workshops.

 

Statement of interest

We have no known conflict of interest to disclose.

 

Funding

This research study was funded by the ARC Linkage Project LP140101062 Transforming Banking Service Delivery Through Connected Communities.

 

Notes

1. Tate and Evermann, 2009, p. 1.

2. Strong, et al., 2014, p. 69.

3. Hartson 2003, p. 315.

4. Strauss and Corbin, 1998, p. 56.

5. We conducted this search for the purpose of ensuring reliable coverage of our findings (i.e., not for an exploratory or comparative study). Because the search is not a main phase of our research study, in the rest of our paper we focus on our empirical research study.

6. Studying the concept of tie strength and similar concepts (e.g., affect) requires a deep study that is out of the scope and purpose of this paper. With regard to the strength of ties, we refer interested readers to original articles (e.g., Friedkin, 1982; Lin, et al., 1981; Riger and Lavrakas, 1981; Granovetter, 1983; Keramati and Nili, 2011; Heranz, 2010) and the articles which have particularly studied the notion of tie strengths.

7. In addition to these mechanisms that need to be used by members, platform owners can use other mechanisms such as vandal-fighting tools that protect content and embed them in software algorithms in order to automate a part of the process of ensuring content validity (Spagnoletti, et al., 2015). However, these mechanisms are often useful for sites that are less about fragmented information from various members. For a platform that supports a community of interest, such as the platform that we have designed, the four above features are still highly important to support a user in evaluating fitness for use of information.

8. Kietzmann, 2011, p. 249.

 

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Appendix

Scenario 1: First-home ownership for young couple

Sue and Ian are in their late 20s and have been living at the Gold Coast where they both work and live in a rental apartment. Ian works as an early career mechanical engineer for a small partner firm which provides services to a larger engineering responsible for domestic and large overseas contracts. Sue has her own sole trader business, working as Book Keeper mostly for tradespeople and small businesses.

Sue and Ian have been in a stable relationship and have made the decision to strengthen their commitment to each other, by getting married and having a child, all things being equal, within the next 18 months. They love the Gold Coast but for reasons of work opportunities and options for future schooling, they have made the decision to Brisbane, ideally located in the south side, within reasonable travel time to Brisbane city but also allowing them to travel to Gold Coast for visits and summer holidays.

Since they’ve been together, Sue and Ian have lived a flexible but not extravagant lifestyle and have accumulated savings. They’ve spent on what they generally, have travelled overseas, both have cars and have tended to buy things beyond necessities and basic leisure.

Now their attention has shifted to home ownership given their desire for a permanent location, especially with a view for having a child and potentially a further child down the track.

Brisbane’s property has progressively increased to the extent that affordability is a major factor for Sue and Ian. However, the couple also recognizes that deferring the decision to buy will soon remove their dream of having a home and building up the lifestyle stability and financial equity that it will bring.

Sue and Ian have limited knowledge of where to look in Brisbane, apart from generally targeting its south side suburbs. They are looking for property that has convenient access to shops, schools and medical and other services, and does not have traffic bottlenecks to get to work and other areas. Ian will have to find a new job and expects to be based in Brisbane city. Sue will be based at home and will travel to clients for a new base which she will have to grow.

The couple wants the security of having multiple options for public transport, being a stable and attractive area, which is lower in price but has a strong prospect for capital gain. They have reasoned that buying into a cheaper area where more middle-class folk are moving, buying on a hill with great and open views and having a decent sized home and block of land, which could be extended over the next five years and have a swimming pool when their debt is manageable. Ian and Sue expect to do the home improvements as they will not be able to afford trades services.

Conceptual design thinking tasks:

Scenario 2: Home ownership and an established life

Consider a couple who have more or less opposite life circumstances than Sue’s and Ian’s circumstances, such as less concern about budget and less flexibility about life circumstances including commute time and other life aspects such suitability of the area for their family (for example, school for their children, market and shopping centre, crime rate in a suburb they are interested in, flooding and flights/airplanes noise). The couple wants a more ideal house in a more ideal suburb and is not much worried about prices.

Please complete the same tasks considering this scenario.

 


Editorial history

Received August 2021; revised 25 January 2022; accepted 4 March 2022.


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This paper is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Salient design features that your social media platform needs: The case of online communities of interest
by Alireza Nili and Alistair Barros.
First Monday, Volume 27, Number 3 - 7 March 2022
https://journals.uic.edu/ojs/index.php/fm/article/download/11831/10615
doi: https://dx.doi.org/10.5210/fm.v27i3.11831