Insights into the adoption of social media mashups

Internet Research Insights into the adoption of social media mashups He Wu Zha Shenghua

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Insights into the adoption of social media mashups

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Department of Information Technology & Decision Sciences, Old Dominion University, Norfolk, Virginia, USA, and

Wu He Received 30 January 2013 Revised 2 May 2013 Accepted 8 May 2013

Shenghua Zha Center for Instructional Technology, James Madison University, Harrisonburg, Virginia, USA


Abstract Purpose – The existing mashup literature paid little attention to the actual adoption and diffusion of mashups in an organizational context. As more and more organizations are engaged in mashup initiatives, more research efforts focussing on the mashup use and adoption issues from the organizational perspective are needed to ensure that organizations can receive the maximum benefits from their mashup initiatives. Two studies are conducted to increase the understanding of the use and adoption issues with social media mashups. The paper aims at discussing these issues. Design/methodology/approach – The paper first used a text mining approach to analyze relevant posts on blogs and messages in a major online mashup forum in order to understand the current status of social media mashup as well as representative themes and issues with social media mashups in general. Subsequently, the paper reviewed a number of social media mashup sites created by higher education institutions (HEIs) in the USA. Findings – The paper identified some representative themes and issues with social media mashups in general. The paper also identified the approaches that were used to design the interface of social media mashup sites by HEIs. Based on the two studies, this paper provides recommendations and insights to guide social media mashup development and adoption in an organizational context. Originality value – This is the first article to discuss the use and adoption of social media mashups in organizational environments. This paper can be used as a starting point to motivate other researchers to further explore the diffusion of social media mashups in different industries. This paper also helps organizations improve their social media mashup initiatives. Keywords Twitter, Facebook, YouTube, Community engagement, Social media mashup, Higher education institutions, Organizations Paper type Research paper

Internet Research Vol. 24 No. 2, 2014 pp. 160-180 r Emerald Group Publishing Limited 1066-2243 DOI 10.1108/IntR-01-2013-0017

1. Introduction The era of social technologies provides a lot of opportunities for organizations (Merchant, 2012). According to Merchant (2012), social era is about “connecting things, people, and ideas” and the social era will reward those organizations that co-create values with employees, partners and consumers. Thus, social media are being increasingly adopted by organizations. Recently, social media mashups have received a lot of attention. A number of organizations have created social media mashups to combine content, presentation or application functionality from various social media sites or accounts. In particular, governments and large organizations such as higher education institutions (HEIs) often have numerous social media accounts which serve different purposes and user groups. To facilitate the management of social media sites and build a strong social media community, many organizations are very interested in adopting social media mashup solutions. The term mashup implies “easy, fast integration, frequently made possible by access to open APIs and data


sources to produce results beyond the predictions of the data owners” (de Vrieze et al., 2010; Bader et al., 2012). A social media mashup is a special type of mashup application that relies on various open APIs and feeds to combine publicly available content from different social media sites to create valuable information and build useful new applications or services. So far most mashup-related studies focus on technical aspect such as the architecture, information integration (Wu et al., 2009; He and Xu, 2013; Li et al., 2012) and underlining technologies (e.g. really simple syndication (RSS)/atom feeds, web services, screen scraping). For example, Fung et al. (2012) describe how a service-oriented architecture and a data mashup algorithm are used to address various integration challenges such as privacy and sensitive information leaking concerns. Chudnovskyy et al. (2012) provide a reference architecture to integrate telco services into enterprise mashup applications and present a systematical approach for the system integration process. Chen and Peng (2012) propose a QoS-aware services mashup model to solve the service routing problem for cloud-based applications with a consideration of the applications’ resource requirements and constraints. Hummer et al. (2010) developed a dependency graph and used a step-bystep debugging technique to support mashup development and maintenance. Meza and Zhu (2008) combined the content from two sets of data sources to organize and display technical reports. Belleau et al. (2008) used public bioinformatics databases to build a data mashup for on-demand bioinformatics knowledge. Maximilien et al. (2008) propose an online service platform to facilitate mashup design and development. However, the existing mashup literature paid little attention to the actual adoption and diffusion of web mashups in an organizational context. As more and more organizations are engaged in mashup initiatives, more research efforts focussing on the mashup use and adoption issues from the organizational perspective are needed to ensure that organizations can receive the maximum benefits from their mashup initiatives. Many organizations have multiple mashup developers and often develop or adopt more than one type of mashup to support their business needs or processes (Bader et al., 2012; Ulmer et al., 2013). This paper mainly examines social media mashups which are different from traditional data mashups used within organizations. In general, an organization’s data mashups often contain sensitive or private enterprise data and are connected to at least an internal data source such as an enterprise database. Social media mashups mainly focus on combining publicly available data from social media sites/ accounts. To the best of our knowledge, currently there are no published research articles discussing the use and adoption issues with social media mashups from an organizational perspective. In an effort to fill this void and understand the use and adoption issues with social media mashups, we conducted two studies. The first study was to conduct a text mining analysis of relevant blog posts and forum messages in order to understand the current development with social media mashups in general. As a result, we identified some representative themes and issues associated with social media mashups. Subsequently, we conducted a more specific study to explore the adoption of social media mashups by HEIs. We reviewed a number of social media mashup sites created by higher education institutions in the USA and introduced the approaches they used to design the interface of their social media mashup sites. The reason that we selected HEIs is twofold: first, many HEIs have a large number of social media accounts/sites that are created and maintained by various departments, administrative units and student organizations; and second, many HEIs have a large social media audience. The rest of this paper is organized as follows. Section 2 provides a brief review of mashups and text mining. Section 3 describes our research questions and two separate

Social media mashups

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studies to answer the research questions. Section 4 provides recommendations and insights for the development, use and adoption of social media mashup in organizations. Section 5 presents conclusion and future research. 2. Literature review 2.1 A brief review of mashups In general, a mashup is a web site or web application that combines internal and external data sources (Beemer and Gregg, 2009; Benslimane et al., 2008). A combination of data from different data sources can generate interesting web applications or new web services (Li and Liu, 2012). For example, Archambault et al. (2011) suggest that Google maps and a list of top-ranked high schools can be combined to build a map with top-ranked high schools features directly on the map; Lee et al. (2009) indicate that a market share mashup report can be created by integrating “an external list of all houses sold in the last week with internal data about which houses one agency sold.” Mashups can be created using conventional web programming technologies, dedicated mashup tools such as IBM mashup tools and Yahoo pipes (Daniel et al., 2011;Yu et al., 2008), or dashboard platforms such as Netvibes. Mashup tools significantly lower the barriers to mashup development and allow novice web users to easily build mashup applications from various data sources (Cappiello et al., 2010). A mashup architecture involves three different participants: content providers, mashup site and the web browser used by the clients (Lee et al., 2009). Content providers play a critical role in the mashup architecture and determine what services could be offered. There are many types of mashup services (Lee et al., 2009; Archambault et al., 2011; Niu et al., 2013a, b) such as maps (e.g. Google Map), videos (e.g. YouTube), social media (e.g. Blog, Facebook, Twitter, Mypsace, Linkedin), photos (e.g. Flickr) and online payment (e.g. paypal). These different mashup services can be seamlessly and flexibly integrated using APIs to meet specific needs. Currently, there are thousands of open APIs available on the web. Many companies such as Google, Yahoo, Microsoft, Amazon and ebay have published APIs based on web standards and made them available for end users to develop various mashups (Lee et al., 2009; Kulathuramaiyer and Maurer, 2007). Popular APIs for mashups include Google Maps, Flickr, YouTube, Amazon, Twitter, eBay, VirtualEarth, del.icio.us, Google, YahooMaps and so on (Lee et al., 2009). The famous mashup repository web site programmableweb lists more than 8,000 APIs and 6,800 mashups. These APIs are developed using different programming languages such as PHP, Python, .Net, Ruby, Java, JavaScript, etc. In particularly, currently there are over 900 social APIs listed on programmableweb. In general, mashups offer many benefits such as bringing disparate applications together, enhancing existing data source with extra information, improving the usability of existing applications and making a web site or service more informative, engaging, entertaining and dynamic (Hoyer et al., 2011; Zang and Rosson, 2008). For example, mashups can gather all relevant information and display them on one portal in a meaningful way (Li et al., 2008) and thus improve knowledge sharing, dissemination and collaboration (Belleau et al., 2008). The automatic importing of updates from various social media sites into one mashup site eliminates the need to converse and share information with people in different locations and can save people a lot of time and efforts to check updates. On the other hand, individual social media sites such as blogs or twitters can also benefit from a social media mashup which often attracts and brings many new users. In recent years many organizations have adopted mashups to expand and enrich business services. Bader et al. (2012) suggest two additional benefits


of mashups when they are used in organizational environments: flexibility and productivity. Mashups offer strong flexibility because data sources can be easily added or removed by using published programmable APIs, open web services or RSS/ATOM feeds; the development of mashup tools also improves productivity by enabling end users without strong programming skills to create their own mashups. The use of open APIs, web services and RSS feeds reduces the time and efforts for application development. Mashups can be classified into different types by the type of media employed (Moran, 2008) such as mapping mashups, video and photo mashups, search and shopping mashups, news mashups and social media mashups. Mashups can also be classified into different types by patterns such as “data source mashups, process mashups, consumer mashups, enterprise mashups, client-side mashup, server-side mashups and developer assembly mashups” (Lee et al., 2009). As far as this paper is concerned, social media mashups are more likely to be considered as data source mashups and server-side mashups. To build social media mashups, one needs to combine two or more social media data sources such as Blog, Facebook, MySpace, Twitter, YouTube, Flickr and Google Plus. However, many social media mashups also integrate other data sources such as local files (e.g. text files, spreadsheets, XML files and PDF files), content management systems, databases, web sites, web services and RSS feeds (Hachani et al., 2013; Mietzner et al., 2011; Viriyasitavat et al., 2012; Xu et al., 2009). These different data sources, APIs, feeds and web services are the building blocks for mashup development (Kulathuramaiyer and Maurer, 2007). In addition, external data sources are influenced by the emerging technology trends including big data (Chen et al., 2012), business intelligence (Duan and Xu, 2012), cloud computing (Wang et al., 2012) and smart city infrastructure (Naphade et al., 2011). These emerging technologies will generate large collections of complex data residing in multiple and heterogeneous platforms (Guo et al., 2012a, b). Future mashup development needs to incorporate these external data sources and consider how the pace and flow of these external data impact the quality of mashups. There are a very limited amount of studies related to the use of mashup in organizations. Giessmann et al. (2011) found that perceived usefulness strongly affected the attitude toward using enterprise mashups. de Vrieze et al. (2011) point out that developing a mashup is more than just selecting different sources of data. IT governance has been recognized as an issue with mashup development in enterprises (de Vrieze et al., 2011). Bader et al. (2012) propose a mashup readiness assessment framework to help enterprise managers and decision makers determine the needs and readiness for mashups in an organizational context. However, these studies did not specifically look into the use of social media mashups in organizations. 2.2 A brief review of text mining As an emerging technology, text mining aims to extract meaningful information from unstructured textual data (Hung and Zhang, 2008; Liu et al., 2011). To glean useful information from a large number of textual documents quickly, it has become imperative to use automated computer techniques (Liu et al., 2011; Chiang et al., 2011; He, 2013a). Text mining is focussed on finding useful models, trends, patterns or rules from unstructured textual data (Hung and Zhang, 2008; Romero et al., 2008; Lin et al., 2009; Abdous and He, 2011; Kaiser and Bodendorf, 2012). Different from traditional content analysis, the main purpose of text mining is to automatically identify useful patterns or trends hidden in the text documents (He, 2013b; Zhong et al., 2012).


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Text mining techniques have been used to analyze large amounts of textual data. Abdous and He (2011) used text mining techniques to analyze the online questions posted by video streaming students and identified a number of learning patterns and technology-related issues. Fuller et al. (2011) used text mining to detect deception and lies in real world data. Their results show that automated text mining techniques have the potential to aid those who must try to detect lies in text. Hung (2012) used clustering analysis as an exploratory technique to examine e-learning literature and visualized patterns by grouping sources that share similar words, attribute values and coding rules. Percha et al. (2012) used text mining to capture the semantics of individual gene-drug relationships and uncover new drug-drug interactions. Various text mining techniques have been used to extract valuable knowledge from blog posts too. Chau and Xu (2007) applied text mining to analyze the blog content related to hate groups and identified some interesting demographical and topological characteristics in these groups. Later, Chau and Xu (2012) proposed a framework for gathering business intelligence from blogs and applied text mining and content analysis approaches to extract and analyze blog contents related to Apple’s iPod music player. Abbasi et al. (2008) used text mining techniques to identified opinions and sentiments expressed in web forums, blogs and online stories. Some major applications of text mining include: clustering (Duan et al., 2007), information extraction (text summarization) and link analysis (Hung, 2012; He et al., 2012; Zhong et al., 2012). Currently, there are a wide range of tools that can be used for text mining and analysis, such as the SPSS Modeler (formerly Clementine), Leximancer, SAS Enterprise Miner and NVivo. Due to the powerful capabilities of text mining, it is believed that applying text mining to textual data including messages posted on social media such as blogs and online forums can yield interesting findings (Pang and Lee, 2008; Abdous et al., 2012; He, 2013b; Barbier and Liu, 2011; Chau and Xu, 2012). 3. Research studies 3.1 Research questions We conducted two separate studies to answer the following research questions: RQ1. What patterns, themes or issues can be found from blog posts and online forum messages about social media mashup? RQ2. How are social media mashups being used by HEIs? In the following two sections, the first study answers the first research question. The second study is conducted to answer the second research question. 3.2 Study 1: mining blog posts and online messages related to social media mashups Social media mashups are a quite new phenomenon and currently there are no published journal articles about social media mashup in the literature. In an effort to answer the first research question and understand current status of social media mashups in general, we decided to conduct a text mining analysis of relevant blog posts and online forum messages first. When text mining techniques are used to analyze blog posts, sometimes it is called blog mining (Rubin et al., 2011). Blog posts are a valuable information source for conducting internet-related research (Chau and Xu, 2012) because blogs are freely and publicly available online, and contents were created by self-motivated bloggers independently of the study (Rubin et al., 2011).

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First, we conducted query search using the advanced search option of Google Blog Search (http://www.google.com/blogsearch) with the keywords “social media mashups.” Google Blog Search is specially designed to retrieve content from blogs that are freely and publicly available on the internet. After the query search, Google returned a large number of blog posts that are created by numerous internet bloggers. To identify the latest development in social media mashup, we limited the query results to blog posts that were posted between January 1, 2009 and December 20, 2012. As a result, Google showed a message that more than 5,530 results were found in 0.09 seconds. However, a step-by-step examination of the result pages found that Google actually displayed only 119 blog posts related to social media mashup and automatically filtered other blogs posts which were considered to very similar to the first 119 blog posts. Figure 1 displays the number of social media mashup-related blog posts by year from 2009 to 2012(as of December 20, 2012). The figure indicates that social media mashup is receiving growing attention.

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Second, to further increase our coverage for social media mashups, we also examined the messages posted in the online forum (Kaiser and Bodendorf, 2012) of the largest web mashup repository web site programmableweb. As of December 20, 2012, there were 2,392 messages available in the online forum of


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Frequency


As social media mashups is such a new area, relevant discussions on social media mashups are often posted by technology consultants and experts on blogs first before they appear in academic publications. For example, many blog posts discuss specific solutions to some technical issues and problems in creating mashups. Thus, those blogs are a very useful data source for learning about the latest social media mashup development, trends and existing issues. A limitation with blog mining is that the information on blogs is not peer reviewed as journal publications and often represents personal opinions and attitudes. Thus, researchers need to be careful of the bias on blog posts when they analyze the content of blog posts. As a result of the mining, we identified some representative themes and issues associated with social media mashups. Below is a description of the procedures and findings:

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2010

2011 Year

2012

Figure 1. The number of blog posts for social media mashup by year

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programmableweb. These messages were organized into four sub-forums. However, the majority of these messages (91 percent) were API-specific discussions. Only 212 messages in two separate sub-forums were about the use, adoption and diffusion aspects of web mashup. To further enrich our findings, we also included these 212 messages into our sample set for text mining and analysis. .


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Third, we manually copied the textual content of the selected blog posts and online forum messages into an excel spreadsheet. Two popular text mining and analysis software, SPSS Clementine text mining tool and NVivo 9, were, respectively, used to help us with the analysis of the content stored in the spreadsheet. It is noted that each of the tools offers some advantages in certain features and functionalities. We mainly used SPSS Clementine’s linguistic methods (extracting, grouping, indexing, etc) to explore and extract key concepts, generate categories and help us quickly gain insights from the textual data. We mainly used NVivo 9 software to conduct various query searches. The query searches were mainly used to test ideas, find interesting patterns, connections and unusual information based on the research questions. Figure 2 lists the main steps for the text mining process used in our study. By following the three steps (pre-processing, applying text mining and evaluating the mining results and recognize actionable information), we were able to identify new knowledge including patterns, issues and themes from the collected textual data. Typically, conducting text mining and analysis requires continuous evaluation

Step 1

Step 2

Step 3

Text Pre-processing

Text Processing/Analysis

Actionable Intelligence

Blog posts and forum messages Text Collection

Extraction and preparation

Viewing results to identify patterns, issues, trends, models

Applying Text Mining (Extraction, categorization, clustering, etc.)

Recommendations and Actions Text Collection

Figure 2. A process for text mining

Results

Source: Adapted from Abdous and He (2011)

of the data and multiple rounds of refinement to achieve rich findings (He, 2013a; Romero and Ventura, 2010).


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Fourth, as a result of the mining of our sample data set, the SPSS Clementine software tool generated many concepts and related terms using its text analytics algorithms. After reviewing the numerous concepts and terms generated by the software, we were able to quickly identify a number of emerging themes. The generated results rely on researchers to make interpretative judgments and analysis (Eisenhardt, 1989; Pavlou and Dimoka, 2006). Two experienced researchers discussed and compared these themes and made some adjustments by deleting, merging, combining and/or refining, and then, we identified six main categories. We reached concensus on the final categories and their labels after going through several iterations for refinement. Oftentimes, applying text mining to data sets requires continuous evaluation and refinement to achieve the best results (Zeng et al., 2012; Romero and Ventura, 2010). Furthermore, we did a lot of query searches based on the emerging themes using NVivo to get more understanding on each theme. The main findings are listed in Table I.


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The following results answer RQ1. As we were particularly interested in the issues related to social media mashups, we presented a detailed description for the issues that affect the development, use and adoption of social media mashups below: .

Data source use and trust/security issues. There are many social media sites and accounts. Some social media sites have their own terms and conditions regarding the access and use of their APIs and data such as maps. Some APIs may have security issues such as SQL injection problems or tracking users’ behavior and information secretly. Thus, there is a concern in determining which data sources to use or trust.

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Frequent update with data source issue. To engage people in the campus community, a successful social media mashup needs to be linked to multiple active social media accounts that can provide quality contents on a daily basis.

Categories Sharing social media mashups

Descriptions

Sharing their social media mashups; recommend APIs or feeds; seeking suggestions and feedback Discussing mashup development Sharing experience in social media mashup development and strategy or methods integration such as data sources used, platforms, coding, use of individual APIs and social media sites; sharing example codes; asking for help regarding integrating APIs and development; looking for programmers or testers Issues related to mashup Data source trust/security issues; frequent update with data development source issue; data quality issue (e.g. junk messages, spam, filtering, copyright, privacy); API quality; constant software evolution; usability and accessibility issue Engagement How to get people to access my mashups; mashup contests; promoting social media mashups; social media coordinator; partnership and collaboration; motivating people to contribute Table I. Business aspects Cost and budget; time needed; seeking business models or cases; Main themes and categories in social media success stories; mashup advantages/benefits Evolution/Future Trends Will mashup go away? Intelligent mashups or widgets; Web 3.0 mashup-related discussion

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If an HEI’s social media sites such as Facebook or Twitter are not updated frequently, social media mashups will not be engaging and successful. Social media mashups that are not updated frequently will lose the audience over time. On the other hand, it is necessary to find an appropriate amount of updates and ensure that the frequent updates are not going to overload internet users. SocialVibe (2012) found that when there are too many updates from social media sites, many people are likely to stop interacting with social media. User surveys can be used to help find a good balance in terms of the frequency of updates. Data quality issue. To build a trustworthy site and maintain a good reputation, it is necessary to ensure the quality of the content posted on the social media mashup sites. Data quality can be measured in terms of accuracy, completeness, timeliness and availability (Cappiello et al., 2010; Narman et al., 2011). A study by SocialVibe (2012) found that many internet users stop accessing a social media site because they did not see value in remaining connected with this site. Thus, quality content is critical to the value of social media mashups. This is particularly important if an organization decides to embed social media mashups on their institution’s homepage directly. In reality, some people post junk messages (spam), or inappropriate messages such as defamatory or commercial advertising messages to official university social media sites. The spam messages will also show up in the mashup sites if there are no filtering or mitigation mechanisms. In addition, occasionally educational materials without copyright clearance also appear on mashups (Moran, 2008). Some comments expressed their concerns on potential copyright and privacy on social media mashups. These messages should be filtered and moderated, and should not be allowed to appear on the social media mashups. API quality and constant software evolution. Cappiello et al. (2010) recommend the assessment of API quality using functionality, reliability, learnability/ understandability and operability. Furthermore, as mashups are built using many external web services, APIs and software components, mashups are fragile and highly sensitive to change of these web services, APIs and components (de Vrieze et al., 2011). For example, the published APIs from a social media data source may become unreliable (e.g. suddenly change or stop working) after the upgrading process; sometimes a mashup API has compatibility issues with certain browsers after the upgrading. As a result, this may cause problems with existing social media mashups if the social media mashups are not updated efficiently. There is a need for monitoring the performance and availability of third-party APIs to minimize issues and consequence caused by APIs that are no longer valid. Usability and accessibility issue. Perceived ease-of-use strongly affects the user attitude and acceptance toward information technologies (Davis, 1989). A welldesigned mashup interface can enhance user’s online experience, engage users and sustain a long-term engagement; a poor designed mashup interface can turn away users. Some mashups have minor issues with the display in browsers such as Safari. As mashup sites contain data from many different sources, sometimes the mashups are difficult for the visually impaired because some social media sites used “images and bullets as hyperlinks and did not have high contrast between text and background colors” (Hite and Railsback, 2011) and also lack consistency in font sizes and styles. Thus, a usability testing is needed to test the developed mashups using the five most popular web browsers: Internet Explorer, Firefox, Google Chrome, Safari and Opera.


3.3 Study 2: social media mashups in HEIs So far many different organizations including governments, news media outlets, business companies and non-profit organizations have adopted social media mashups. These organizations have implemented social media mashups for different purposes and user groups. As it is unrealistic for us to review all the social media mashups sites available on the internet, we decided to select a specific industry for further research. It is noted that a major industry that uses social media mashups is higher education. In recent years quite a few universities and colleges in the USA have implemented social media mashups to create a centralized and branded social media portal (Powers, 2010; Leung, 2012). To answer the second research question, we reviewed a number of social media mashup sites created by HEIs in the USA. The results for the second research question are listed as follows. An extensive Google search helps us find a few social media mashup sites made by HEIs in the USA. Although most universities in the USA have not engaged in the development of social media mashup sites, we did identify a trend that social media mashups are attracting the attention in HEIs. We found that many of the social media mashup sites were actually created in the past two years. Some good examples of social media mashups in higher education are listed in Table II. After looking at each of the above mashup sites, we found that these universities created social media mashups either by themselves or partnered with industrial companies such as AllofE. The primary purpose of their social media mashups is to display daily happenings, events and activities, attract social media audience and engage campus community in a more effective way. Furthermore, our review identified several approaches they used to design the interface of their social media mashup sites. Simple listing of social media accounts/sites and RSS feeds. The simplest way of building a social media mashup is to list the social media accounts/sites and RSS feeds available (Figure 3). The list can be organized by categories such as social media tools, type of organizations (academic colleges and departments, administrative offices and others), etc. Embedding social media mashup content into the university homepage directly. A more sophisticated way is to add social media mashup content onto the university homepage. A good example is offered by Missouri State University which uses their combined social media stream including related Twitter feeds, Facebook accounts and news posts to fill the “News & Events” column on the university homepage

Name of HEIs

URL of social media mashup

Johns Hopkins University Harvard University College of William and Mary Vanderbilt University Missouri State University Tufts University Youngstown State University Kent State University Depaul University Northern Illinois University Our Lady of the Lake University Oregon State University Boston University

http://www.hopkins-interactive.com/ http://www.harvard.edu/all-harvard-social-media http://social.wm.edu/ http://social.vanderbilt.edu/ http://www.missouristate.edu/ (directly on university homepage) http://socialmedia.tufts.edu/ http://web.ysu.edu/smashup/home http://social.kent.edu http://depaul.edu/smashup http://www.niu.edu/smashup/ http://cms.schooleffects.com/ollu/home http://oregonstate.edu/main/socialmedia http://www.bu.edu/buniverse/


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Table II. Some good social media mashup examples from HEIs

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Figure 3. A simple social media list by categories

Figure 4. Social media stream on university homepage (from Missouri State University)

(Powers, 2010). The latest social media content appears on the top of the column. As a result, the university homepage always has new content in this column throughout a day (Figure 4). Tab-based interface design. The mashup page has multiple tabs for different social media such as Twitter, Facebook, YouTube and so on. The tabs serve as navigation menu and often appear either on the top of the page or on the left of the page. A visitor can click one of the tabs to look at the social media stream from one type of social media. Quite a few universities such as Tufts University and Vanderbilt University have adopted tabs to organize their social media content from different types of social media tools. An example of the tab-based interface (Figure 5) design made by Tufts University can be seen at http://socialmedia.tufts.edu/ Smashup interface design. A company called AllofE has created social media mashups for a few universities using a template called Smashup. Basically a mashup page is divided into multiple sections or boxes. Each section or box shows the updated stream from a type of social media such as Twitter, Facebook and YouTube. An example



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Figure 5. Tab-based interface design

of the Smashup interface design made by Northern Illinois University can be seen at http://www.niu.edu/smashup/ Hybrid interface design. A few universities use two or more of the above methods to build a comprehensive social media mashup portal. A good example is offered by Vanderbilt University who has combined social media stream on the main page. In addition, visitors can click one of the tabs on the top to view specific content from Twitter, Facebook or other types of social media tools. 4. Recommendations for social media mashup in higher education We learned a lot of insights from our in-depth review of the relevant blog posts, online forum messages and social media mashup sites made by HEIs. We found that many HEIs are increasingly interested in implementing social media mashups. However, due to the potential issues and challenges posed by mashups, it is important for HEIs to follow a methodology to implement social media mashups. Before the implementation, each HEI needs to determine whether they are ready for creating and maintaining social media mashups successfully with their existing recourses. Specifically, each HEI needs to assess the infrastructure readiness and people readiness (Bader et al., 2012). Based on the literature in web mashups, analysis of blog posts and our practical experience in the development and use of mashup, we offer the following recommendations and insights for those who are interested in the adoption and use of social media mashups in higher education environments. First, HEIs need to determine their needs and readiness for social media mashups first before they spend a lot of time and efforts implementing social media mashup initiatives. Shao (2009) argues that individuals use social media and user-generated media tools in different ways for different purposes. Methods such as surveys and interview can be used to understand the needs and opinions of various stakeholders (Bader et al., 2012; Wetzstein et al., 2011) and determine the purpose, relevance and importance of each social media site or platform (Evans, 2012). For example, HEIs need to find out how many social media sites they currently have and how often those social media sites are updated in order to make sure that they have a feasible environment to support the development and use of social media mashups. Each HEI should maintain a list of social media directory and update it on a regular basis. To generate the list of

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social media directory, a “call for inclusion” message can be sent to various units and organizations in the university community from time to time. After the mashup site is implemented, a form can be provided on the site to allow people to submit their social media sites for consideration. Second, an HEI may have many different social media sites at different levels. In addition to the official social media accounts or sites at the university level, many colleges, academic departments and administrative units (e.g. admission, communication and marketing, student services), student organizations and other campus community organizations/units also have their own social media sites or accounts. To build a successful social media mashup site in the long run, an HEI should have a social media acceptable use policy or best practice guidelines to clearly tell people what acceptable conduct on social media is. For example, universities such as MIT, Carnegie Mellon University, University of Texas at Austin, University of Maryland Baltimore County and Depaul University have posted best practice guidelines to help their members use social media effectively, protect personal and professional reputation and follow university policies. In addition, HEIs need to set a set of standards to decide which social media sites will be selected to be integrated into the HEI’s social media mashup site. Setting such standards needs the input, feedback and active involvement of various campus organizations that own social media accounts/sites. In addition, not every social media site can be technically added to a social media mashup. Only those social media sites that offer published APIs or feeds can be integrated into mashup sites. Third, HEIs need to clearly define and clarify the goals and objectives (Engelsman et al., 2011; Nogueira et al., 2013) of their social media mashup initiatives. The IT division of the HEI will play an important role in this phase (Hoyer and StanoevskaSlabeva, 2009) to engage the campus community to provide feedback for mashup development and enable the campus community to collaborate and help define the goals, design the user interface, develop the content, clarify the issues, etc. After the social media mashup is implemented, an HEI should actively promote it and inform people in the campus community about the use of social media mashup. Some good ideas include embedding the social media mashup into the HEI’s official homepage (similar to what Missouri State University does), adding a hyperlinked mashup icon on the HEI’s web site footer, creating a social media directory on the homepage and running social media campaign on campus. A social media coordinator position may be created to support the development and operation of social media mashups. A list of motivational incentives (Bengs and Wiklund-Engblom, 2012; Chen et al., 2013) such as monetary prizes, name recognition, top ten lists, honor-roll lists and awards should be considered to engage people to contribute to social media platforms provided by HEIs. In recent years, many complex social media mashups have been developed or are being developed. In general, the development of complex mashups requires multiple processing steps and techniques to format, combine and integrate a number of data sources and APIs. Therefore, it can become very challenging to debug complex mashups if the delivered result is not as expected or the mashup has any problems later (Hummer et al., 2010). Thus, HEIs need to require their mashup developers to provide in-depth documentation about the mashup development procedures including the data sources, APIs involved, processing steps and programming techniques in order to save future efforts and resources in troubleshooting and maintenance. After the social media mashup is implemented, the data from different data sources are formatted, combined and integrated into the new mashup site. As different


data sources such as internal data and external data typically contain different data schemas, formats and semantics (Di Lorenzo et al., 2009; Xu, 2011a, b; Fang et al., 2013), data pre-processing and manipulation such as conversion, mapping, filtering, format transformation and combination often need to be done for effective integration. Maximilien et al. (2007) recommend mashup developers to conduct integration analysis at three different levels: data level, process level and presentation level. To present the data output properly, considerable programming work and manual integration efforts are often needed from mashup developers. On the other hand, currently there are few automated cost-effective and reliable solutions to quickly detect spammers and sufficiently evaluate the data quality of different data sources upfront to completely avoid inappropriate information such as defamatory and commercial advertising messages on mashup sites. Technologies such as CAPTCHAs are useful but not sufficient to get rid of spam and unwanted content. The IT division will have to monitor and moderate the content of their social media mashup site to reduce negative impacts. HEIs need to develop a strategy to deal with sudden increase of external contribution. Occasionally, the number of external users who contribute to social media may suddenly increase due to breaking news or disruptive events. The rapid increase of new content will certainly impact the social media mashup at the data level and may cause some issues such as data redundancy and overload. As we mentioned earlier, too many updates on the mashup applications could overload Internet users and cause them to stay away from social media. HEIs need to be aware of these potential issues and prepare a plan in advance to balance the needs of the users who contribute to the social media content and the needs of the users who read the combined social media content on the social media mashup sites. There is a need to develop metrics to evaluate the benefits of social media mashups. HEIs need to collect raw data from their social media mashups and conduct ongoing evaluation to measure return on investment in social media mashups and determine whether the mashup realizes their goals and objectives such as community engagement. Chen et al. (2013) found that members’ attitudes toward virtual communities (VCs) and knowledge contribution intention are mainly related to their motivations and past experiences in using the VCs. Thus, relevant metrics may include both quantitative and qualitative indices such as number of visitors and contributors, number of returned users, number of meaningful conversations, number of replies, sentiment (feeling and emotion), levels of engagement, social influence (Graham and Greenhill, 2013), etc. Data mining techniques such as opinion mining or sentiment analysis (Pang and Lee, 2008; Qiu et al., 2003) can also be used to identify the attitudes of social media users on certain topics. In terms of the significance of our study, we believe that some of the above recommendations and insights are general in nature and are applicable to other types of organizations such governments, large corporations and non-profit organizations (e.g. Red Cross). Many of the aforementioned issues (e.g. data quality, integration, update, API change) faced by HEIs are likely to be encountered by organizations in other sectors when they implement social media mashup sites. Similar to HEIs, many businesses such as multinational enterprises have many divisions and subsidiaries with numerous employees, customers, suppliers and partners (Xu et al., 2012; Li, 2011). Accordingly, they have established many social media sites to meet diverse needs and thus have a need to build social media mashup sites. A business can draw on this analysis to guide the design and development of their social media mashup initiative. Building a social media mashup site can help the business more effectively manage and monitor their communication with different types of users on a centralized platform


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and possibly create new means of interactions with users. As more and more businesses embrace customers as co-creation partners in their innovation efforts (Merchant, 2012), a business may be able to leverage the power of social media mashups to expedite the process of creating, delivering and capturing values and to further enhance their business model in this social era. In addition, our recommendations and insights can help governments and public service organizations integrate various social media data sources for particular purposes. For example, there are many social media sites that are related to emergency management such as traffic information, power outage, storm surge predictions, emergency shelters, evacuation routes, online maps and videos, etc. Effectively integrating these social media sites is a challenging but worthwhile project. Thus, our study provides a good starting point for further research about the adoption of social media mashups in various organizational environments. On the other hand, the emerging technology trends including big data (Chen et al., 2012), business intelligence (Duan and Xu, 2012), cloud computing (Wang et al., 2012) and smart city infrastructure (Naphade et al., 2011) will certainly influence the pace and flow of user-generated content on the internet and pose a big challenge for organizations to manage, integrate and process a large amount of data from multiple sources on the web (Shi et al., 2012). Our study will contribute to the development of relevant strategies to meet these emerging challenges in the digital technology landscape. To the best of our knowledge, our paper is the first academic article to specifically discuss the use and adoption of social media mashups in organizational environments. The study has two limitations. First, the analysis and interpretation of the mining results and was limited by the experience and skills of the authors to find patterns and themes that are latent in the generated concepts. It is possible that we missed some themes in our results; Second, this paper mainly focusses on providing insights and does not provide specific guidelines for implementing and managing social media mashup. More work is needed to convert the insights into stronger impact measures. To make big impact on the practice of implementing and managing social media mashups, each organization has to take efforts to convert these insights into specific guidelines based on their priorities and the concerns of their stakeholders. We recommend organizations to identify the involved stakeholders and set up meetings to go through these insights and come up with the detailed guidelines. 5. Conclusion and future research Social media mashups are designed to integrate content from different social media sites/platforms in a meaningful way. Creating social media mashups offers the potential to foster social media awareness, improve communication and increase dialogue in the target user community. As the list of social media sites/accounts is constantly growing, many organizations have showed their interests in integrating multiple social media channels and implementing social media mashups. Currently, there is little discussion in the literature about the adoption and use of social media mashups in organizational environments. In this paper, we identified some representative themes and issues associated with social media mashups through mining relevant blog posts and forum messages. We also reviewed various social media mashup sites created by HEIs and introduced the approaches they used to design the interface of their social media mashup sites. Finally, we provided recommendations and insights for social media mashup development and adoption. Our work can be used to help HEIs and other organizations with their strategic planning, decision making and implementation related to social media mashups initiatives. As for future research, we will reach out to HEIs that


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Xu, S., Xu, L. and Basl, J. (2012), “Introduction: advances in e-business engineering”, Information Technology & Management, Vol. 13 No. 4, pp. 201-204. Yu, J., Benatallah, B., Casati, F. and Daniel, F. (2008), “Understanding mashup development”, IEEE Internet Computing, Vol. 12 No. 5, pp. 44-52. Zang, N. and Rosson, M.B. (2008), “What’s in a mashup? And why? Studying the perceptions of web-active end users”, IEEE Symposium on Visual Languages and Human-Centric Computing, Herrsching am Ammersee, September 15-19, pp. 15-19. Zeng, L., Li, L. and Duan, L. (2012), “Business intelligence in enterprise computing environment”, Information Technology & Management, Vol. 13 No. 4, pp. 297-310. Zhong, N., Li, Y. and Wu, S. (2012), “Effective pattern discovery for text mining”, IEEE Transactions on Knowledge and Data Engineering, Vol. 24 No. 1, pp. 30-44. Further reading Duan, L., Street, W.N. and Xu, E. (2011), “Healthcare information systems: data mining methods in the creation of a clinical recommender system”, Enterprise Information Systems, Vol. 5 No. 2, pp. 169-181. About the authors Dr Wu He is an Assistant Professor in the Department of Information Technology and Decision Sciences at the Old Dominion University. Wu He received his PhD at the University of Missouri. His research interests include knowledge management, social media, data mining, cased-based Reasoning and information technology education. Dr Wu He is the corresponding author and can be contacted at: [email protected] Dr Shenghua Zha is an Assistant Professor at the James Madison University. She holds a PhD in information science and learning technologies (University of Missouri-Columbia, USA). Her research interests include social media, e-learning and faculty development.

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