Keywords

1 Introduction

After the concept of Gamification was introduced in early 2010, the interest of both business and academia in Gamification, has grown significantly [2]. Indeed, Gamification has become a sort of phenomenon, and a regular part of our social, cultural, and professional life [4]. This phenomenon has especially been used in social media applications (SMA) [5]. Gamification systems for utility applications involve a special array of attributes. Nevertheless, traditional usability and game design elements each fail to encompass these attributes on their own. In the field of Gamification, much work has been devoted to designing gamified software using game design elements. However, there is presently insufficient research on designing gamified software investigating both entertainment and utilitarian elements [1]. In addition, pure game design elements are not made to support user-defined tasks, which differentiate them from utility software design elements [6]. Also, there are many ambiguous uses of this technology and technical difficulties, because of the limited documentation of Gamification data usage in the industry [2]. Although the Gamification is a promising approach to encourage users’ motivation, engagement, enjoyment and efficiency, ignoring SMAs’ design elements, as part of Gamification elements would make it difficult to design the system and hard to guarantee success [7]. In addition, implementing Gamification in utility systems without considering HCI concepts could be more risky and difficult to get optimal Gamification benefits, due the tight attachment to users’ behaviors, business processes, organizational structure, and the business model they support [7]. Game design elements deal more with virtual life [8]. However, SMA design elements involve attributes that serve utility applications and entertainment at the same time for real life issues [9]. Since Gamification in utility software deals with real life issues, developing a Gamification framework would be more related to SMA design elements. Investigating both SMA and game design elements, as a combination of entertainment and utilitarian elements, gives insight about unique attributes of designing gamified software systems. SMA provides practical functionalities while entertaining at the same time. Learning from SMAs will help us understand how to integrate Gamification (entertaining features) into utilitarian applications. This study reviews prior research in both Gamificationa and SMA main concepts and prior frameworks and heuristics. After reviewing prior studies about user interactions with SMA and Gamification, this study is inspired by the revised lexical approach [3] to investigate Gamification framework by analyzing large-scale online SMAs’ reviews. It includes the following major steps: (1) Collecting and downloading a large body of online reviews; (2) Employing Basic Natural Language Processing (NLP) techniques to build a dictionary of Gamification descriptive adjectives; (3) Extracting users ratings of adjectives; (4) Conducting factor analyses to identify Gamification traits based on adjectives; (5) Conducting card sorting technique to further simplify the complex factor structure discovered in the factor analysis. The main objective of this study is to develop a Gamification framework using lexical approach to investigate user behavior in SMA from both entertainment and utilitarian perspectives.

2 Literature Review

Most computer users today have grown up playing video games and have integrated social media applications into their daily lives. Playing video games and involvement in social media applications are popular and far-reaching activities people practice for different purposes. Thus, many systems and applications use video games and SMA design elements to influence their users towards specific behavior [1, 10,11,12]. Driving users toward specific behavior, changing their behavior, developing skills and/or innovation are the most common objectives of using Gamification. Lexical approaches can be adopted to understand the importance of SMA design elements to develop effective Gamification framework and enhance the user experiences [3].

2.1 Gamification

Gamification Studies.

Gamification has been a hot topic of investigation since 2010. Most researchers defined Gamification as merging game design elements in non-gaming contexts [1]. Enjoyable systems and interfaces have been studied intensively since the early 1980s. Several studies discussed challenge, fantasy, and curiosity as video game design elements to influence user enjoyment and UX on other systems [13]. Thus, a gamified layer on non-gaming systems adopts elements involving enjoyable factors, while providing options for decision making, creating additional feelings that transfer from the digital to the real world, uncertain connections to external values, and work by rules [14]. Some of these gaming elements - such as clearly defined goals, better scorekeeping and scorecards, more frequent feedback, a higher degree of personal choice of methods, and consistent coaching - came from understanding the role of enjoyment in improving performance throughout many gaming and non-gaming environments [15]. In the field of education, Mitchell and Danino [16] have recently analyzed the importance of student involvement in the development of an effective learning experience by motivating them through self-learning tools. Mitchell and Danino [16] also argued that connecting the Gamification process to the user’s real world would motivate the user even further. For instance, “one student commented that seeing his team move up and down the leader-board was like seeing his grade go up and down each day, and this made him increase the effort he put in.” Similarly in the field of advertising, Terlutter and Capella [17] discussed that there are out of system factors that could affect the Gamification application. First, individual factors include: level of maturity, cognitive capabilities and capacities, advertising literacy, media literacy, recognition of commercial intent, game familiarity, gaming experience, brand familiarity, attitudes toward advertising, involvement with game and brand, flow and its antecedents, and entertainment. Second, social factors include whether the game is single- player versus multiplayer, whether there is any social interaction during game play, peer communication, peer group influence, family influence, opinion leadership, and culture. In addition, most Gamification developers focus on customers and ignore employees. This is in spite of the fact that employee satisfaction builds customer satisfaction, and employee dissatisfaction could destroy the organization and customer loyalty [18]. Aparicio et al. [19] identified several tasks involved in creating an effective Gamification environment. First, producers must identify the main goal of the function they want to gamify. Second, they must identify objectives that are interesting to people. Third, they must select game mechanics that match the objectives and support the needs of human motivation. Finally, they should test the effectiveness of the Gamification application based on fun, quality indicators and satisfaction, and service quality. Another use of Gamification lies in combining games with social media networks, which leads to changes in the lifestyles of consumers. Berkovsky et al. [20] stated that Gamification’s enjoyable properties of playing elements can change the nature of the activity, and induce participants to participate in bursts of physical activity. In other words, gamified systems borrow elements from video games, SMA design elements, and HCI theories and concepts to make other “non-game” services and products more enjoyable and engaging [1]. Some studies identified gamified systems according to gaming and playing concepts [1]. According to these studies, any system can be gamified if one gaming element is used in part of that system. Also, Gamification application uses game design elements rather than being a fully-developed game, video game, or serious game [1, 21]. Gamification “has the game structure, but not the game surface” [22]. While the objectives of both Gamification and serious games applications are not entertaining, serious games are more related to simulated game solutions. These developed for the purposes of training, investigating, and advertising [1, 21]. Serious games feature a full-fledged game design [1]. On the other hand, Gamification is more suitable under partial game designs [1]. However, some research suggested that gamified systems should be defined as the continuous process of improving users’ system interaction, with opportunities for gameful experiences to fulfill stakeholder needs and values using game design elements [23]. Therefore, connecting user-centered design concept of game design elements to Gamification would make it more effective [10]. Nicholson [10] in fact suggested that a meaningful Gamification system would include user-centered game design elements into non-game contexts. Furthermore, Gamification structure, as previously mentioned, influences the system output by changing user behavior. But, how do gaming elements work to motivate people? Flatla et al. [24] discussed this particular question, and argue that collaboration is an essential part of interactive systems to ensure that input and output are ideally configured. Thus, Gamification structure would motivate users to participate, thereby improving the performance and accuracy of human-computer interactions [24]. Also, Mekler et al. [25] added that meaningful frame elements motivate participants to generate more interactions and inspired them to do better at tasks within the game, thereby creating a high quality experience. Gamified applications take the advantage of using powerful game design elements, and apply this advantage to solving problems in different fields [26]. The use of Gamification concepts has the ability to affect engagement and loyalty, improve motivation, change behavior, encourage contribution, increase involvement, and contribute to efficiency [26, 27]. For example, a company could use Gamification for brand awareness, improving marketing strategies and effectiveness, and increasing user retention and participation [1, 21]. For instance, in personal and business use, Gamification could affect work completion time positively and improve the quality and the quantity of work [27]. In addition, it would reduce errors and mistakes with faster feedback, in order to improve visibility of progress and recovery from errors [27]. Gamification adopted concepts from the video game industry, psychology, computer science and marketing to deliver more effective results [28, 29]. In Gamification, psychology and HCI studies and concepts play the role of understanding human behavior and needs [12]. Thus, it drives user behavior toward specific targeted values [21]. Furthermore, motivating contribution on a system using Gamification can affect user behavior [28]. Moreover, social psychology theories contribute to our understanding of Gamification, particularly with regards to understanding the motive behind human social interactions and participation in gamified systems [27, 28].

2.2 Gamification Theories and Frameworks

Many developed Gamification systems’ design based on theories and frameworks from HCI, gaming, and psychology fields. For example, a “User-Centered Theoretical Framework for Meaningful Gamification” has been used to define users as the center of designing meaningful gamified system [10]. Also, many studies considered Gamification investigations under persuasive technology field [30]. Fogg [31] provided eight steps in the process of designing persuasive technology “(1) Choose simple behavior to target; (2) Choose receptive audience; (3) find what is preventing the target behavior; (4) choose an appropriate technology channel; (5) find relevant example of persuasive technology; (6) imitate successful example; (7) test and iterate quickly; (8) expand on success].” These steps are used as milestone for effective design in Gamification. Since gamified system in utility applications required users integration, understanding people behavior is an essential factor in designing an effective Gamification system. “Self-determination Theory” (SDT) [32, 33] helped understanding users behavior. SDT predict goal-oriented behavior through user needs and motivation [34]. Three fundamental needs (Competence – Relatedness – Autonomy) were defined to enhance personal growth [32]. Two motivation categories (intrinsic and extrinsic) were defined from SDT needs [32, 34]. Four type of intrinsic reword (Satisfying work - Experience of being successful - Social connection – Meaning) developed using SDT theory [35]. Gamification applications use combination of both extrinsic and intrinsic motivations to engage the users [36, 37]. As well as “Four –Drive” model helps to understand the reasons of users acting in a certain way [38]. The model categorizes motives to change user behavior to four categories [38]. Thus it would satisfy our biological need for curiosity. These categories include [38]: (1) Acquire: The felling of obtaining physical and emotional things; (2) Bond: The relationships and communication between individual; (3) Defend: Protection from physical and emotional threats; (4) Learn: Gaining new knowledge and skills. In addition, “Fogg’s Behaviour” model proposed three elements to change use behavior (Motivation – Ability – Trigger) [21, 29, 39]. (1) Motivation: The desire level of engagement in an activity; (2) Ability: The level of skills to performer a task; (3) Trigger: The level of encouragement to do a task. Furthermore, “Persuasion Profiling” model provide several principles to enhance users’ behavior towered specific manner [29]. These principle include: (1) Reciprocation: The obligated feeling to return a favor; (2) Scarcity: People value rare things more; (3) Authority: The power of legitimate authority request (people will follow/believe the request); (4) Commitment and Consistency: People do as they said they would; (5) Consensus: People do as other people do; (6) Liking: We say yes to people we like. From game design field, the “Four Elements that Defined a Game” theory [40] has been used on Gamification. This theory provides four elements to define games, which it could be useful for designing gamified systems [40]. These elements include goal and outcome, rules, feedback, and voluntarily participation [40]. McGonigal’s Four Game Experiences model [40] support using game design elements to develop an effective Gamification system. This model suggests four types of experiences in Gamification influenced by game design elements [40]. (1) “Urgent optimism”: enjoying come over issues to engage with the system and others by searching for the solutions; (2) “Blissful Productivity”: the motivation of continues efforts to face challenges; (3) “Social Fabric”: The fell of belonging; (4) “Sense of Epic Meaning”: enjoying selfless objective achievements. In addition, using “Dignan’s Behavioral game” model in designing gamified systems would enhance any activities and tasks to be more engaging and learnable by employing game elements to user interactions in everyday experiences [41, 42]. Moreover, Flow Theory [43] is widely used in Gamification. Flow is defined as the state where users are deeply involved in an activity, forget about the time and nothing around matter [43]. This concept would affect changing behavior; witch is one of the objectives of gamifying applications and activities [29]. In term of players personality “Bartle’s Four Player Personality” Types model would help understand the users behavior may on play experience to develop effective gamified strategy [44]. It suggests four types of players [44]. (1) Explorers: This type drives by the enjoyment of finding, understanding and exploring everything; (2) Killers: This type drives by the enjoyment from causing anxiety; (3) Socialisers: This type drives by the communication and relationship between players; (4) Achievers: This type drives by the enjoyment of getting to the goal/objective. Finally, “Five Stages Behavior Change Lifecycle” model provides insight into the type of games that will modify users’ behaviors [30]. It presents the different stages associated with key challenges and primary gaming mechanism. This, model is used as an indicator of game design element for Gamification that effect user behavior (Table 1).

Table 1. “Five Stages Behavior Change Lifecycle” model [30].
Full size table

In short, the research in this field discussed that gamified systems are having some game design elements and gameful experience to produce some quality results in context other than games. In addition to using game design elements and providing gameful experience, most of the gamified systems are using social media application elements to enhance specific users’ behavior toward the system values. Therefore, the combination of psychology, video games, business, computer science, and HCI theories and concepts is the power of Gamification to deliver more effective results. Gamification uses these powerful elements and applies it to solve different issues relate to different fields (help organizations teach, persuade, motivate, and develop meaningful brand relationships, and enhance the user experience) [29, 45]. Most of the studies of Gamification focused on the general concept and factors of Gamification. They propose high cost implementation to different existing system such as enterprise systems. In addition, they ignore the utilitarian elements of these systems. Many theoretical frameworks have defined and measured Gamification; however, they generally have used a small sample size or ignored the user’s perspective. Therefore, there are limitations in generalizing these frameworks. Furthermore, there is little to no research investigating the specific technological requirements, methods, and tools that are necessary for successfully implementing Gamification in utility applications. Lastly, Gamification research studies often do not examine the long-term effects of applying Gamification.

2.3 Social Media Applications (SMA)

SMA provides practical functionalities while entertaining at the same time. Learning from SMAs will help us understand how to integrate Gamification (entertaining features) into utilitarian applications.

SMA Interface Design and Trust.

Many studies have investigated the effect of interface design on user behavior. It was found that systems’ engagement characteristics and content enhance viewership of “user-generated content”, and that the type of background is crucial to user attitude toward the content of the page [46, 47]. In addition, SMAs, as a lifestyle changing tool, influence user behavior and affect both online and offline activities [48]. Users usually overcome privacy issues in SMA by trusting their capability to control the information they give and who gets access to it [49]. Also, privacy concerns are only a weak predictor of the membership to the network [49].

User Characteristics and Engagement.

In addition to Social media environment, user personality, user sociality, and user needs influence SMA usage. Some studies suggested that online activities could be affected by personality structures, inspiration and capability [50]. Personality effects user engagement in SMAs and number of friends [51,52,53,54]. In addition, user preference of specific social network platforms is associated with differences in personality and the platform design and features [55]. Social network design and uses is inevitably associated with a user’s social world and areas of life (work, family, and friends) [56]. Also, other studies found that users who present their true self in the Internet were more likely than others to have close online friendship and moved these relationships to a “face-to-face” level [57]. User needs and the awareness of the value of the social media can change UX within the application. This even starts from the level of adoption and how it affects the use of services [58, 59]. In addition, individuals choose the identities that aid them to better situate within a given social environment depending on the characteristics of the environment in which they find themselves [59]. People with a high need for cognition are more experienced on the Internet, use more hyperlinks, stay longer in the site and use information services in the Internet relatively more than those with a low need for cognition [60, 61]. On the other hand, it is noted that people with low need for cognition prefer interactive over linear sites [61]. Some research supported that personality is marginally related to Information Exchange (email and accessing information) and Leisure (instant messaging and playing games) [62].

Prior SMA Experiences.

Some studies suggested that improved user understanding of a system leads to better performance and then results in improvement in user experiences [63]. Fischer and Reuber [9] indicated that social media interaction increases the amount of access to resources and can expand the community. They propose that this huge amount of resources and the connection with more communities make a significant difference in decision-making and communication [9]. However, they found that investing heavily in social network interactions could lead to less productivity [9].

SMA Experience and Marketing Research.

Building an experience and personalizing are two of the most effective factors in social media that have a huge impact on utility applications [64]. SMA provides a lot of implicit feedback that could build excellent sources of information about users. Thus, a common task of recommender systems is to improve customer experiences through personalized recommendations based on prior implicit feedback.

SMA as an Entertainment Tool.

Sharing content and opinions in SMAs reflect on its environments, in term of fun, meaningful, rewarding and entertainment [65]. SMA as an entertainment tool fulfill users need of entertaining, emotional release, and anxiety relief [66]. In addition, enjoyment is one of the main motivations to make user share moments and photos within SMA [67]. Prior studies suggested that integration in SMA is one way of entertainment in this era [68, 69].

2.4 The Lexical Approach

The lexical approach originally used to investigate personalities is based on a lexical hypothesis. Socially relevant to life and notable individual differences are encoded into natural languages. Also, many people would describe these differences by similar terms. Thus, exploring personality-descriptive adjectives in natural languages can identify personality traits [3, 70, 71]. Using lexical hypothesis requires systematic search in a dictionary to get a collection of personality-descriptive adjectives [70]. After that, the least used terms will be excluded from the initial created list of adjectives. Then, a large sample of subjects would rate each adjective on the final list based on how each term would describe themselves or others. Considering personality traits research, the “Big Five” (openness, conscientiousness, extraversion, agreeableness, and neuroticism) is considered as one of the major models in the psychology studies [72]. Zhu and Fang [3] introduced a revised lexical approach to study user experience in game play by analyzing online reviews. Four stages were involved in this revised lexical approach: (1) Stage 1: Collecting online reviews, (2) Stage 2: Building a dictionary of game descriptive adjectives, (3) Stage 3: Extracting game player ratings of adjectives, and (4) Stage 4: Factor analyses. In this study, we argue that adopting the revised lexical approach [3] can help to investigate user experience of SMA. It is hypothesized that collecting a large number of online reviews of SMA would reflect the common patterns of adjectives used by users to describe the most important issues and factors of SMA. Investigating SMA user experience and design element would reflect both utility and entertaining factors in Gamification [73].

3 Development of a Conceptual Framework

Although Gamification is a promising approach to encourage users’ motivation, engagement, enjoyment and efficiency, ignoring SMAs’ design elements, as part of Gamification elements would make it difficult to design the system and hard to guarantee success [7]. In addition, implementing Gamification in utility systems without considering HCI concepts could be more risky and difficult to get optimal Gamification benefits, due to the tight attachment to users’ behaviors, business processes, organizational structure, and the business model they support [7]. Game design elements deal more with virtual life [8]. SMA design elements involve attributes that serve both utility applications and entertainment for real life issues [9]. Most SMAs are utilitarian in nature by offering some useful features and functions to the user. At the same time SMAs include some game elements. The tremendous success of SMAs should shed some light on the Gamification of general utilitarian systems. In order to develop a Gamification framework, this section analyses models from both game research and social media application design elements as well. Based on both perspectives, an integrated framework was proposed. Identifying the factors that shape the use and design of SMAs would help professionals to develop effective gamified applications and build excellent user experience. The approach used to develop the Gamification conceptual framework is inspired by the lexical approach used in studying personality traits [70]. The idea of using lexical approach came from understanding people behavior from representative words and phrases. This approach becomes a good base for psychologists to understand personality traits using words to understand people behavior [74]. While users integrate in SMAs and gamified systems by commenting on these apps, major factors impacting SMAs can be studied in a similar fashion. Thus, these factors can help develop a Gamification framework that understands user experiences in Gamification. A lexical analysis can be performed to discover the clusters among the adjectives used in online reviews of SMA. Since the proposed framework is supported by a large number of online reviews, it will likely have higher validity. We present a revised lexical approach to investigate Gamification framework by analyzing large-scale online SMAs’ reviews. As suggested by Zhu and Fang [3], it employed a lexical analysis process with the following 4 stages: Stage1: Collecting online reviews, Stage2: Building a dictionary of Gamification descriptive adjectives, Stage3: Extracting user ratings of adjectives, Stage4: Apply factor analyses, and Stage5: Card Sorting. Stage 5 was added to further simplify the complex factor structure discovered in Stage 4.

3.1 A Lexical Analysis of Online Reviews of SMAs

Stage 1 Collecting Online Reviews.

The goal of this stage is to download SMA users’ reviews from independent online websites. Then, store the reviews as a structured relational database for subsequent analysis. Reviews represent different users perspectives including experts, end users, developers, and other professionals related to each application type. Each user describes applications in different point of views by using their own words. These words could be similar or different for different users. Using these massive reviews would help us to ensure the validity and reliability of the Gamification framework. Only textual information was downloaded to use lexical analysis. Selection of these website is based on the following criteria to ensure the quality of the data. First, ensure the diversity of SMA users by choosing popular independent SMA review website with significant amount of traffic. Second, ensures the popularity of the applications by selecting highly ranked SMA review website that user trust. Third, achieve the maximal generalizability by choosing websites that have reviews of a wide variety of SMA. Thus, five websites were selected based on these criteria (Table 2).

Table 2. Sources of online reviews, websites traffic and ranking about SMA
Full size table

Perl provides a powerful facility for text manipulations. Thus, Perl was selected as the main programming language to develop a special web crawler program for each five SMA websites. Six standards were addressed in developing the web crawlers: (1) download text SMA reviews; (2) remove all HTML tags or any markup language tags from the text; (3) remove repeated contents; (4) develop a web crawlers that deal with any hierarchical structures on the five selected SMA Websites; (5) develop the web crawler to handle any interpretation in the downloading process and not re-download duplicate content; (6) save downloaded data in structured relational database.

Stage 2 Building a Dictionary of Gamification Descriptive Adjectives.

This stage is designed to analyze adjectives describing Gamification and SMAs from the downloaded online reviews. An application was developed using relevant Perl modules for natural language processing (NLP). The process of this stage starts by parsing individual words from original texts and checking the part of speech (POS). Second, identify Gamification and SMA descriptive terms. Then, filter out stop words and retaining new jargons created by users. After that, combine synonyms and antonyms together. Finally, capture overall frequency and the number of reviews containing a word. To ensure the quality of the reviews, unique aspect of an application, and unique users experiences, we propose a basic strategy to examine words semantically rather than parsing sentences syntactically using WordNet. WordNet is a rich lexical bank filled with words belonging to four POS. At the end of this stage terms will be stored into the structural database with one frequency of the number of the term’s occurrences in the entire content, and the number of distinct reviews has the same term. Defining and extracting descriptive terms is based on splitting raw sentences into a group of tokens based on punctuation marks. Thus, the program can extract the useful terms for this study. Also, Stop-words (e.g. the, a, in, at) defined in the English language, and phrases with non-character symbols other than hyphens or underscores were eliminated to improve the efficiency of parsing process. Furthermore, comparative or superlative adjectives were converted back to their basic forms before storing them to the database. Gamification and SMAs jargons treated as a special category of terms that featured by the words or phrases unrecognized by WordNet. Dictionary.reference.com website were used to avoid misspelled words. Then, Levenshtein Distance algorithm [75] were used to offer a proper spellings to store on the same database.

Stage 3 Extracting User Ratings of Adjectives.

Each online review was treated as an independent observation and converted to a dataset. Each word on the list of adjectives produced in Stage 2 was treated as an individual item. The list of adjectives was saved as the field names (columns) of a database table. Then, all online reviews were retrieved one at a time. Each review about one application was processed as an individual record. Adjectives used in the same review must be somehow related because they were used to describe the same application. If an adjective appeared in this review, the value for this adjective (field) was set to 1. Otherwise, a zero value was registered.

Stage 4 Factor Analyses.

An exploratory factor analysis with varimax rotation was conducted to discover potential patterns among 7730 descriptive adjectives. We conducted two rounds of factor analyses with major steps to develop a short list of factors and consolidate synonyms and antonyms to these factors. The objective of the first round was to group adjectives into discriminative factors. Un-weighted least squares (ULS) estimation was chosen for this factor analysis, and communalities were estimated by square multiple correlations (SMC). Also, in case of rarely used adjectives three extra steps have handled it. First, calculate the basic descriptive statistics for each term. Second, Identify and remove the terms with the smallest variances (e.g., 1.4675377E−5). Then, re-compute the correlation matrix. The program will return to step 2 in case of any issues in step 3. This process cleared the data to leave 3,287 adjectives in the final analysis. After that, conduct factor analysis procedure on the new matrix (474,002 by 3,287). The first round of factor analysis produced 513 factors. There are some initial impressions of these factors. First, it presents continuous loading values and factor loading ranges from 0 to 0.741 with no clear dividing point between the loadings for most of the factors. Second, Several factors include adjectives of similar meanings. Thus, two criteria were used to re-organize the factors in order to capture significant patterns and to minimize the odd of misinterpretation (1) Words have to be recognized by WordNet as synonyms or antonyms; (2) Words within the same group should statistically correlate]. The first condition ensures semantic similarity, and the second helps verify that the words in the same group were actually used as similar words. Consolidating these adjectives by compare each adjective on each factor from the first round to the other adjectives as synset words, similar, and antonyms. Also, considering only words from the collected adjectives previously. In addition, moving a term from one factor will take them off other factors with no further processing. This process produced 1,358 groups of adjectives to use in the second round of factor analysis. In the second factor analysis the number of distinct adjectives on the same group showing in the same review was used as the value of this group for this review. Based on a 474,002 by 1,358 matrix, 621 distinct factors were extracted and ranked. Each factor was then thoroughly studied and labeled.

Stage 5 Card Sorting.

The 621 factors were clearly too complex and have substantial redundancy. Thus, factor structure must be simplified. Two rounds of card sorting were conducted to consolidate the 621 factors in a sensible way. Cart sorting session #1 was designed to identify high level constructs among the 621 factors and cart sorting session #2 was intended to verify the high level constructs from session #1. Cart Sorting Session #1 was conducted as an open card sorting. Since the initial 621 factors contained too much information and were more than participants could handle in one card sorting session, only the first 100 factors were included in this session. This session would present the most critical information and could be representative among all of the factors. Also, in the worst case, the Card Sorting Session #2 would analyze all factors and catch any significant information that might have been missed in Session #1. Three experts at both card sorting and social media apps participated in this card sorting session. They examined the 100 factors, group them, and label each group with a short title and a brief definition. All of the adjectives with a factor loading of 0.5 or higher were provided for each factor during this process. A participant could introduce as many or few labels as he or she deemed appropriate. The factors were randomized in order when presented to participants. As a result of this session, the participants created 18 labels. These labels were further analyzed and consolidated into six high-level factors (Table 3) by the research team. The six high-level factors were used in Card Sorting Session #2. The objective of Card Sorting Session #2 was to validate the six high-level factors from Session #1. Three participants who were familiar with SMAs were recruited. They were asked to review all 621 factors and place each of them into one of the six high-level SMA factors. The participants were allowed to create new labels for any factors that didn’t fit in any of the six high-level factors. Each factor was presented along with adjectives with a factor loading of 0.5 or higher. Cohen’s Kappa values were calculated to check the inter-rater agreements among the participants (Table 4). The overall Kappa value for all six factors was 0.796. This Kappa value strongly suggests that the six high-level factors indeed represent the structure of all 621 factors.

Table 3. Six factors on SMA from open card sorting
Full size table
Table 4. Cohen’s Kappa values
Full size table

3.2 Conceptual Framework and Hypotheses

As a result of the lexical analysis, six Gamification factors were discovered: playability, creativity, usability, trust, sensation, and engagement. Figure 1 presents a framework depicting the six most important traits about user experience of SMAs and its contribution to use Gamification features and performance with non-game applications. Playability is a set of criteria that describe user experience to evaluate user interaction to specific system to provide enjoyment, entertainment and learning strategies [76]. The lexical analysis in this study found that this factor is related to adjectives concerning fun, challenge, skill, entertainment, relaxation, escape, and enjoyment. Usability represents another important aspect of Gamification. As any computer applications, gamified systems must be effective, efficient, and user-friendly. Usability always plays an important role. Creativity is the emerging in action of a novel relation product, growing out of the uniqueness of the individual on the one hand, and the material, event, people, or circumstances of his/her life on the other [77]. Both gamified applications and users have creativity characteristics and they influence each other’s creativity over time [78]. Prior studies suggested that creativity is a collaborative effort [78]. Since Gamification design and user creativity affect each other, user acceptance of gamified systems will be affected by the design creativity. The construct, Creativity, is related to adjectives about creativity in Gamification design perceived by users. This construct is consistent to the findings from prior studies indicated that Gamification designing elements have a huge impact in user behavior toward these application and other activities [46]. Trust reflects a unique attribute in user’s experience of gamified systems. Also, users’ interaction on gamified applications build up users trust with the application and other users over time [79]. Trust between users and gamified application encourage the use of the application [80]. Most adjectives converged on this construct are about the ability of an application to be meaningful, doing the right thing, truthful, and safe. Sensation stands for users’ affective reactions that may be stimulated by narratives, visual and audio effect, etc. Gamification has variety of use and wide range of content, design, and activities. When Gamification users socialize with each other via the apps, users would have affective reaction toward the design and the content or both [81]. Engagement reflects socialization, experience, social needs, personality, personal behavior, and social media activity. Personality, user needs, socialization, UX in Gamification, and Gamification activities affect Gamification usage [50, 54, 82]. Per the above discussions, a gamification framework is proposed (Fig. 1).

Fig. 1.
figure 1

The 6 factors contribution to Gamification

Full size image

This framework presents 6-traits: playability, usability, creativity, trust, sensation, and engagement. These elements are combinations of service and game design factors. Since these traits were extracted from a large number of online reviews, they represent the most critical traits in user experience. They can be used to measure and improve user experience. Gamification is associated to game design elements applied to utility software dealing with real life issues. Developing gamified systems based on understanding enjoyment and adding elements from the digital world can improve solving issues and enhance performances in the real world [1, 15]. Many studies discussed the value of enjoyment and adding game design element, such as challenge, clear goal, feedback, fantasy, emotions, and curiosity, to design effective gamified systems [13, 14]. Playability could change the nature of a utility application and encourage more participation and engagement on the application [20]. Also, it could enhance user experience and user integration with specific system [76]. Moreover, it would increase the use of the gamified application [76]. Since Gamification focuses on the non-gaming context and utility application, usability of productivity software will be needed. Adding user-centered game design element concepts to Gamification development can increase the effectiveness of the application [10, 28, 29]. Also, Gamification could reduce time to complete certain task and improve the quality of the work [27]. Moreover, it would reduce the amount of errors and recovery time from errors [27]. Improve usability in an application will increase efficiency, ease of learn, memorability and reduce error frequency and severity [83]. Developing and designing routine tasks in an innovative and new ways lead to more effective Gamification settings [16]. Redesign and representation of a task is one of the effective elements in Gamification that borrowed from game design field [16]. Many business attempt to apply it in utility applications to improve users performance and encourage engagement in these systems [16, 84]. Some studies investigated that users continue enjoy and use Gamification because it make them feel more creative and effective [10]. Crumlish and Malone [85] supported that, Gamification assist users to engage and solve issues in new and innovative ways. Moreover, trust in collaborative environments plays very important role to encourage engagement and increase the speed of doing a task [86]. Also, trusting the application to continue fulfill the users’ needs reflects on the use of the application on the long run [87]. We know that Gamification increases users engagement and encourages participations in activities [24]. Furthermore, Gamification could be used to fulfill users’ emotional needs [24, 25]. Usually users feel attached to the outcomes and to the system when it fulfills their needs, emotions and values [23]. Sensation and reflections keeps users emotionally connected to gamified applications [88]. Also, it influences users’ engagement and participation in gamified application activities [89]. Encouraging users’ involvement in a system improves user experience [16]. Also, collaboration would ensure the quality of the input and outputs in interactive systems [24].

4 Conclusion and Future Studies

In short, six factors were presented: playability, creativity, usability, trust, sensation, and engagement. They are the most important elements about user experience of SMA and its contribution to Gamification. Playability is set of criteria that describe user experience to evaluate user interaction to specific system to provide enjoyment, entertainment and learning strategies [76]. As any computer applications, Gamification systems for utility applications must be effective, efficient, and user-friendly, usability always plays an important role. Creativity is the emerging in action of a novel relation product, growing out of the uniqueness of the individual on the one hand, and the material, event, people, or circumstances of his/her life on the other [77]. Both the gamified system and users have creativity characteristics. In addition, trust reflects a unique trait in user’s experience of gamified system. Sensation stands for users’ affective reactions that may be stimulated by narratives, visual and audio effect, etc. Finally, engagement reflects socialization, experience, social needs, personality, personal behavior, and activity within the gamified system. This study demonstrates a new perspective to conduct research in the intersection of Game Studies and HCI (entertainment and utilitarian aspects). Gamification traits are identified for creating design guidelines and evaluating Gamification tools.