Academy of Marketing Studies Journal (Print ISSN: 1095-6298; Online ISSN: 1528-2678)

Review Article: 2024 Vol: 28 Issue: 3S

Consumer Factors and their Influence on Behavioural Intentions towards the Adoption of Gamification

Rajni Gupta, NSB Academy, Bangalore

Kavita Mathad, Dean & Chief Growth Officer (CGO), GIBS Business School, Bangalore

Hayri Uygun, Recep Tayyip Erdogan University Rize Turkey

Citation Information: Gupta, R., Mathad, K., & Uygun, H. (2024). “Consumer factors and their influence on behavioural intentions towards the adoption of gamification”. Academy of Marketing Studies Journal, 28(S3), 1-10.


Purpose: This article studies the consumers' behavioural intentions toward adopting gamification as a promotional technique. This research examines consumer-related aspects like Price Consciousness, Usefulness, Awareness, Personal Perspective, Perceived Critical Mass, Easy to Use, and Personal Innovativeness to establish which elements impact adoption intentions most. Design/methodology/approach: Data was collected from 367 consumers. Stepwise regression analysis was employed to investigate the nature and significance of the observations. Findings: The reactions of consumers who are more and less concerned with pricing to price reductions and bonuses across low and high benefit levels have been studied. The findings also indicate that more price- and value-conscious consumers are likelier to adopt gamification. Stepwise regression indicated that each attribute contributed differently to the consumers' behavioural intentions. Originality/value: Digital marketing is a promising technique for marketers to engage the targeted customers. Gamification is one of the most recent techniques used in digital marketing. The success of this digital promotional technique depends upon consumers' behavioural intentions. Therefore, an attempt has been made in this paper to analyze the impact of identified factors on consumer behavioural intentions, which is scarce and unique and very limited work has been done in this area.


Gamification, Price Consciousness, Usefulness, Awareness, Personal Perspective, Perceived Critical Mass, Easy to Use, Personal Innovativeness, Behavioural intentions.


Technology advancements have changed consumer shopping habits and behaviour. It has provided various shopping options, including mobile and online buying. The growth of mobile phones and internet connectivity is now credited to India. Mobile phones are becoming more widespread as most people now own one. According to the Nilsen Informate Mobile Insight Report, 27 million people in urban India, or nearly one in five of the population, are smartphone users. Most of them are between the ages of 18 and 24. (2012). According to Meeker (2014), India has 117 million smartphone users, making it the third-largest smartphone user base after China and the US. Around 8 million people use these smartphones for e-banking, online shopping, and travel.

E-commerce has opened many options for e-retailers, increasing competition between e-retailers daily. E-retailers use different strategies and techniques to advertise and promote their products in a competitive environment. Companies are designing innovative digital sales promotion techniques to beat the competition, among which gamification is a highly engaging promotional technique. Gamification is one technique in which customers play online games to win reward points for discounts on their purchases. Mobile Behaviour Report (2014) states that 46% of users use m-coupon to get deals. Given that the market is used to m-coupon, gamification would help marketers promote their products through increased customer engagement and loyalty to achieve a better conversion rate at e-commerce or m-commerce.

Today, the gamification market is still nascent in most developing countries; however, India is one potential market to bring in more business opportunities soon because of the higher proportion of the young population. Gamification increases in various fields to increase user engagement (Hamari & Koivisto, 2015). Online Games are becoming a part of youngsters' daily routines, which is very appealing. Although Nick Pelling coined the gamification technique in 2002, it gained popularity post-2010. By 2025, more than 70% of the top 2000 firms, according to Gartner (2018), should be using at least one gamification application. According to an M2 study (2012), 5.5 billion dollars will be spent on gamification tools, services, and applications worldwide by 2025. These data demonstrate online buyers' enormous potential to utilize gamification to receive discounts.

Gamification has recently gained popularity in marketing to increase loyalty, like playing games and online reviews (Moro, Ramos, Esmerado & Jalali, 2019). Although very little is clear about consumers' attitudes towards emerging marketing channels, a few companies are testing water in these new trends. For instance, SCVNGER is an interesting gamification app that gives various brands an excellent platform for implementing gaming tactics for better customer engagement. Green Giant, an initiator in gamification, teamed up with Farmville to increase online sales through online customer engagement. A few more names, like Nike+, Samsung, and Fashos, used gamification to promote and deal effectively. There is considerable research on various online promotional activities and a few on gamification, but little is known about the factors influencing consumers' adoption. As gamification has recently received considerable attention among marketers to enhance their online presence and sales, it is necessary to study the factors influencing consumer behaviour toward this technique. The success of gamification depends on the game's design, and a suitable gamification plan is only possible when e-retailers can predict consumer behaviour. The paper seeks to address this gap and find the influence of identified factors on consumer behavioural intentions toward the adoption of gamification.

Literature Review

Marketers use gamification to encourage the desired behaviour; it combines game elements into applications, including websites, training courses, educational materials, marketing efforts, and customer service. Gamification was not widely used in the industry until 2010. The term "gamification" refers to "the usage of game design principles in non-gaming contexts" (Deterding et al., 2011). Modern authors like Zichermann (2013) made gamification famous by describing the function of game mechanics in marketing as a part of loyalty programs in his book "Game-Based Marketing." McGonigal (2011) strongly emphasizes the usage of games to engage online users in "Reality Is Broken." The existing literature provides various theories and concepts to explain the adoption behaviour of customers for any new technology in general—however, few studies related to the adoption of gamification in developing countries' markets.

Researchers worldwide have worked on influencing factors leading to the acceptance or rejection of a particular technology and have attempted to model behavioural intention factors. However, research seldom works on the adaption of gamification in the Indian market.

The Adoption Behaviour

The theory of reasoned actions discusses how subjective norms and attitudes influence user behaviour. According to innovation diffusion theory, social norms impact adoption choices. According to psychology and economics, there are two categories of social influence: social norms and critical mass. Using a variety of factors, including social influence, Hsu and Lu (2004) investigated user behavioural intentions to play online games. They discovered that social norms directly impacted online games' uptake and claimed that users could feel obligated to play games to stay current with the community. Social influence was also described by Hamari and Koivisto (2013) as a critical incentive for using gamification. They discovered that social elements positively influence how consumers perceive their recognition.

Similarly, a study by Trandis (1980) and other studies have discovered that social norms favour an individual's attitude and conduct (Hsu & Lu 2004). Users behave this way; businesses use this to engage customers and boost sales. By demonstrating that gamification uses social networks to create competition that could ultimately increase user engagement, Maan (2013) recommended social connection as the critical component to bringing about business transformation through gamification and assumed that social norms directly influence intentions and attitudes.

A detailed review of the literature repeatedly gives a few identified factors influencing the consumer's behavioural intentions in various markets: Proneness to deals, Price Consciousness, Enjoyment/ entertainment/ fun component of playing games, and the trust factor on e-commerce.

Proneness to deals, one of the influencing factors, was initially coined by Lichtenstein et al. in 1990. Proneness, in general, can be defined as a response of a deal-prone consumer to promotion techniques and deals. Hackleman and Ducker (1980) described a deal-prone consumer as more likely to find a deal 'impossible to refuse.' Jayasingh and Eze (2012) found in their study on adoption behaviour toward coupons that coupon proneness significantly influences the users' attitude toward using m-coupons. Lichtenstein et al. (1990) found that value consciousness induces redeeming coupon behaviours rather than the Proneness to coupons. At the same time, Jayasingh and Eze (2010) found that coupon proneness significantly impacts users' attitudes. There are mixed results from the Proneness to deals in other research; this study hypothesized that Proneness to deals influences users' intentions.

In 1971, Wells and Tigert coined the term "price consciousness." Price consciousness is the desire to purchase goods at less expensive costs. Price consciousness was defined by Lichtenstein et al. (1990) as "the degree to which the client concentrates on paying a low price." The budget-conscious consumers hunt down coupons and look for several promotions and discounts (Ramaswamy & Srinivasan, 1998). In sales promotions, customer price consciousness is a crucial problem. Price awareness was identified by Sinha and Batra (1999) as the primary motivator for goods purchase decisions.

Similar findings were made by Jayasingh and Eze (2010), who discovered that consumers who place high importance on price are more likely to take advantage of online promotions like m coupons. According to Palazon and Delgado (2009), price consciousness is the crucial driving characteristic that moderates price discounts and premiums at moderate and beneficial levels. As a result, we view pricing sensitivity as an essential quality influencing customers' decision to adopt gamification. So, in the suggested model, we postulated that price consciousness affects consumers' intentions.

Trust in e-commerce is the idea that web users will carry out specific tasks after consumers' confidence (Gefen, 2002). According to many researchers, Trust in e-commerce is essential to e-commerce and is thus crucial for a successful business (Morgan & Hunt, 1994) provided the measurement scale for the Trust construct in 2000, and they discovered that Trust significantly influences people's inclinations to utilize e-commerce. Similarly, Sungwoo (2010) discovered a favourable relationship between consumer intentions to use mobile commerce and Trust. Because users are required to provide their personal information as part of the process, which the various organizations may misuse, Trust is essential in gamification.

Doubts and concerns about credit card fraud impact the trust element of gamification. Wu and Liu (2007) discovered that while Trust has little direct influence on intention, it significantly affects the player's attitude. According to certain studies, people who trust an online gaming website will find the site's game information credible (Wu & Liu 2007). Previous research revealed that the trust factor influences attitudes and intentions. Fishbein and Azen (1980), who argued that Trust mediates the relationship between consumer attitude and adoption of online banking, also hinted at the relationship between attitude and Trust. According to Wu and Liu's 2007 research, Trust impacted intentions indirectly through attitude but not directly through intentions. Therefore, based on previous research, the study hypothesized Trust impacts intentions through attitude.

Not only in offline activities but also in an online context, enjoyment is crucial (Blakney & Sekely 1994, Forman & Sriram 1991). It can be characterized as the extent to which engaging in an activity is thought to bring pleasure and delight, excluding the effects on performance (Venkatesh, 2000). Childer, Carr, and Carson provided the metrics to gauge enjoyment (2001). People are typically driven by their intrinsic interests (Huang and Cappel, 2005; Kim et al., 2002; Xi & Hamari, 2019). Previous studies have demonstrated that users motivated by interest or other intrinsic factors are likelier to engage in this behaviour (Deci and Ryan, 1985). Results from the studies on how enjoyment affects planned behaviour have been inconsistent.

Jarvanpaa and Todd (1997) discovered a significant effect, although Igbaria et al. (1995) found no significant effect on system use; Sungwoo (2010) discovered that enjoyment is positively associated with the intent to utilize m-commerce. Wu and Liu (2007) discovered that enjoyment was the best predictor of playing intentions in online gaming.

Previous research indicated that consumers' behavioural intentions were directly impacted by their level of enjoyment (Dick and Basu, 1994). As a result, the emotional response of pleasure is more likely to increase the consumer's intrinsic incentive to play more (Huang & Cappel 2005, Kim et al.) In addition, Lee et al. (2005) discovered that enjoyment affects behaviour directly and indirectly through attitude. Wu and Liu (2007) discovered that players' attitudes are significantly impacted by how much fun they have when playing online.

Despite the exorbitant research on the various motivational and influential factors, gaps exist in explaining the behavioural intentions for adopting gamification (Helmefalk & Marcusson, 2018). Therefore, this study aimed to find the influence of various factors which lead to behavioural intentions.

Research Design

This study aims to find the impact of identified factors on consumer behavioural intentions toward adopting gamification. The area selected for study is Bangalore, the Silicon Valley of India, with a sample size of 400. The questionnaire is designed to investigate significant factors retrieved from various pieces of literature that affect customers' behavioural intentions for the adoption of gamification by factor analyses. The identified factors are: (personal perspective (PP), Usefulness (use), Price Consciousness (PC), Perceived Critical Mass (PCM), Flow Experience (flow), Easy-to-use (easy), Awareness (Aware), personal Innovativeness (Inn).

The box plot method was used to identify outliers in the data, and 33 examples were found to be outliers. Three hundred sixty-seven replies were ultimately taken into account throughout the data analysis. To ensure content validity, the study's items were designed using a variety of constructs, most of which were borrowed from earlier studies on various information technology adoption behaviours. Using the correlation matrix method, construct validity is carried out together with convergent and discriminant validity. We examined the normalcy of the data. All of the data's items' skewnesses fall within the permitted range of -1 to +1. Most of the objects' kurtosis values range from -1 to +1. The data is almost symmetrical and typically distributed. The respondents' profiles are shown in table 1.

Table 1 Profile of Respondents
Measure Scale Frequency (N)
Total = 367
Percentage (%)
Gender Male 180 49
Female 187 51
Age = 24 161 43.9
25-35 127 34.6
36-45 47 12.8
46 – 55 17 4.6
55 and above 15 4.1
Highest Education Graduation 114 31.1
Post-graduation 235 64
Other 16 4.4
Occupation IT Professional 64 17.4
Self- employed 30 8.2
Student 182 49.6
Homemaker 28 7.6
Other 63 17.2
Family income per annum <5 lacs 89 24.3
6-15 lacs 134 36.5
16-25 lacs 59 16.1
26-35 lacs 42 11.4
>35 lacs 43 11.7

Data Analysis

The association or relationship between independent and dependent variables, or between two or more independent variables, is quantified by correlation analysis. The correlation between the independent and dependent variables is shown in Table 2. All correlation coefficient values are more than 0.3, indicating a positive association between behavioural intentions and those values, and all correlation coefficient values are significant at the 5% significance level.

Table 2 Correlation between Independent and Dependent Variables
  MPP MUse MPC MPCM MFlow MEasy MAware MInn MBI
PP Pearson Correlation 1 .480** .509** .560** .400** .384** .354** .362** .739**
Sig. (2-tailed)   .000 .000 .000 .000 .000 .000 .000 .000
N 367 367 367 367 367 367 367 367 367
Use Pearson Correlation   1 .498** .344** .409** .593** .510** .350** .587**
Sig. (2-tailed)     .000 .000 .000 .000 .000 .000 .000
N   367 367 367 367 367 367 367 367
PC Pearson Correlation     1 .457** .396** .477** .321** .432** .624**
Sig. (2-tailed)       .000 .000 .000 .000 .000 .000
N     367 367 367 367 367 367 367
PCM Pearson Correlation       1 .398** .271** .336** .420** .597**
Sig. (2-tailed)         .000 .000 .000 .000 .000
N       367 367 367 367 367 367
Flow Pearson Correlation         1 .460** .349** .327** .344**
Sig. (2-tailed)           .000 .000 .000 .000
N         367 367 367 367 367
Easy Pearson Correlation           1 .393** .373** .451**
Sig. (2-tailed)             .000 .000 .000
N           367 367 367 367
Aware Pearson Correlation             1 .290** .385**
Sig. (2-tailed)               .000 .000
N             367 367 367
Inn Pearson Correlation               1 .360**
Sig. (2-tailed)                 .000
N               367 367
BI Pearson Correlation                 1
Sig. (2-tailed)                  
N                 367

The next step for the analysis is linear regression. The forward method is used for the multiple regression analysis.

BI = α + βpp (PP) + βuse (Use) + βPC (PC) + βeasy (Easy) + βPCM (PCM) + βFlow (Flow) +βaware (Aware) + βInn (Inn)

We verified each regression model's presumptions to validate it and discovered that the data did not contradict any of the presumptions made by the research at hand. Therefore, we accept this regression model, and tables 3-5 show the outcomes.

Table 3 demonstrates the R-value of 0.851, indicating a strong association between the variables. According to the R2 and modified R2 values of 0.725 and 0.719, these independent variables account for 71.9% of the variance in behavioural intentions, while other unidentified factors account for the remaining 28.1%. The ANOVA table is shown in Table 4, and the p-value was discovered to be 0.000, which is significant at a level of 5%. It demonstrates the regression model's statistical significance and strong data fit.

Table 3 Regression Analysis – Model Summary
R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
0.851a 0.725 0.719 0.53031460 2.303
Table 4 Anovaa
Model Sum of Squares Df Mean Square F Sig.
Regression 265.318 8 33.165 117.93 0.000b
Residual 100.682 358 0.281    
Total 366 366      

The p-values for every factor are displayed in Table 5. Except for flow (0.177), which has p values higher than the accepted significance level of 0.05, all p values for independent variables are significant at the 0.05 significance level.

Table 5 Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF
  (Constant) 1.002E-013 0.028   0.000 1.000    
PP 0.468 0.028 0.468 16.872 0.000 1.000 1.000
Use 0.372 0.028 0.372 13.405 0.000 1.000 1.000
PC 0.352 0.028 0.352 12.706 0.000 1.000 1.000
PCM 0.429 0.028 0.429 15.490 0.000 1.000 1.000
Flow 0.038 0.028 0.038 1.353 0.177 1.000 1.000
Easy 0.200 0.028 0.200 7.226 0.000 1.000 1.000
Aware 0.114 0.028 0.114 4.098 0.000 1.000 1.000
Inn 0.072 0.028 0.072 2.614 0.009 1.000 1.000


This study uses gamification to investigate how the identified characteristics affect the efficacy of price reductions and premiums at various degrees of promotional benefits. The reactions of consumers who are more and less concerned with pricing to price reductions and bonuses across low and high benefit levels have been studied. The findings also indicate that more price- and value-conscious consumers are likelier to adopt gamification. This backs up earlier work by Palazon and Delgado (2009). The findings of this study demonstrate that regardless of the offered discount size, incredibly price-conscious customers are inclined to look for a better deal. Given this propensity, providing a low-price guarantee would be an excellent way to stop buyers from searching for the lowest prices.

The current study adds to the body of research on gamification by providing insights into the elements influencing consumer usage. The findings have significant ramifications for both current and future research. The study's findings prove that utilizing a regression model to analyze consumer perceptions of gamification adoption is reasonable. According to the study, there is a correlation between Behavioural intents and Usefulness, Price Consciousness, Ease of Use, Perceived Critical Mass, Flow Experience, Awareness, and Personal Innovativeness.

This study supports earlier research, where usefulness and ease of use were discovered to be essential antecedents for behavioural intentions toward information technology (e.g., Hsu, Wang, and Wen, 2006; Jayasingh and Eze, 2009; Venkatesh and Davis, 2000; Lallmahamood 2007; Safeena, Hundewale and Kamani 2011). This study aligns with earlier studies that revealed a strong impact of price consciousness on consumers' behavioural intentions to utilize mobile coupons in mobile marketing (e.g., Palazon and Delgado 2009; Dutta and Biswas 2005). Similarly, studies on behavioural intentions for adopting information technology (e.g., Hsu, Wang, and Wen 2006; Amoroso and Donald 2014; Meijer and Shliapnikov) supported results from perceived critical mass and awareness.

However, earlier research suggested that the flow would significantly impact how people played online games. This study suggests that consumers' behavioural intentions to adopt gamification are not significantly influenced by flow; nonetheless, Pan (2011) discovered that although flow does not directly affect users' adoption of mobile games, it may do so indirectly through other driving variables. Several interesting managerial implications can be derived. The significant influence indicates that a segmentation approach might effectively reach the customers to adopt gamification. Underused gamification may cause potential users to be unaware of the technique and resources required. Furthermore, adopting user-friendly interfaces can facilitate people's familiarity with gamification applications and help them develop positive, easy-to-use beliefs in gamification.


According to the usefulness of the data, a consumer's decision to use gamification is influenced by an extrinsic motivation component; as a result, marketers should develop gamification strategies that appeal to and are advantageous to customers from all different demographic groups. To do this, pop-ups could be incorporated into the game's design. The pop-ups can provide information on brand-new goods, special offers, or freebies like cheap movie tickets or free entry to a theme park. The validated model offers managers a useful framework for determining the likelihood of success for m-coupon introductions and designing proactive mobile coupon campaigns. The study's findings demonstrated several variables significantly influencing customers' behaviour while utilizing gamification and can be applied to future research.


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Received: 28-Sep-2023, Manuscript No. AMSJ-23-14048; Editor assigned: 29-Sep-2023, PreQC No. AMSJ-23-14048(PQ); Reviewed: 27-Oct-2023, QC No. AMSJ-23-14048; Revised: 04-Jan-2024, Manuscript No. AMSJ-23-14048(R); Published: 06-Feb-2024

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