Does Experience Matter? Unraveling the Drivers of Expert and Non-Expert Mobile Consumers
Abstract
:1. Introduction
2. Literature Review
3. Hypotheses Development
4. Methodology
4.1. Measurement and Respondents’ Profile
4.2. Data Analysis
5. Results
6. Discussion
7. Practical and Theoretical Implications
7.1. Practical Implications
- Omnichannel Convergence and Personalized Experiences: By integrating seamless experiences across various channels, marketers can harness the power of omnichannel strategies in m-commerce applications to create more engaging and personalized shopping journeys. Tailoring these experiences to align with the user’s proficiency in navigating m-commerce platforms can lead to enhanced user engagement, satisfaction, and, ultimately, loyalty. For instance, novice users could be guided with more intuitive and educational content, while experienced shoppers might appreciate advanced filtering and search capabilities.
- CRM Customization: It is vital for Customer Relationship Management (CRM) systems to adapt to the diverse spectrum of consumer expertise in m-commerce. By understanding and segmenting users based on their m-shopping experience, retailers can provide more personalized services, from product recommendations to experience-based promotional offerings. This level of customization not only improves the user experience but also strengthens the consumer-brand relationship.
- Emphasis on Entertainment Value: M-commerce platforms should not only serve as transactional interfaces but also as engaging and entertaining environments. Features that integrate entertainment—such as gamification, interactive content, and personalized storytelling—can transform routine shopping into enjoyable experiences, thus enhancing consumer engagement and cultivating a positive brand image.
- Security Features and Trust Building: For both less and more experienced m-shoppers, our findings highlight that security is paramount in earning consumer trust, especially in the digital shopping realm where concerns about data privacy and transaction safety prevail. M-retailers need to invest in robust security mechanisms and communicate these features effectively to consumers, highlighting their commitment to safeguarding user data and ensuring transactional integrity. This approach can significantly alleviate consumer apprehensions and bolster trust and confidence in the m-commerce platform.
- Resource Allocation for Trust Building: Trust is the cornerstone of successful m-commerce ventures. Marketing strategies should, therefore, prioritize establishing and nurturing trust. This includes not just investing in advanced security technologies but also in transparent communication and reliable customer service. These efforts should aim to create a trustworthy brand image that resonates with both novice and experienced shoppers, encouraging repeat business and fostering brand loyalty.
- Customer Retention Strategies: The research underscores the pivotal role of customer retention in m-commerce success. Strategies should be multifaceted, addressing the specific needs and preferences of different consumer segments. By leveraging data analytics to understand consumer behavior, retailers can deliver personalized and relevant content and offers. Effective use of push notifications and loyalty programs can act as key tools in maintaining ongoing engagement, enhancing the consumer’s shopping experience and, thus, retaining them in the long term.
7.2. Theoretical Implications
- Advanced Model with PLS-SEM and ANN: The hybrid approach using partial least squares structural equation modeling (PLS-SEM) and artificial neural networks (ANNs) presents a novel method for understanding consumer behavior in m-commerce. This approach recognizes the importance of consumers’ experience levels in shaping perceptions.
- Consumer Segmentation Based on Experience: By examining less and more experienced m-shoppers separately, the research contributes to the literature by highlighting the importance of segmenting consumers based on their technology familiarity. This segmentation enables more accurate and efficient marketing strategies.
- Identification of Key Drivers of M-Commerce Attitude: The study identifies trust and usefulness as significant antecedents of m-commerce attitude for both less and more experienced users. This insight adds to the existing literature by emphasizing the pivotal role of these factors in shaping consumers’ attitudes.
- Multi-Sample Modeling Validation: The research extends m-commerce knowledge by employing a multi-sample methodology. This approach, validated through modeling, provides a foundation for developing targeted marketing tactics that consider the psychological dimensions of consumer–technology interactions.
8. Limitations and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variable | Less Experienced m-Shoppers (N = 214) (%) | More Experienced m-Shoppers (N = 158) (%) | |
---|---|---|---|
Gender | Female | 77.6 | 66.5 |
Male | 22.4 | 33.5 | |
Employment status | Student | 61.2 | 43.0 |
Employed | 36.9 | 51.9 | |
Searching for a job | 1.9 | 5.1 | |
Favorite m-commerce apps | Food-and-delivery apps | 25.7 | 20.9 |
Fashion apps | 48.6 | 43.0 | |
Grocery shopping apps | 3.7 | 8.9 | |
Beauty products apps | 15.9 | 13.3 | |
Other | 6.1 | 13.9 | |
Personal monthly income (Euro/month) | <500 | 58.9 | 41.1 |
501–1000 | 30.4 | 31.0 | |
1001–1500 | 7.9 | 16.5 | |
>1501 | 2.8 | 10.8 |
Items (Sources) | Less Experienced | More Experienced | ||
---|---|---|---|---|
Loadings | α/CR/AVE | Loadings | α/CR/AVE | |
M-Commerce Attitude [2,38] | 0.867/0.876/0.790 | 0.855/0.857/0.775 | ||
AT1: “Overall, I feel favorable toward m-commerce.” | 0.875 *** | 0.869 *** | ||
AT2: “Using m-commerce seems like a good idea to me.” | 0.892 *** | 0.892 *** | ||
AT3: “I feel positive about shopping on m-commerce apps.” | 0.899 *** | 0.880 *** | ||
Enjoyment [26,33,35] | 0.784/0.796/0.699 | 0.736/0.764/0.652 | ||
E1: “M-commerce is fun.” | 0.815 *** | 0.812 *** | ||
E2: “M-commerce is enjoyable.” | 0.888 *** | 0.855 *** | ||
E3: “M-commerce is very entertaining.” | 0.802 *** | 0.751 *** | ||
Satisfaction [24,48] | 0.853/0.856/0.774 | 0.858/0.860/0.778 | ||
SAT1: “I am very satisfied that m-commerce apps meet my requirements.” | 0.910 *** | 0.892 *** | ||
SAT2: “My interaction with mobile shopping is very satisfying. ” | 0.905 *** | 0.884 *** | ||
SAT3: “Overall, I am satisfied with my experience with m-commerce.” | 0.822 *** | 0.870 *** | ||
Subjective Norms [2,23,26] | 0.720/0.758/0.778 | 0.764/0.765/0.809 | ||
SN1: “Important people in my life encourage me to adopt m-commerce.” | 0.848 *** | 0.897 *** | ||
SN2: “People who are important to me support me to use mobile commerce.” | 0.916 *** | 0.902 *** | ||
Trust [2,48] | 0.858/0.858/0.704 | 0.834/0.837/0.670 | ||
TR1: “I believe m-commerce apps are trustworthy.” | 0.891 *** | 0.881 *** | ||
TR2: “The information provided on m-commerce apps is reliable.” | 0.775 *** | 0.731 *** | ||
TR3: “I felt secure in ordering and receiving orders through m-commerce apps.” | 0.882 *** | 0.855 *** | ||
TR4: “The information provided by mobile shopping apps is reliable.” | 0.803 *** | 0.799 *** | ||
Usefulness [22,23] | 0.806/0.807/0.722 | 0.797/0.803/0.710 | ||
U1: “I would find m-commerce useful in my daily life.” | 0.810 *** | 0.864 *** | ||
U2: “Using m-commerce would increase my productivity.” | 0.866 *** | 0.836 *** | ||
U3: “Using m-commerce would help me accomplish things more quickly.” | 0.872 *** | 0.828 *** |
Sample | Variables | Fornell–Larcker Criterion | Heterotrait–Monotrait Ratio (HTMT) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 1 | 2 | 3 | 4 | 5 | 6 | ||
Less experienced m-shoppers | 1. Enjoyment | 0.836 | |||||||||||
2. M-commerce Attitude | 0.596 | 0.889 | 0.720 | ||||||||||
3. Satisfaction | 0.662 | 0.739 | 0.880 | 0.807 | 0.859 | ||||||||
4. Subjective Norm | 0.516 | 0.481 | 0.582 | 0.882 | 0.674 | 0.599 | 0.731 | ||||||
5. Trust | 0.578 | 0.772 | 0.749 | 0.497 | 0.839 | 0.702 | 0.894 | 0.874 | 0.623 | ||||
6. Usefulness | 0.501 | 0.707 | 0.615 | 0.499 | 0.654 | 0.849 | 0.617 | 0.845 | 0.744 | 0.645 | 0.786 | ||
More experienced m-shoppers | 1. Enjoyment | 0.808 | |||||||||||
2. M-commerce Attitude | 0.622 | 0.880 | 0.766 | ||||||||||
3. Satisfaction | 0.674 | 0.760 | 0.882 | 0.828 | 0.884 | ||||||||
4. Subjective Norm | 0.474 | 0.379 | 0.494 | 0.900 | 0.628 | 0.465 | 0.605 | ||||||
5. Trust | 0.620 | 0.763 | 0.739 | 0.433 | 0.819 | 0.788 | 0.896 | 0.868 | 0.541 | ||||
6. Usefulness | 0.616 | 0.728 | 0.749 | 0.475 | 0.625 | 0.843 | 0.792 | 0.876 | 0.895 | 0.612 | 0.756 |
Sample | Hypotheses | Coeff | t-Test | CI 1 | f2 | Sig. | Result | |
---|---|---|---|---|---|---|---|---|
Less experienced m-shoppers | H1a | Enjoyment→Attitude | 0.098 | 2.135 | 0.007–0.189 | 0.017 | 0.033 * | Supported |
H2a | Enjoyment→Satisfaction | 0.265 | 4.777 | 0.153–0.372 | 0.126 | 0.000 *** | Supported | |
H3a | Usefulness→Attitude | 0.289 | 4.598 | 0.167–0.413 | 0.145 | 0.000 *** | Supported | |
H4a | Usefulness→Satisfaction | 0.109 | 1.479 | −0.031–0.256 | 0.019 | 0.139 n.s. | Rejected | |
H5a | Subjective Norm→Attitude | −0.037 | 0.674 | −0.142–0.073 | 0.003 | 0.500 n.s. | Rejected | |
H6a | Subjective Norm→Satisfaction | 0.173 | 3.302 | 0.07–0.276 | 0.059 | 0.001 *** | Supported | |
H7a | Trust→Attitude | 0.356 | 4.218 | 0.183–0.508 | 0.161 | 0.000 *** | Supported | |
H8a | Trust→Satisfaction | 0.438 | 6.661 | 0.303–0.558 | 0.278 | 0.000 *** | Supported | |
H9a | Satisfaction→Attitude | 0.251 | 2.510 | 0.07–0.460 | 0.070 | 0.012 * | Supported | |
More experienced m-shoppers | H1b | Enjoyment→Attitude | 0.076 | 1.111 | −0.062–0.202 | 0.009 | 0.267 n.s. | Rejected |
H2b | Enjoyment→Satisfaction | 0.186 | 2.412 | 0.025–0.33 | 0.060 | 0.016 * | Supported | |
H3b | Usefulness→Attitude | 0.301 | 4.013 | 0.145–0.435 | 0.127 | 0.000 *** | Supported | |
H4b | Usefulness→Satisfaction | 0.378 | 4.939 | 0.222–0.521 | 0.245 | 0.000 *** | Supported | |
H5b | Subjective Norm→Attitude | −0.086 | 1.498 | −0.187–0.039 | 0.018 | 0.134 n.s. | Rejected | |
H6b | Subjective Norm→Satisfaction | 0.071 | 1.242 | −0.03–0.192 | 0.012 | 0.214 n.s. | Rejected | |
H7b | Trust→Attitude | 0.389 | 4.076 | 0.189–0.563 | 0.221 | 0.000 *** | Supported | |
H8b | Trust→Satisfaction | 0.357 | 4.185 | 0.192–0.531 | 0.225 | 0.000 *** | Supported | |
H9b | Satisfaction→Attitude | 0.238 | 2.192 | 0.048–0.475 | 0.057 | 0.028 * | Supported |
ANNs | Less Experienced | More Experienced | ||
---|---|---|---|---|
R2 = 0.7041 | R2 = 0.6952 | |||
Training | Testing | Training | Testing | |
RMSE | RMSE | RMSE | RMSE | |
1 | 0.0609 | 0.0559 | 0.0817 | 0.0615 |
2 | 0.0699 | 0.0650 | 0.0875 | 0.1001 |
3 | 0.0617 | 0.0494 | 0.0625 | 0.0497 |
4 | 0.0668 | 0.0559 | 0.0932 | 0.1193 |
5 | 0.0576 | 0.0677 | 0.0623 | 0.0577 |
6 | 0.0625 | 0.0452 | 0.0611 | 0.0484 |
7 | 0.0600 | 0.0713 | 0.0579 | 0.0856 |
8 | 0.0614 | 0.0881 | 0.0592 | 0.0614 |
9 | 0.0600 | 0.0560 | 0.0620 | 0.0608 |
10 | 0.0610 | 0.0529 | 0.0603 | 0.0658 |
Average | 0.0622 | 0.0607 | 0.0688 | 0.0710 |
St. dev. | 0.0036 | 0.0126 | 0.0133 | 0.0232 |
ANN | Less Experienced | More Experienced | |||||
---|---|---|---|---|---|---|---|
Enjoyment | Satisfaction | Trust | Usefulness | Usefulness | Trust | Satisfaction | |
1 | 0.2001 | 0.2901 | 0.3227 | 0.1871 | 0.3387 | 0.4346 | 0.2267 |
2 | 0.1386 | 0.3220 | 0.2459 | 0.2935 | 0.2411 | 0.5815 | 0.1774 |
3 | 0.1876 | 0.2735 | 0.3128 | 0.2261 | 0.2350 | 0.5128 | 0.0000 |
4 | 0.1730 | 0.2263 | 0.4188 | 0.1819 | 0.2724 | 0.4320 | 0.2956 |
5 | 0.1522 | 0.2507 | 0.3348 | 0.2623 | 0.2518 | 0.4798 | 0.2684 |
6 | 0.1389 | 0.2402 | 0.2805 | 0.3404 | 0.2823 | 0.5188 | 0.1988 |
7 | 0.2073 | 0.1860 | 0.3185 | 0.2881 | 0.3778 | 0.3831 | 0.2391 |
8 | 0.0627 | 0.2572 | 0.4012 | 0.2789 | 0.3555 | 0.4670 | 0.1775 |
9 | 0.1094 | 0.2009 | 0.4485 | 0.2412 | 0.2819 | 0.4980 | 0.2201 |
10 | 0.0894 | 0.2429 | 0.3352 | 0.3325 | 0.2539 | 0.5503 | 0.1958 |
Average | 0.1459 | 0.2490 | 0.3419 | 0.2632 | 0.2891 | 0.4858 | 0.1999 |
Normalized importance | 42.44% | 71.66% | 95.88% | 75.61% | 61.27% | 100.00% | 47.47% |
Sample | Examined Driver of Attitude | Path Coefficient | ANN Result—Normalized Importance | PLS-SEM Ranking | ANN Ranking | Conclusion |
---|---|---|---|---|---|---|
Less experienced m-shoppers | Enjoyment | 0.098 | 42.44% | 4 | 4 | Matched |
Usefulness | 0.289 | 75.61% | 2 | 2 | ||
Trust | 0.356 | 95.88% | 1 | 1 | ||
Satisfaction | 0.251 | 71.66% | 3 | 3 | ||
More experienced m-shoppers | Usefulness | 0.301 | 61.27% | 2 | 2 | Matched |
Trust | 0.389 | 100.00% | 1 | 1 | ||
Satisfaction | 0.238 | 47.47% | 3 | 3 |
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Vinerean, S.; Dabija, D.-C.; Dominici, G. Does Experience Matter? Unraveling the Drivers of Expert and Non-Expert Mobile Consumers. J. Theor. Appl. Electron. Commer. Res. 2024, 19, 958-974. https://doi.org/10.3390/jtaer19020050
Vinerean S, Dabija D-C, Dominici G. Does Experience Matter? Unraveling the Drivers of Expert and Non-Expert Mobile Consumers. Journal of Theoretical and Applied Electronic Commerce Research. 2024; 19(2):958-974. https://doi.org/10.3390/jtaer19020050
Chicago/Turabian StyleVinerean, Simona, Dan-Cristian Dabija, and Gandolfo Dominici. 2024. "Does Experience Matter? Unraveling the Drivers of Expert and Non-Expert Mobile Consumers" Journal of Theoretical and Applied Electronic Commerce Research 19, no. 2: 958-974. https://doi.org/10.3390/jtaer19020050