Review Article: 2023 Vol: 27 Issue: 6
Mature Customers: What Drives them for Online Shopping
Prashant Kumar Rai, Rajiv Gandhi Institute of Petroleum Technology, Amethi
Sanjay Kumar Kar, Rajiv Gandhi Institute of Petroleum Technology, Amethi
Citation Information: Kumar Rai, P., & Kumar Kar, S. (2023). Mature customers: what drives them for online shopping. Academy of Marketing Studies Journal, 27(6), 1-12.
Abstract
The COVID-19 pandemic has made the internet the major channel for shopping across countries. The advent of the internet and its rapid penetration in developing economies like India has also significantly increased online shopping. This study examined the various factors that affect the online purchase intention among customers whose age is 50 years and above, who constitute a significant proportion of the consumer market in India. The study uses Partial least square modeling to validate the research model. Findings suggest that the “performance expectancy, effort expectancy, facilitating conditions, social influence” are significant factors impacting online shopping.
Keywords
Mature customers, Shopping Behavior, UTUAT, Partial Least Square, Online Shopping.
Introduction
The purchase intention is the aspiration to acquire a specific product or service within a specified period. The consumer's willingness to buy from an e-commerce company also affects online purchasing intention (Kasuma et al., 2020). Consumers who are knowledgeable and familiar with e-commerce businesses are more likely to visit an online shopping site with the intention of purchasing (Lian & Yen, 2014; Naseri, 2021; Qalati et al., 2021). Knowledge of e-commerce enables consumers to comprehend what is occurring, why it is occurring, and what will occur next (Gefen & Straub, 2004; Pham et al., 2020; Rafqi Ilhamalimy & Ali, 2021).Consumers' intention to purchase indicates what they believe they will purchase in the future to meet their needs and desires (Tandon et al., 2016). Consumers' objectives, however, can shift due to unforeseen circumstances. Therefore, businesses must take appropriate measures to assure that their products and services are looked positively by their consumers. Consumers' purchasing intentions are regularly determined by the marketing strategy, their attitudes, and their level of interest in the deals made by a particular business (Kautish & Sharma, 2019; Singh & Söderlund, 2020). As a result, it is self-evident that online businesses must meet consumer requirements to boost purchasing intentions (Dewi et al., 2020), as organizations must adhere to consumer requirements and standards. According to (Pentz et al., 2020; Wang et al., 2020; Xiao et al., 2019) the inclination for online shopping is a significant predictor of shopping experience. Thus, for online shopping companies to attain their actual purchase goal, they must ensure that their consumers have considerable intention to buy their products. Customers' true purchasing behavior is binary in nature, as they are either obligated or not obligated to purchase the product (Lim et al., 2016).
While intent is a necessary condition for purchase, a customer's intention does not always translate into action (Miandari et al., 2021). Expectations, on the other hand, are insufficient to anticipate actual behavior, such as a transaction (Bhattacherjee & Sanford, 2009). Thus, the term "gap between intention and behavior" refers to the difference between behavioral intentions and actual behavior (Hamed AL-Shukri Udayanan, 2019). The disparities are attributed to the diverse characteristics of consumer attitudes (Bhattacherjee & Sanford, 2009). Because purchasing can only be quantified through actual sales figures, consumer intention research is viewed as an acceptable method for determining customers' attitudes and feelings toward a potential purchase (Miandari et al., 2021). Consequently, this study focuses on the purchasing intentions of rising reality. Purchase intentions in an online environment can differ significantly from those in more traditional sales channels, such as brick-and-mortar stores (Tandon et al., 2018). As such, it is critical to understand consumers' shopping intentions in e-commerce settings to accomplish the study's objective. This can be explained by the fact that navigating the internet on a computer or other smart device requires a certain level of knowledge or resources (Xiao et al., 2019).
Literature Review
Various research studies examined elements influencing consumers’ online shopping behavior. On contrast fewer studies have been performed on older adults shopping behavior. A study by (Lian & Yen, 2014) included three dimensions in their study on older adults. Drivers, barriers, and intention which were studied in Chinese context. The study concluded that the major factors for online shopping in the context of older adults are social influence and performance expectation. The major barriers according to the study were tradition, value, and risk. The study also indicated that the gender had no significant moderating effect and younger online consumers have more drivers and less barriers.
(Reisenwitz et al., 2007) studied the impact of risk aversion, nostalgia, proneness and innovativeness on older adult’s internet usage in the USA. The study revealed that mature customers having enhanced online experience showed lower risk aversion towards internet than other older adults. The adults with nostalgia showed less access and internet usage as compared to their counterparts. Innovativeness also impacted older adult’s internet usage, their frequency of usage, satisfaction, comfort, online purchase, and experience. The study concludes that older adults are viable segment for internet companies. The time spent on internet, frequency of usage will affect the older adult’s evaluation of risks involved with internet usage. Kwon et al. (Kwon & Noh, 2010) investigated the purchase intention, risks and benefits perceived by older adults while shopping online for apparels and the role of age and experience. The study was conducted among mature American adults born before 1964. The study found that the intention to shop online for apparel was dependent on consumers perception of price discount, financial risk, and product benefits. Also, the recognition of risk and advantages will be affected by their prior electronic shopping experiences which was also concluded by (Reisenwitz et al., 2007). But this study concluded that general online experience and age had limited influence on older consumer purchase intentions and perceptions.
(Weaver, 2006) studied the participation of older adults and their attitude in ecommerce activities by modifying the TAM model. It tested the influence of usefulness, ease of use and trust on e-commerce usage. It found that trust and usefulness had a direct positive effect on usage but surprisingly ease of use as a factor did not contribute significantly to e-commerce participation. The deviated ease of use result was stated to be caused by frequent shoppers encountering difficulties in shopping online. The findings suggested that perceived ease of use had a direct influence on perceived usefulness. Also, users trust on website and website usefulness positively affected user behavior. The study indicates that the TAM model is beneficial in investigating online shopping usage and behavior, but the relationship between variables is distinct in the case of older shoppers.
(Ryu et al., 2009) studied the behavior of older adults on user-created video content services based on TAM and integrated variables. The study integrated TAM, motivation theory, innovation diffusion theory, and used elderly specific constructs- “perceived user resource, prior related experience, and anxiety related with computer. Physical and psycho-social age constructs- perceived physical condition and life course events. The study was done with adults of over 50 years of age, and findings suggest that perceived benefits, enjoyment, and ease of participation affects behavior of participant. Older adult specific variables which act as critical factors include life span events, computer anxiety, available resources, previous experience, self-assessments, and health condition.
(Pfeil et al., 2009) compared the social capital of teenagers (13 to 19 years age) with matured adults (over 60 years) on social networking website Myspace. Web crawlers were used to collect data from Myspace users profile pages. Content analysis was employed to find the variances in the online activities of the two groups. The study found that the teenagers had more social capital with larger network of friends within their age group as compared to older adults. Also, teenagers used diverse media to share with their friends; they expressed themselves more frequently as compared to their older counterparts. It has been observed that many studies were done on older adults and online behavior but fewer studies on e-commerce platforms. Consumer behavior varies from developed to emerging markets especially with age and gender differences. Therefore, this study adds to the present literature and tries to fill gaps by studying the drivers of online shopping on mature population in an emerging market with reference to online shopping.
Theoretical Background
Unified Theory of Acceptance and Use of Technology
Many theoretical models for assessing technological behavior have been proposed in recent past. Models help to examine and predict the use and acceptance of technology. Some models have been particularly successful such as technology acceptance model (TAM) and “unified theory of acceptance and use of technology (UTAUT)” (Venkatesh et al., 2003). Till recently some studies have applied these models on older adults (Lian & Yen, 2014; Magsamen-Conrad et al., 2015; Nägle & Schmidt, 2012). Understanding user’s intention with respect to technology acceptance is a critical area of research in information management studies.
Frequently used major theoretical models are - the TAM model, the “Theory of Planned Behavior (TPB), Diffusion of Innovation Theory, the Theory of Reasoned Action (TRA) and DeLone and McLean’s IS” success model. The UTAUT is combination of these theoretical models which are related to technology behavior and acceptance. It has evolved from the theory of planned behavior which states that indulging in a behavior is based on prior intention. The intention comprises of norms, perceived control over behavior and the attitude of person. The focus of UTAUT is on technology usage and the behavior involved in using systems. The behavioral intention is predicted by four components i.e., “performance expectancy, effort expectancy, social influence and facilitating conditions”. The performance expectancy is the potential user’s expectation of likely utility of the system, how can it give them perceived benefits by using the system. For example, a user’s performance expectancy from WhatsApp will be its ability to connect with his friends instantly using the internet all over the world. Effort expectancy relates to the effort made by the user to use the system. For example, how hard it will be to learn WhatsApp. Social influence refers to the user’s perception about what significant others will think of him/her if he/she uses a particular system. For example, a friend may think it’s good that his friend uses WhatsApp.
Facilitating conditions, decide whether the user thinks it is possible to show the actual behavior. For example, are the essential supporting devices available to set up WhatsApp. These four key constructs are mediated by age, gender, voluntariness of use (crucial in work environment), and experience. These mediators impact behavior and intention Figure 1.
Figure 1: Unified Theory Of Acceptance And Use Of Technology (Utaut) (Venkatesh Et Al., 2003).
Research Method and Hypothesis
The major drivers of online shopping intention based on UTAUT model are performance expectation, effort expectation, facilitating conditions and social influence. The factors will positively affect the online shopping intention of mature customers. The model is illustrated in Figure 2.
Figure 2:Research Model.
Research Hypotheses
Numerous studies on online shopping have been done using the UTAUT model. Various studies found that users' intentions toward ICT with respect to in an e-government setup are influenced by factors like “performance expectations, effort expectations, social influence, and facilitating conditions”. In their research of user intentions for online shopping, (Hamed AL-Shukri Udayanan, 2019) included the ‘‘subjective task value" concept. They discovered that expected performance and expected effort are important variables. Authors (Kasuma et al., 2020) studied on factors influencing intentions of customers, and (Qingfei et al., 2008) studied acceptance of mobile commerce among shoppers, all of which supported the importance of the four important factors in the background of online shopping. To examine users' intentions toward online shopping in India, Tandon (Tandon et al., 2016) combined the UTAUT and UTAUT-2 models and discovered that with perhaps the deviation of effort expectation, the other three drivers had substantial impacts. Based on the preceding explanation, the four UTAUT drivers provide sufficient evaluative power in an e-commerce setting. (Wagner et al., 2010) discovered that UTAUT may be used to study older individuals' acceptance of computers. Pan and Jordan-Marsh (Pan & Jordan-Marsh, 2010) also corroborate the explanatory power of utilizing UTAUT to examine Chinese older persons' Internet use intentions. Finally, UTAUT has explanatory value in the context of comprehending older persons' adoption of assistive social agent (Heerink et al., 2010). As a result, UTAUT will be useful in determining older individuals' acceptability of internet purchasing. We propose the following hypothesis to understand the drivers of e-commerce or online shopping by mature customers based on the preceding studies.
Consumers may now engage with businesses in novel ways as a result of advancements in internet technology and associated applications (Hendriyani & Raharja, 2018). The same proponent may not be applied in the case of mature consumers. Thus, organizations are increasingly investing in customer-centric online resource technologies in order to expand their market share in the online market (Wirtz et al., 2013). Performance expectation refers to how individuals relate the benefits of technological progress to the output they receive. Mature individuals mostly view a technology that achieves their desired objectives as having higher performance outcomes, if it allows them to utilize innovation (Laukkanen et al., 2007).
Performance expectations are a notion based on the premise that using technology benefits the customer and results in performance improvements. According to prior research, performance expectation is the greatest predictor of behavioral intention to use mobile apps. As a result, if customers discover innovations and value in social networking apps, they will be more likely to continue using and purchasing social networking services. Thus, the following hypothesis is formulated:
H1: Performance expectations are significant drivers of online shopping for mature customers.
Mature customers assess the performance expectations of social networking apps based on information sharing and communication messages before utilizing them (Mattila et al., 2003). In terms of various social networking apps, this set of customers perceives the advantage differently (Law & Ng, 2016). Social networking apps, for example, should be able to assist users in sharing information, joining a particular interest group, and establishing connections.
Effort expectancy has been defined as the degree of ease with which a user uses any technology. Various researchers examined the effect of effort expectancy on online shopping intention of customers. The findings suggested that effort expectancy has a significant positive association with purchase intention.
H2: Effort expectancy is a significant driver of online shopping for mature customers’ intention to purchase online.
Venkatesh et al. (Venkatesh et al., 2003) defined “facilitating circumstances as the degree to which a person thinks that an organizational and technological infrastructure exists to facilitate technology usage”. Joshua and Koshy (Joshua & Koshy, 2011) have shown in the mobile banking adoption literature that the more convenient respondents' access to computers and the internet, the more competent their use of computers and the internet, resulting in a greater adoption rate of respondents utilizing electronic banking. As a result, based on UTAUT, the following hypothesis is advanced:
H3: Facilitating conditions is significant driver of online shopping for mature customers.
Various studies have examined the role of social influence on online shopping intention of customers (Lian and Yen, 2014). Authors like Hassan et al., (Hassan et al., 2015) attempted to evaluate the elements that influence the impact of UTAUT on the online purchasing behaviour of polytechnic students. Additionally, it examines the effect of “self-efficacy and anxiety on students' behavioural intention to use an e-commerce website”. It conducts an experiential examination of distinct types of self-efficacy and worry in relation to the behavioural choice to purchase online. Expectations about performance, effort, social influence, and self-efficacy, were found to have a substantial and stable association with behavioural intention. While the enabling condition has a significant association with user acceptability, internet, and ease of online shopping have a marginally significant relationship with behavioural intention. As such below hypothesis is proposed to study the impact of social influence on online shopping intention of mature customers
H4: Social influence is a significant driver of online shopping for mature customers.
Data Analysis And Results
Measurement Model
The estimation of the research model was done in two stages. First, measurement model for drivers of online shopping was estimated. Subsequently, the relative importance of each factor of online shopping drivers in explaining the online shopping behavior for mature customers was analyzed. Smart PLS software was used to analyze the collected data. The survey was carried out between September 2020 to January 2021 among older adults above the age of 50 years. 369 respondents provided their response through a structured questionnaire.
Demographics
Most respondents among the older adults were between 50 to 55 years, followed closely by the over 60 age group. Males were greater in number than female respondents (Table 1).
| Table 1 Age Distribution |
||||
|---|---|---|---|---|
| Age | Gender | Number (%) | Grand Total | |
| Male (%) | Female (%) | |||
| 50-55 | 136 (37) | 30 (6.69) | 166 (45) | 369 |
| 56-60 | 53 (13) | 21 (7.27) | 74 (20) | |
| Over 60 | 66 (20) | 63 (17.44) | 129 (35) | |
| 255 (69) | 114 (31) | |||
Reliability and Validity
The reliability and validity of the proposed measurement model was examined using the confirmatory factor analysis (CFA) of the constructs. As the measurement variables in this research were adopted from earlier studies, validity and reliability of the constructs were found to be appropriate. The admissible cut-off value for Composite Reliability (CR) is >0.7 Average Variance Extracted (AVE) is >0.5. Moreover, the cut-off value for Cronbach’s alpha in this study is >0.6 (Nunnally, 1978). Based on the above criteria all of the indices in this study are admissible (Table 2). Hence, items in this study have admissible individual item reliability (factor loading >0.5), convergent validity (AVE> 0.5), composite reliability (CR >0.7).
| Table 2 Reliability Analysis Of The Constructs |
||||
|---|---|---|---|---|
| Construct | Cronbach's Alpha | rho_A | Composite Reliability (CR) | Average Variance Extracted (AVE) |
| Effort Expectancy | 0.706 | 0.731 | 0.817 | 0.531 |
| Facilitating Conditions | 0.788 | 0.801 | 0.876 | 0.703 |
| Online-Shopping Intention | 0.775 | 0.786 | 0.87 | 0.691 |
| Performance Expectancy | 0.771 | 0.779 | 0.869 | 0.689 |
| Social Influence | 0.775 | 0.783 | 0.87 | 0.69 |
| Effort Expectancy | 0.706 | 0.731 | 0.817 | 0.531 |
Table 3 shows the discriminant analysis results of constructs. As the diagonal values are greater than other values, the constructs show admissible discriminant validity.
| Table 3 Discriminant Validity Of Drivers Of Online Shopping |
|||||
|---|---|---|---|---|---|
| Constructs | Effort Expectancy | Facilitating Conditions | Online Shopping Intention | Performance Expectancy | Social Influence |
| Effort Expectancy | 0.73 | ||||
| Facilitating Conditions | 0.60 | 0.84 | |||
| Online Shopping Intention | 0.67 | 0.72 | 0.83 | ||
| Performance Expectancy | 0.62 | 0.59 | 0.65 | 0.83 | |
| Social Influence | 0.68 | 0.72 | 0.68 | 0.59 | 0.83 |
Five-point Likert scale was used to record the response on each variable on a scale of one to five. Greater value shows that the respondent’s intention to shop online is strong whereas a lower value indicates week intention to shop online. To test the hypotheses partial least square (PLS) model was deployed. The analysed results are shown in Table 4.
| Table 4 Path Coefficients Of The Structural Model |
|||||
|---|---|---|---|---|---|
| Variables | Original Sample (O) | Sample Mean (M) | Standard Deviation (STDEV) | T Statistics (|O/STDEV|) | P Values |
| Facilitating Conditions -> Online Shopping Intention | 0.351 | 0.349 | 0.053 | 6.669 | 0.000 |
| Effort Expectancy -> Online Shopping Intention | 0.234 | 0.235 | 0.052 | 4.692 | 0.000 |
| Performance Expectancy -> Online Shopping Intention | 0.204 | 0.202 | 0.05 | 4.034 | 0.000 |
| Social Influence -> Online Shopping Intention | 0.149 | 0.153 | 0.067 | 2.236 | 0.025 |
Assessment of the Structural Model
The following table provides the overall estimate of the structural model Figure 3.
It is evident from table 5 that all the path coefficients are statistically significant, and all the hypotheses are supported by the model estimates.
| Table 5 Analysis Results |
|||
|---|---|---|---|
| Hypotheses | b | t | Support |
| H1: Performance expectation → Online shopping Intention | 0.204 | 4.034 | Y |
| H2: Effort Expectancy → Online shopping Intention | 0.234 | 4.692 | Y |
| H3: Facilitating Conditions → Online shopping Intention | 0.351 | 6.669 | Y |
| H4: Social Influence → Online shopping Intention | 0.149 | 2.236 | Y |
*p <0.001.
Figure 3:Structural Model.
Performance expectancy, effort expectancy, facilitating conditions and social influence are significant drivers of online shopping intention for mature customers.
Discussion
This section presents the implications of the results. The theoretical construct investigates the drivers of online shopping for mature customers. The study found important drivers of online shopping in India for mature customers and corroborated their association with online shopping intention. Despite significant studies to investigate the various factors of online shopping, a comprehensive model is lacking which explores the factors that influence the online shopping among mature customers in context of Indian and developing economies. Current study fills this gap by empirically investigating the factors of online shopping and their association with online shopping intention.
Drivers of Online Shopping
It has been observed that effort expectancy is the most prominent driver that affects the online shopping intention among mature customers. The results suggest that mature customers perceive that online shopping is convenient for them and it provides a variety of products to choose from. Furthermore, customers perceive that language used in online shopping websites is easy to understand and shopping websites are user friendly. The customers feel that their friends shop online, and they should also shop online. Respondents are of the view that they have skill and knowledge to use shopping websites. The research examined major factors driving online shopping among mature customers. The drivers found from the present study are: (1) performance expectancy, (2) effort expectancy, (3) social influence, and (4) facilitating conditions. The performance expectancy and effort expectancy emerged as significant drivers of online shopping. These results are consistent with the findings of Lian and Yen (Lian & Yen, 2014) signifying that “performance expectancy” and “effort expectancy” are major drivers of online shopping by older adults. The results also suggest that facilitating conditions to shop online is also an important driving factor for mature customers. This suggest that mature customers have got themselves acquainted with the new technology and infrastructure like internet, smart phones and skills required to operate these devices is not a challenge for them.
The findings confirms that Indian mature customers have got acquainted themselves with the new technology and feels that they can use online shopping websites. This is also because of the challenges posed by COVID-19 where online channel was one of the prominent modes of shopping during COVID lockdown and because of safety concerns.
Implications and Conclusion
The study has significant implications for academicians and managers. The study was carried out to understand the factors of online shopping and their association with online shopping intention among mature customers in a developing economy. The findings of the study can be further extended for other emerging economies to assess the factor leading to online shopping by mature customers.
One important offering of this study is to empirically examine the factors driving online shopping among mature customers. The results of this study have practical implications for industry as well. A significant customer base comprises customers of age fifty and more in India. Understanding the factors affecting online shopping for this customer base would help managers to amplify the features like convenience and price. Further, the understanding will enable managers to create campaigns to influence the behaviors driving the online usage for these mature customers. The managers should also create awareness about the online shopping among female customers enabling them to use the online shopping.
Limitations and Future Research
This study has a few considerable limitations. The study was conducted in India, so the scope of generalization of findings are limited. The findings can be validated in other developing economies for mature customers. Finally, because of logistical constraints, the study examined only the drivers of online shopping for mature customers. Future research may investigate the barriers of online shopping as well. New studies may also consider the moderating effect of income on online shopping by mature customers Appendix 1.
Following measurement items are adopted from Venkatesh et al. (2003)
| Appendix 1 Measurement Items |
||
|---|---|---|
| Sl. No. | Questions | Codes |
| 1 | “I feel Online shopping is convenient for me” | PE1 |
| 2 | “I feel On-Line Shopping provides variety of product availability” | PE2 |
| 3 | “I feel On-Line shopping helps to shop more quickly” | PE3 |
| 4 | “I feel that Online shopping websites are easy to use” | EE1 |
| 5 | “The Language used by online retailers are easy to understand” | EE2 |
| 6 | “It is easy to learn online shopping” | EE3 |
| 7 | “I feel Technology involved in online shopping is easy to use” | EE4 |
| 8 | “I have skill and knowledge for online shopping” | FC1 |
| 9 | “Online shopping is similar to other online services that I use” | FC2 |
| 10 | “I have necessary infrastructure to shop online” | FC3 |
| 11 | “A person who is very important to me think that I should shop-online” | SI1 |
| 12 | “Many of My friends shop online” | SI2 |
| 13 | “My friends think that I should shop online” | SI3 |
| 14 | “I intend to shop online” | OSI1 |
| 15 | “I plan to continue online shopping” | OSI2 |
| 16 | “ I try to shop online” | OSI3 |
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Received: 10-May-2023, Manuscript No. AMSJ-23-13579; Editor assigned: 11-May-2023, PreQC No. AMSJ-23-13579(PQ); Reviewed: 26-Jun-2023, QC No. AMSJ-23-13579; Revised: 28-Aug-2023, Manuscript No. AMSJ-23-13579(R); Published: 14-Sep-2023