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Article

Motives towards e-Shopping Adoption among Pakistani Consumers: An Application of the Technology Acceptance Model and Theory of Reasoned Action

by
Aqeela Saleem
1,
Javed Aslam
1,
Yun Bae Kim
1,*,
Shazia Nauman
2 and
Nokhaiz Tariq Khan
3
1
Department of System Engineering, Sungkyunkwan University, Suwon 16419, Korea
2
Riphah School of Business and Management, Riphah International University, Lahore 45320, Pakistan
3
Faculty of Business and Management, Information Technology University, Lahore 54590, Pakistan
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(7), 4180; https://doi.org/10.3390/su14074180
Submission received: 16 February 2022 / Revised: 29 March 2022 / Accepted: 29 March 2022 / Published: 31 March 2022
(This article belongs to the Section Sustainable Management)
Browse Figure
Review Reports Versions Notes

Abstract

:
Digital technologies play a vital role in daily human life and significantly influence human attitudes toward the adoption of new and attractive lifestyles. The internet has been widely accepted in every modern society, and the act of purchasing products or services over the internet, known as online/internet shopping, has revolutionized business. This study was designed using the technology acceptance model and theory of reasoned action. This study identified important factors such as perceived awareness of security, perceived usefulness, personal innovativeness, and perceived ease of use in purchasing, together with the effects of these factors on online purchasing intentions and the mediating role of consumer attitudes toward online purchasing. The results show that the identified factors are positively and significantly related to consumer intentions and attitudes toward online purchasing. This study has the potential to guide online retailers and managers in expanding online purchasing platforms and in increasing their responsiveness to the need to restructure business models according to new technological developments.

1. Introduction

Digital technologies play a vital role in the daily lives of humans today. These digital technologies are going to change human attitudes toward the adoption of new and attractive lifestyles [1]. These changes are not only applicable to developed countries—even developing (and to some extent, underdeveloped) countries are very active in their efforts to improve the lifestyles of their citizens through the advancement of technology [2,3]. Governments worldwide are also trying to improve society by providing better technological infrastructure. Technological developments help business sectors to expand the economy, and e-commerce is the most important aspect of the global digital economy [4], with many countries trying to uplift their economy using digital technologies [5,6]. The internet is one of today’s most effective and common sources of information, and it is ubiquitous in every modern society [7]. The act of purchasing products or services over the internet is known as online/internet shopping, and the phenomenon has revolutionized business [8]. From the consumer’s point of view, online shopping is one of the most effective uses of the internet. Internet shopping has seen rapid growth on a massive scale, appearing as an emerging market to provide a platform for domestic and international transactions [9]. Online stores are also known as e-stores, e-shops, e-web stores, web-shops, or virtual stores. Online shopping is considered very useful because it provides a comparative analysis of products in ways that are easy, rapid, and effective by way of users’ electronic clicks [10,11,12,13] and is generally recognized because of its effective services, security, efficiency, and popularity [14].
With technology, traditional shopping styles (also known as in-person mall visits) are less convenient than online shopping [15]. In a seminal work of existing literature, the Engel Kollat Blackwell (EKB) model was proposed to analyze consumer behavior in the context of traditional shopping styles [16]. The EKB model established five stages, including (i) need recognition, (ii) information search, (iii) evaluation of alternatives, (iv) purchase decision, and (v) post-purchase behavior [17,18]. The EKB model is also a comprehensive tool that can help to understand the consumer buying decision process under technological development. In the online purchasing decision model, the EKB model stages have been merged into a two-stage model—the first stage involves finding, comparing, and selecting products or services, including the placing of orders. Stage one is the combination of four stages of the EKB model, namely (i) need recognition, (ii) information search, (iii) evaluation of alternatives, and (iv) purchase decision, while the second stage consists of order tracking, delivery, and the return of anything that does not meet consumer expectations, which is related to the EKB model’s fifth stage, i.e., post-purchase behavior [19,20]. Put more briefly, the first stage is known as the ordering stage and the second stage is termed the order fulfillment stage [21,22,23]. Additionally, these two stages can be performed more quickly with electronic gadgets such as mobile phones, personal computers (PCs), tablets, and laptops that are internet-enabled. Each of these aspects seems more convenient in comparison to offline/traditional shopping styles. This convenience is the significant attribute and principal motivation for consumers’ acceptance of online shopping, which leads to the long-term development of consumer behaviors [10,24]. Eventually, the benefits of online purchasing, such as convenience, better price comparison, more product variety, privacy, and time-saving nature, are highly effective in reshaping consumer behavior, and the technology acceptance model (TAM) and the theory of reasoned action (TRA) are the most influential theories to explain and predict users’ acceptance of the new system [25]. Unfortunately, people around the globe are still facing problems with delivery times, quality issues, and defective products despite how rapidly technology has accelerated in terms of online retail.
According to Statista (2020), e-commerce has a significant impact on the economy, with the summary revealing that the total value of global revenue in the e-commerce market is US $2,237,481 million, 14.9% higher than the previous year in 2019, with an expected annual growth rate of 7.6% in the number of users from 2020–2024. The current number of e-commerce users is 4176.8 million, expected to increase to 5060.3 million by 2024, with a growth rate of 7.20%. This statistical analysis explains the importance of online purchasing in the development of economies [26]. With endless development in the retail industry, online shopping defines the massive market growth and provides an effective platform for technology such as high-speed internet. Internet penetration has a major effect on the online buying market, and its acceptance ratio is higher in developed counties than in developing countries [27]. In the context of Pakistan, the e-commerce sector is rapidly growing, and its annual growth rate is expected to be 7.55% with a projected market volume of US $7903 million by 2025 [28]. Pakistan is the 46th largest e-commerce market, and its contribution to worldwide growth is 29% in 2020. In the understanding of these statistics, the e-commerce industry is the backbone of the country’s economy, and information technology (IT) also plays a vital role in developing the e-commerce environment by providing an effective IT infrastructure. Regarding the Pakistan online retail industry, numerous online shopping sites have been developed, and some have become reputable among customers by gaining high customer satisfaction and trust, including Daraz, Telemart, IShopping, HomeShopping, Goto, Yaoyvo, Shophive, and online clothing brand stores such as Khaadi, Limelight, Gulahmad, Sapphire, Sanasafinaz, etc. [29].
It is important to understand the Pakistani consumer behavior regarding the adoption of online purchasing intention because Pakistan is classified as a developing country and needs to uplift the economy using digital transformation. Pakistan is struggling to shift towards a digital economy, and the e-commerce industry provides the best platform for this move. The biggest portion of e-commerce is based on online purchases/shopping; therefore, it is necessary to improve the online purchasing trend among Pakistani consumers. Pakistan is facing certain challenges such as a low literacy rate, poor technological infrastructure, and a lack of information on consumer behavior regarding online purchasing. The acceptance of online purchasing and the adoption of new technology are closely related to each other because if the consumers shift their buying behavior to online, this transformation cannot be fulfilled without the acceptance of internet or computer technology or more specific information technology. Therefore, this study is conducted using those factors that are more related to technology acceptance and alliance with the adoption of online purchasing, while previous studies (Table 1) have been more focused on online shopping oriented research, i.e., online shopping risk, benefits, website design, trust, and commitment [30,31,32,33]. To perform this task, the identified factors are summarized from famous theories such as the technology acceptance model (TAM) and the theory of reasoned action (TRA). The technology acceptance model predicts people’s behavior regarding the acceptance of information technology [34] and highlights several important factors, which are basic characteristics of using information technology, i.e., perceived usefulness, perceived ease of use, personal innovativeness. Furthermore, when considering online purchasing, consumers need to share personal information such as a home address, bank card, and cell phone numbers; thus, personal awareness of security is also considered a curial factor. On the other hand, the theory of reasoned action considers the consumer’s attitude and intention of accepting the particular system. However, both theories are reliable for predicting consumer behavior toward the adoption of new technology. In this study, we used these four factors, i.e., personal awareness of security, perceived usefulness, personal innovativeness, and perceived ease of use in purchasing to understand consumer attitudes and intentions toward online purchasing among Pakistani users. From the perspective of Pakistan, these factors play a vital role in the progress of online retailer platforms because Pakistani online consumer markets are at the initial stage of the digital economy, and it is necessary to study consumer views about usefulness, ease of use, and awareness of the security of online purchasing system as well as the role of personal innovations for the development of online purchasing attitudes and intentions. In line with the objective of this study and the gaps identified in the literature, the following research questions were proposed.
RQ1: How does technology influence online purchasing consumer behavior?
RQ2: How are the identified factors important to predicting online purchasing consumer behavior?
RQ3: Which factors (among those identified) have a significant influence on the development of online purchase intentions?
The remainder of this study is structured as follows. Section 2 is based on an extensive literature review including the technology acceptance model (TAM) and the theory of reasoned action (TRA), a conceptual model, and hypothesis development. The research methodology is described in Section 3. The results and discussion are presented in Section 4. Lastly, the conclusion is summarized in Section 5, which includes limitations, recommendations, implications, and suggestions for future research.

2. Literature Review and Hypotheses Development

2.1. Technology Acceptance Model (TAM) and Theory of Reasoned Action (TRA)

Technological advancement has led to significant improvements in almost every field, and information technology offers numerous ways to enhance the performance of a particular system [35]. Performance outcomes resulting from these advancements, however, are highly dependent on the willingness of users and the capability of the systems. The technology acceptance model (TAM) is one of the most influential theories in explaining and predicting users’ acceptance of information systems [36]. This model is widely used to develop assumptions about what factors impact the intentions of consumers to use new and novel technologies [37,38,39]. The TAM was established from the theory of reasoned action (TRA), originally explained in the work of Fishbein and Ajzan (1975). According to TRA, attitude is highly based on personal beliefs, and TAM predicts consumers’ perceived usefulness and perceived ease of use regarding new technologies, which are dynamic attributes in the acceptance of new information technologies by these potential users [37,40,41]. In considering individuals’ behaviors or attitudes toward the acceptance of technological advancement, TAM is the dominant theory. Moreover, TAM is also very active in predicting the behaviors of both experienced and inexperienced consumers as determinants of their purchase intentions [42]. The theory of reasoned action was proposed to describe how a consumer is led to a particular buying behavior. The TRA claims that intentions are predicted by the consumer’s attitude [43,44,45]. In the context of Pakistan, the existing literature is based on different factors that are considered to measure consumer behavior, which is focused on the shopping orientation factor (given in Table 1), but the adoption of technology is necessary to investigate the acceptance of online purchasing using TAM and TRA. It will help to understand the basic factors that develop consumer intentions and attitudes toward online purchasing. Table 1 presents the different factors that have been investigated in previous studies.

2.2. Personal Awareness of Security

Personal awareness of security is theorized to be the degree to which consumers or users accept that the internet is secure for communicating personal and financial data for purposes of the transaction [47,48,49]. From the customer perspective, the perception of security is an essential attribute of online purchasing because e-commerce requires the sharing of sensitive and risky information such as credit card details to purchase goods or services [50]. Therefore, perceived security has the most significant impact on consumers’ attitudes and intentions toward online purchases. When a customer feels more secure in making an online purchase, the customer’s intentions and attitudes toward online purchasing are increased. Accordingly, the following hypotheses are proposed:
Hypothesis 1a (H1a).
Personal awareness of security is positively related to online purchasing intention.
Hypothesis 2a (H2a).
Personal awareness of security is positively related to attitudes toward online purchasing.
Hypothesis 3a (H3a).
Personal awareness of security is positively related to online purchasing intention via attitudes toward online purchasing.

2.3. Perceived Usefulness

Perceived usefulness is based on consumers’ acceptance of new technologies or systems as being more convenient in comparison to the previous system [37,42]. In online shopping, perceived usefulness means that a customer feels that a website is very effective for buying goods through an online platform. Moreover, when consumers feel that online shopping is very useful in saving them shopping time, this perceived usefulness influences consumer intentions and attitudes toward online purchasing. Therefore, the following hypotheses are suggested:
Hypothesis 1b (H1b).
Perceived usefulness is positively related to online purchasing intention.
Hypothesis 2b (H2b).
Perceived usefulness is positively related to attitudes toward online purchasing.
Hypothesis 3b (H3b).
Perceived usefulness is positively related to online purchasing intention via attitudes toward online purchasing.

2.4. Personal Innovativeness

Personal innovativeness describes the behavior of consumers who show a willingness to adopt new technologies and who pay much attention to the application of this willingness with corresponding cognitive attitudes [51]. In previous studies, innovativeness has been discussed as a personality construct in terms of consumer behavior. This means that innovativeness is highly based on individuals’ experiences with adopting new things—which may pertain to technology, lifestyle, attitude, or intention [52]. Consequently, if a person is highly innovative, then his or her attitudes and intentions toward technology adoption are high. Likewise, there are known associations between personal innovativeness, attitudes toward online purchasing, and online purchase intention, and this relationship is expressed in the following hypotheses:
Hypothesis 1c (H1c).
Personal innovativeness is positively related to online purchasing intention.
Hypothesis 2c (H2c).
Personal innovativeness is positively related to attitudes toward online purchasing.
Hypothesis 3c (H3c).
Personal innovativeness is positively related to online purchasing intention via attitudes toward online purchasing.

2.5. Perceived Ease of Use

Referring to the TAM, perceived ease of use refers to the behaviors of an individual who believes that any given new technology will require less effort than previous iterations or products [37]. Similarly, perceived ease of use describes consumers’ expectations to find specific information or products without putting in extra effort and requiring the least amount of time. According to the research, perceived ease of use indicates that a consumer believes that he or she will obtain a great deal of information about purchasing goods with relatively little effort, and it thus leads to levels of customer satisfaction that boost customers’ intentions for online purchasing [30,33,38]. Moreover, perceived ease of use is also conceptualized as the easiest way to find goods through the internet and to benefit from comparative analysis by assessing similar products according to price, availability, payment terms, delivery, and return policies [53]. Therefore, we conclude that perceived ease of use in purchasing is positively related to consumers’ attitudes and intentions toward online purchasing with the following hypotheses:
Hypothesis 1d (H1d).
Perceived ease of use/purchasing is positively related to online purchasing intention.
Hypothesis 2d (H2d).
Perceived ease of use/purchasing is positively related to attitudes toward online purchasing.
Hypothesis 3d (H3d).
Perceived ease of use/purchasing is positively related to online purchasing intention via attitudes toward online purchasing.

2.6. Attitude and Online Purchasing Intention

Attitude has consistently been established as a significant predictor of intention when considering the acceptance of any technology by consumers. The TAM explains that the intentions of users toward new technologies or systems are strongly affected by their attitudes toward using the technology [54]. According to previous studies [36,40,43,45,55], online purchasing intention is highly based on the acceptance of online purchasing attitudes. Thus, the following hypothesis is proposed:
Hypothesis 4 (H4).
Attitude toward online purchasing is positively related to online purchasing intention.
Eventually, this study focuses on four factors of online purchasing behavior and describes them as independent variables—personal awareness of security, perceived usefulness, personal innovativeness, and perceived ease of use. We assess the direct impact of these factors on consumers’ intentions for online purchasing (the dependent variable), as well as the mediating effects via consumers’ attitudes toward online purchasing (a mediating variable). Figure 1 demonstrates the proposed research model of the study.

3. Methods

3.1. Data Collection and Sampling

For this study, to prevent the spread of COVID-19 among participants, a convenient sampling technique was used to collect the data through an online survey method (i.e., Google forms) from Pakistani consumers from July to September 2021 who lived in different regions of Lahore, Pakistan. Survey questionnaires were adopted from the existing literature and measured on a five-point Likert scale and pre-tested to ensure that the wording, structure, and format were appropriate for this study. We distributed 500 questionnaires online and 370 were returned, of which 353 were determined to be valid. Thus, we had an effective response rate of 70.6%.

3.2. Construct Measurement and Analysis

According to the conceptual model of this study (Figure 1), six variables were used to measure consumer behaviors toward online purchasing. For this, four variables were designated as independent variables: Personal awareness of security (PAS), perceived usefulness (PU), personal innovativeness (PI), and perceived ease of use (PEU). Furthermore, one variable was set as the dependent variable, namely online purchasing intention (IT), and consumer attitudes toward online purchasing (AT) served as the mediator variable of this study. An online survey questionnaire was developed through a literature review about consumer behavior towards online purchasing and technology adoption. A series of TAM and TRA questions including attitude and intention was adopted from previous studies [37,42,56] and modified as per other studies [33,57,58,59,60,61], where the attitude toward online purchasing was based on four items and intention to online purchasing was also based on four items. Personal awareness of security consisted of five questions adopted from [60,62], perceived usefulness was measured using three items adapted from [63,64], personal innovativeness was measured [57,58,65] using six questions, and perceived ease of use consisted of five items [41,63]. From a demographic perspective, there were four questions regarding age, gender, level of education, and items that respondents frequently purchased online (Appendix A). The construct variables were measured using a five-point Likert scale with scores that ranged from strongly disagree (1) to strongly agree (5). The data were coded and input in SPSS and AMOS statistical software. Data were evaluated via statistical analysis using demographic, descriptive, correlation, reliability, factor loading, validity, Kaiser–Meyer–Olkin (KMO), and Bartlett tests, as well as structural equation modeling and regression analysis.

4. Results and Discussion

4.1. Demographic Characteristics

In this study, we asked four demographic questions about age, gender, level of education, and items that respondents frequently purchased online. In the age category, the largest age group (50% of the total sample) was comprised of respondents between 18 and 29 years, followed in descending order by 20% in the age group of 30 and 40 years, 15% in the age group of under 18 years, 10% in the age group of 41 and 50 years, and 5% in the age group of 50 years or over, which suggests that the respondent of this study are sufficiently mature in age to understand the ongoing study. Regarding gender, 61% of respondents were male and 32.9% were female. In terms of levels of education, 56.1% of respondents reported having a master’s degree, 24.4% claimed to have a bachelor’s degree, 13.6% were educated at the college level, and 5.9% of respondents held a doctoral degree. Regarding categories of items that were frequently purchased online, 56% of respondents participated in general online shopping, 23.5% of respondents used online purchasing for food/restaurants, 13% reported using online purchasing to reserve travel tickets/transportation, and 8% reported other options. The demographic characteristics show (Table 2) that the respondent profile is sufficient because the age group is more likely to accept the adoption of new technologies and the education background is also better suited to understanding the online purchase intention and attitude.

4.2. Descriptive and Correlation Analysis

The descriptive analysis explains the statistical attributes of the data (i.e., means and standard deviations), and correlation analyses describe the relationships among variables according to the significance value. Table 3 expresses the descriptive and correlation analyses of the current study.

4.3. Reliability, Factor Loading, and Validity Analysis

The reliability, factor loading, and validity analysis of data was performed via SPSS 21.0 and examined Cronbach’s alpha (α), factor loadings through the principal component method, Kaiser–Meyer–Olkin (KMO) values, and the cumulative variance rate. As indicated in Table 4, factor loading values ranged from 0.501 to 0.895 (greater than 0.4) and Cronbach’s alpha values of all 27 items were between 0.730 to 0.897 (greater than 0.7), showing good internal consistency and reliability. The composite reliability (CR) values of the variables were greater than 0.7 and the average variance extracted (AVE) value was greater than 0.5, meeting the criteria to demonstrate convergent validity. Moreover, KMO values equal to or greater than 0.7 and a cumulative variance rate above 60% indicate that the construct has good validity. The KMO value was 0.855 (greater than 0.7) and the cumulative variance contributor rate was 69.75%, thereby confirming that the scale had good construct validity.

4.4. Goodness of Fit

In this study, AMOS 26 was used to measure the goodness of fit for the model and hypothesis testing. Model fitness values are indicated in Table 5. The results reveal that the root mean square error of approximation (RMSEA) value was 0.049 (lower than 0.060), and the root mean square residual (RMR) was 0.045 (less than 0.050), thus showing an acceptable model fit. Other values are higher than the acceptable value of 0.90. Therefore, the overall fit indices indicate that the model is a good fit.

4.5. Regression Analysis

Regression analyses were examined to analyze the results of the proposed hypothesis. Before examining the regression analysis, multicollinearity analysis was performed using the variance inflation factor (VIF) test and tolerance level method presented in Table 6. According to the literature, the VIF value must be smaller than 10 and the tolerance level value greater than 0.1, as these values are considered threshold values [66,67]. The results of VIF and the tolerance level of this study are under the acceptable value, whereby no VIF value was found to be higher than 10 and the tolerance level was less than 0.10; therefore, we conclude that this study has no issue with multicollinearity. The detailed results of the regression analysis are presented in the following subsection.

4.5.1. Direct Effects

The direct regression analysis (refer to Figure 1) was carried out to express the impact of the identified factors of personal awareness of security, perceived usefulness, personal innovativeness, and perceived ease of use on intention (H1a, H1b, H1c, H1d) and attitude (H2a, H2b, H2c, H2d), respectively, as well as the impact of attitude on intention (H4) to validate the proposed model using the structural equation model. The results of H1a, H1b, H1c, and H1d (Table 6) had a positive and significant impact on intention to online shopping. Among the identified four factors, perceived ease of use is the most important factor for the consumer to develop online purchase intention (β = 0.381, p = ***), which indicated that the consumer considers online purchases to be more convenient in terms of using the online platform/website in comparison with the offline store. Therefore, retailers need to develop an easy and user-friendly online platform to grab the consumer’s intention toward their online store. Furthermore, perceived usefulness is another important attribute for developing online purchase intention (β = 0.220, p = ***). Hence, consumers predict that online purchasing is useful because it provides numerous benefits such as easy product price comparison, convenience, more variety, no store opening/closing time constraints, and no crowds. These benefits can be earned in a few electronic clicks at home, in the office, or even anywhere and anytime. The remaining factors, personal innovativeness (β = 0.173, p = ***) and personal awareness of security (β = 0.151, p = ***), have a positive and significant impact on online purchasing intention. The coefficient value of personal innovativeness is lower in comparison to ease of use and usefulness because the literacy rate among Pakistani is low and the consumer might be reluctant to adopt the technology. This presents an opportunity for the online industry to develop a less complex online platform and encourage consumers to adopt the system by providing effective marketing campaigns. Moreover, the consumer is also concerned with security-related matters because the consumer needs to provide sensitive information while purchasing products online such as a shipping address, phone number, and bank card. Therefore, online retailers need to address the security-related implications and provide the best tool to secure consumer privacy with avoiding fraud and damages.
To determine the consumer attitude toward online purchasing proposed by hypotheses H2a, H2b, H2c, and H2d (Table 6), the same factors, namely personal awareness of security, perceived usefulness, personal innovativeness, and perceived ease of use, are positively and significantly related to shaping the consumer’s attitude. The results suggested interesting facts (Table 5), such as perceived usefulness (β = 0.402, p = ***) being relatively more valuable to developing the attitude towards online among other factors, which suggests that usefulness affects attitudes towards the adoption of online shopping. On the other hand, personal awareness of security (β = 0.374, p = ***) is considered another critical factor for the development of the attitude toward online purchases, which indicates that consumers are willing to accept the online purchasing system. Furthermore, perceived ease of use (β = 0.257, p = ***) and personal innovativeness (β = 0.147, p = 0.002) are positive and significant impacts in shaping the consumer’s attitude toward online purchasing, which indicated that Pakistani consumers also understand the online purchasing method as easy to use, and regardless of the low literacy rate or less technological infrastructure, consumers were innovativeness enough, which highlights the acceptance of online purchasing under informational technology adoption. However, this study also investigates the relationship between attitude and intention to purchase online by proposing H4. The results suggest that attitude (β = 0.452, p = ***) has a positive and significant impact on online purchasing intention.

4.5.2. Mediation Effects

To examine the mediation effects of the proposed hypotheses H3a, H3b, H3c, and H3d (refer to Figure 1), the total effects, direct effects without a mediator, and indirect effects with the mediator were tested using AMOS. The results show that all identified factors have a positive and significant impact on online purchase intention with the presence of attitude as a mediator. From Table 7, perceived usefulness found the most dominant positive and significant indirect effect 0.251 on online purchase intention via attitude. Consequently, perceived usefulness had the highest total effect of 0.424, which indicated that consumers believe that online purchases are very useful, which shapes consumer attitudes and leads to intentions to purchase online. Moreover, personal innovativeness is also an important factor in the acceptance of online shopping, with 0.181 indirect effects and 0.339 total effects, which explained that Pakistani consumers are considered innovators in terms of using information technology and have developed the intention toward online purchasing. Perceived ease of use and personal awareness of security are considered important factors when measuring the intention via attitude, and the total effect is 0.331 and 0.305, respectively, which indicates that consumers consider ease of use and security-related matters to affect the intention to buy online directly and indirectly. Lastly, these factors have a positive and significant impact on online purchase intention with attitude as a mediator.

5. Conclusions

This research identifies four important factors affecting consumer behavior toward online purchasing, and we conclude that all these factors—personal awareness of security, perceived usefulness, personal innovativeness, and perceived ease of use—are positively and significantly related to online purchasing intention with direct and indirect effects, using attitude as a mediator. These factors are based on the technology acceptance model and theory of reasoned action. These results indicate that Pakistani consumers are influenced by the benefits of online purchasing. This research reveals the interests of consumers in terms of their intentions and attitudes toward online shopping are exciting. Our study also validates the technology acceptance model and theory of reasoned action in predicting Pakistani consumers’ online shopping behaviors insofar as the results of this study demonstrate that consumers are highly drawn to adopting technological changes to improve their lifestyle. With technology, traditional shopping styles are less convenient than online shopping. Our findings have important implications for theory. First, this study expands our understanding of online purchasing intention by using the technology acceptance model and theory of reasoned action. Secondly, the current research supports, contributes to, and broadens the existing literature by considering four major factors—personal awareness of security, perceived usefulness, personal innovativeness, and perceived ease of use (purchasing)—in online consumer behaviors in Pakistan, underlining the positive relationships between these factors as well as their positive impact on online purchasing intention (with direct effects) and attitudes toward online shopping (with indirect effects). In the context of Pakistan, previous research has not described these determinants in evaluating consumer behaviors using these theories. We conclude that the highlighted factors are crucial for consumer adoption of technological advancements following the technology acceptance model. By combining the underlying theories of this study (TAM and TRA), our findings suggest that TAM and TRA have explanatory power in evaluating consumer adoption behaviors toward technology. From a practical point of view, this study guides managers to understand consumer behavior in online purchasing using the proposed factors. We recommend greater responsiveness on the part of online retailers to improve online purchasing platforms by considering our four main factors of consumer behaviors in the context of online purchasing. Furthermore, in today’s competitive and technology-based business environment, organizations must develop robust online selling platforms to meet the evolving needs of consumers. This study has certain limitations. First, in terms of data collection and sampling techniques, data collection was based on an online survey for safety during the COVID-19 pandemic, and respondents used the convenient sampling technique to fill out the survey. Secondly, this study is constructed based on cross-sectional approaches, which are time-dependent. Regarding directions for future research, other factors that affect online consumer behaviors need to be analyzed, e.g., online review violence, internet browsing habits, gender differences, internet advertisements, online purchasing intentions, and attitudes. Moreover, it will be necessary to evaluate the impact of the pandemic on consumer online purchasing behaviors in future studies.

Author Contributions

Conceptualization, A.S., S.N., and Y.B.K.; methodology, J.A.; software, N.T.K.; validation, S.N., Y.B.K., and A.S.; formal analysis, J.A.; investigation, Y.B.K.; resources, Y.B.K.; data curation, N.T.K.; writing—original draft preparation, A.S; writing—review and editing, N.T.K. and J.A.; visualization, J.A.; supervision, Y.B.K.; project administration, S.N.; funding acquisition, Y.B.K. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1A2C1013147).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data will be provided upon request.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Survey Questions
1.
What is your Age?
  • Under 18
  • 18–29
  • 30–40
  • 41–50
  • Above 50
2.
Gender
  • Male
  • Female
3.
What is the highest level of education you have completed?
  • College
  • Bachelor’s degree
  • Master’s degree
  • Doctorate
4.
Which is most frequently items you have purchase through online? Select multiple if applicable
  • Ticketing/transportation
  • Restaurant/food
  • Shopping
  • Other (→)
Table
Table A1. Attitude towards online purchase scale.
Table A1. Attitude towards online purchase scale.
Attitude towards Online Purchase(Five Point Scale Ranging from 1 = “Strongly Disagree”
to 5 = “Strongly Agree”)
Questions12345
The use of online purchasing is a good idea
The use of online purchasing is convenient
The use of online purchasing is beneficial
The use of online Purchasing is interesting
Table
Table A2. Intention towards online purchase scale.
Table A2. Intention towards online purchase scale.
Intention towards Online Purchase(Five Point Scale Ranging from 1 = “Strongly Disagree”
to 5 = “Strongly Agree”)
Questions12345
Given the opportunity, I will use the online purchasing.
I am likely to use online purchasing in the near future
I am open to using the online purchasing in the near future.
I intent to use the online purchasing when the opportunity raises.
Table
Table A3. Personal awareness of security scale.
Table A3. Personal awareness of security scale.
Personal Awareness of Security (Five Point Scale Ranging from 1 = “Strongly Disagree”
to 5 = “Strongly Agree”)
Questions12345
I would feel secure in providing sensitive information (e.g., credit card number) for online purchasing.
It would be no security problem transmitting sensitive information online for buying.
I feel the risk associated with online purchasing is low.
Overall, the internet should be used as a secure way to buy.
I would feel fine in providing sensitive information about myself while buying through online platform.
Table
Table A4. Personal innovativeness scale.
Table A4. Personal innovativeness scale.
Personal Innovativeness(Five Point Scale Ranging from 1 = “Strongly Disagree”
to 5 = “Strongly Agree”)
Questions12345
I like to explore new websites
When I hear about new website, I often find an excuse to go visit it
Among my peers, I am usually one of the first to try out new internet sites
In general, I am interested in trying out new web sites
When I have free time, I would explore new web sites
It is fun to visit a variety of web sites
Table
Table A5. Perceived ease of purchasing scale.
Table A5. Perceived ease of purchasing scale.
Perceived Ease of Purchasing(Five Point Scale Ranging from 1 = “Strongly Disagree”
to 5 = “Strongly Agree”)
Questions12345
Online buying through internet would not require a lot of mental effort
Online buying through internet would be easy
I find it easy to buy product via internet when I need it
Using the internet to buy product would be easy for me
I would find it easy to buy product I want via internet
Table
Table A6. Perceived usefulness scale.
Table A6. Perceived usefulness scale.
Perceived Usefulness(Five Point Scale Ranging from 1 = “Strongly Disagree”
to 5 = “Strongly Agree”)
Questions12345
The Internet would be useful in my purchasing
The advantages of buying the product via internet will outweigh the disadvantages
Overall, using the internet to buy products will be advantageous for me

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Sustainability 14 04180 g001 550
Figure 1. Proposed research model.
Figure 1. Proposed research model.
Sustainability 14 04180 g001
Table
Table 1. Online shopping adoption factors investigated in Pakistan.
Table 1. Online shopping adoption factors investigated in Pakistan.
Adopted FactorsProposed TheoriesRemarksReference
Previous Studies for Determining the Consumer Behavior Toward Online Shopping
Perceived advantagesNot discussedThis study was based on highlighting the risks, psychological, and website design factors to measure online purchasing behavior.[32]
Perceived risk
Psychological
Website design and content
Perceived benefitsNot discussedThis study was focused on shopping-related benefits to determine consumer behavior towards online shopping.[30]
Domain-specific expertise
Shopping orientation
AttitudeTechnology acceptance model The focus of this study is to analyze the role of trust and commitment toward online purchasing using subjective norms and perceived behavior control methods.[33]
Subjective norms
Perceived behavioral control
Trust
Commitment
Convenience riskTheory of planned behaviorDetermined the risk associated with product and convenience.[31]
Product risk
Perceived risk
Social needNot discussedThis research highlighted the social need to adopt online purchasing.[46]
Social influence
Convenience
Proposed Study Consideration about Online Purchasing Factors
Perceived usefulnessTechnology acceptance model and theory of reasoned action. The study aims to determine the factors that are highly interlinked with technology adoption in the perspective of online purchases and investigate the role of these factors on consumer attitude and intention towards e-Shopping using the TAM and TRA.
Perceived ease of use
Personal awareness of security
Personal innovativeness
Table
Table 2. Respondent profile.
Table 2. Respondent profile.
VariableCategorySample NumberFrequency (%)
AgeUnder 185315
18–2917750
30–407020
41–503510
Above 50185
GenderMale23767.1
Female11632.9
EducationCollege4913.6
Bachelor’s degree8624.4
Master’s degree19856.1
Doctorate225.9
Frequently items purchase onlineTicketing/transportation4413
Restaurant/food8223.5
Shopping19656
Other318
Table
Table 3. Descriptive and correlation analyses.
Table 3. Descriptive and correlation analyses.
VariablesMS.D123456
1PAS2.740.8351
2PU3.520.7530.477 **1
3PI3.420.8510.303 **0.549 **1
4PEU3.520.7730.351 **0.665 **0.513 **1
5AT3.650.8420.375 **0.635 **0.489 **0.579 **1
6IT3.680.6720.383 **0.476 **0.429 **0.381 **0.566 **1
** Correlation is significant at the 0.01 level. Mean (M), Standard deviation (S.D).
Table
Table 4. Reliability, factor loading, and validity analysis.
Table 4. Reliability, factor loading, and validity analysis.
VariableItemFactor LoadingCronbach’s αAVECR
PASPAS10.5270.8170.7740.910
PAS20.670
PAS30.774
PAS40.832
PAS50.700
PUPU10.6200.7300.7050.813
PU20.501
PU30.634
PIPI10.7130.8970.7930.920
PI20.658
PI30.606
PI40.833
PI50.708
PI60.739
PEUPEU10.7830.8690.7490.922
PEU20.738
PEU30.778
PEU40.895
PEU50.811
ATAT10.6960.8720.6900.871
AT20.646
AT30.701
AT40.663
ITIT10.6350.8440.7310.821
IT20.731
IT30.561
IT40.686
Table
Table 5. Goodness of fit.
Table 5. Goodness of fit.
Fit IndicesRMSEARMRCFIGFIAGFINFIRFI
Fit Values0.0490.0460.9630.9740.9680.9540.931
Table
Table 6. Regression analysis (direct effects).
Table 6. Regression analysis (direct effects).
Parameter PathModel HypothesisStandardized CoefficientSECRVIFSignificanceResult
IT←PASH1a0.1510.0413.7232.561***Supported
IT←PUH1b0.2200.0583.7752.890***Supported
IT←PIH1c0.1730.0433.9883.121***Supported
IT←PEUH1d0.3810.0437.7233.723***Supported
AT←PASH2a0.3740.0504.2463.531***Supported
AT←PUH2b0.4020.6306.3403.921***Supported
AT←PIH2c0.1470.0473.1102.1020.002Supported
AT←PEUH2d0.2570.0584.4113.412***Supported
IT←ATH40.4520.03512.8964.214***Supported
where *** is a significant value p < 0.05. Standardized Error (SE), Critical Ratio (CR).
Table
Table 7. Regression analysis (mediation effects).
Table 7. Regression analysis (mediation effects).
Parameter PathModel HypothesesDirect Effect without a MediatorIndirect Effect with MediatorTotal EffectResult
PAS → AT → ITH3a0.157 ***0.148 ***0.305 ***Supported
PU → AT → ITH3b0.173 ***0.251 ***0.424 ***Supported
PI → AT → ITH3c0.158 ***0.181 ***0.339 ***Supported
PEU → AT → ITH3d0.069 ***0.262 ***0.331 ***Supported
where *** is a significant value p < 0.05.
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Saleem, A.; Aslam, J.; Kim, Y.B.; Nauman, S.; Khan, N.T. Motives towards e-Shopping Adoption among Pakistani Consumers: An Application of the Technology Acceptance Model and Theory of Reasoned Action. Sustainability 2022, 14, 4180. https://doi.org/10.3390/su14074180

AMA Style

Saleem A, Aslam J, Kim YB, Nauman S, Khan NT. Motives towards e-Shopping Adoption among Pakistani Consumers: An Application of the Technology Acceptance Model and Theory of Reasoned Action. Sustainability. 2022; 14(7):4180. https://doi.org/10.3390/su14074180

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Saleem, Aqeela, Javed Aslam, Yun Bae Kim, Shazia Nauman, and Nokhaiz Tariq Khan. 2022. "Motives towards e-Shopping Adoption among Pakistani Consumers: An Application of the Technology Acceptance Model and Theory of Reasoned Action" Sustainability 14, no. 7: 4180. https://doi.org/10.3390/su14074180

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