Determinants of Consumers’ Purchase Intention on Digital Business Model Platform: Evidence from Ethiopia Using Partial Least Square Structural Equation Model (PLS-SEM) Technique

doi:10.21203/rs.3.rs-2084439/v1

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Determinants of Consumers’ Purchase Intention on Digital Business Model Platform: Evidence from Ethiopia Using Partial Least Square Structural Equation Model (PLS-SEM) Technique

https://doi.org/10.21203/rs.3.rs-2084439/v1

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published 29 Aug, 2023

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A digital business model is a way of generating value based on the creation of client advantages using digital technology. The goal of the digital solution is to provide a significant advantage that customers are willing to pay for. For businesses facing digital disruption and digitization, developing digital business models is a crucial responsibility. A global platform for communication, the Internet is rapidly being utilized as a cutting-edge tool for product and service marketing. A competitive threat to conventional retail establishments, online digital market platforms have many benefits over traditional shopping methods. Consumers are quickly embracing internet shopping around the world, and Ethiopia is no exception. Online digital market platform marketers and retailers can further expand their marketing strategies to draw in and keep customers in order to gain a competitive edge globally if they are aware of the elements influencing consumers' purchasing intentions.

Due to the scarcity of published research on the variables influencing Ethiopian consumers' intention to purchase online, there is a conceptual gap in the literature on online digital marketing. By identifying the crucial variables impacting Ethiopian consumers' desire to purchase on online digital market platforms, this study aims to close this conceptual gap in the context of online market platform purchasing.

The information was gathered from 100 Ethiopian, Addis Ababa University, final year engineering students using a self-administered 5-point Likert scale questionnaire. The partial least square structural model (PLS-SEM) technique was used to examine the data. The results of this study highlighted five crucial decision-making elements that have an impact on Ethiopian consumers' intention to buy on online digital market platforms including Website Design, Perceived Usefulness, Perceived Ease of Use, Trust, and Subjective Norm. With the use of this information, manufacturers, online marketers, and retailers can create effective market strategies, enhance technology, and make smart marketing choices that will help them gain global competitive advantage.

Digital Business Model

online digital market platform

PLS-SEM

Online Purchase intention

Ethiopia.

A business model describes how an organization creates, delivers, and captures value (Osterwalder et al., 2005). In both theory and practice, the term "business model" refers to a wide range of informal and formal descriptions that represent the fundamental elements of an organization or business, such as the mission, business process, target market, offerings, strategies, infrastructure, organizational structures, sourcing, trading practices, operational processes and policies, including culture. Business models are used to describe and categorize enterprises, particularly in an entrepreneurial setting, but they are also used by managers within corporations to investigate opportunities for future development. Popular business models can serve as guidelines for innovative managers (Baden-Fuller & Morgan, 2010).

Business strategy includes the development and change of business models, which is often referred to as business model innovation. Organizations are becoming more aware that achieving their sustainability goals requires innovation on a business model level as well as new technologies. (Geissdoerfer et al., 2017). When an organization creates a new business model, the process is called business model innovation(Osterwalder et al., 2005) (Chesbrough, 2010).

The way technology is employed nowadays may determine the kind of company concepts. For instance, online business owners have developed brand-new business models that are wholly dependent on current or developing technology. Businesses may reach a lot of people for little money by using technology. Additionally, strategic sourcing, complicated supply chains, and shifts to collaborative, relational contracting structures are now part of business models due to the increase of outsourcing and globalization (Wiener et al., 2009).

The growth of platforms as a business model is producing global disruption by destroying the status quo across industries and boundaries. The way we interact and conduct our everyday lives in this new corporate environment is vastly different from what it was less than a decade ago. The client is increasingly approaching company with a digital first attitude. A tailored experience that is real-time, integrated, and seamless has become the standard. All of this leads to quick improvements in products and services, which necessitate flexible business models capable of meeting the demand. This is precisely what platform as a company wants to give. The emphasis has shifted to facilitating business relationships among participants and role-players. These participants and role-players can communicate across industries, share products and services within industries, or focus only on providing best-in-class products and services inside a single firm within that industry (Deloitte, 2019).

A digital business model is a method of creating value focused on the production of customer benefits via the use of digital technologies. The digital solution's purpose is to deliver a considerable benefit for which clients are ready to pay. Developing digital business models is a critical task for firms confronting digital disruption and digitalization (Teece & Linden, 2017) .

The Internet, as a worldwide communication platform, is fast becoming a cutting-edge instrument for product and service marketing (Dwivedi et al., 2021). Because of the internet, the traditional nature of retail purchasing has received a new dimension. Online digital market platforms, a competitive challenge to established retail enterprises, provide several advantages over traditional purchasing techniques (Ennew et al., 2005). Consumers all across the world are fast adopting internet shopping, and Ethiopia is no different. Marketers and merchants on online digital market platforms can further develop their marketing tactics to attract and retain customers in order to achieve a competitive advantage internationally if they are aware of the factors influencing consumers' purchase intentions (Ennew et al., 2005).

Online purchasing on digital market platforms is increasingly becoming a popular purchasing strategy for clients globally. However, internet purchasing is not as common in Ethiopia. Understanding the variables influencing Ethiopian consumers' purchasing behavior is vital information for organizations that have already invested in online shopping, as it allows them to design stronger marketing tactics to turn potential customers into active ones. From a global standpoint, if corporations can enhance their awareness of Ethiopian customers' online purchasing preferences before entering the Ethiopian online market, the information can raise the probability of success. As a result, the primary goal of this study was to investigate the drivers of Ethiopian consumers' online market purchasing intention.

2.1 Technology Acceptance Model (TAM)

The Technology Acceptance Model (TAM) is a theory of information systems that describes how people come to accept and use technology. TAM seeks to assist academics and practitioners in determining why a certain technology or system may be acceptable or unsuitable and in taking appropriate steps by offering explanation in addition to prediction (LAI, 2016).

The real system usage is the point at which people interact with technology. People utilize technology because of their behavioral intentions. The attitude, which is the broad perception of the technology, influences the behavioral intention (BI). According to the concept, when consumers are faced with new technology, a variety of variables impact their decision about how and when to use it, most notably perceived usefulness (PU) and perceived ease-of-use (PEOU)(Davis, 1989). External elements such as social influence have a significant role in determining attitude. People will have the attitude and intention to use technology once these items are in place (Davis, 1989).

The TAM has been continuously studied and expanded the two major upgrades being the TAM 2 (Venkatesh & Davis, 2000) and the unified theory of acceptance and use of technology (or UTAUT, (Venkatesh et al., 2003b) ). A TAM 3 has also been proposed in the context of E-commerce with an inclusion of the effects of trust and perceived risk on system use (Venkatesh & Bala, 2008).

Despite its widespread use, TAM has been heavily criticized, prompting the original proposers to seek to reinterpret it multiple times. TAM's critics point to its poor heuristic value, low explanatory and predictive capacity, triviality, and lack of practical utility (Chuttur, 2009).

2.2 Theories of Reasoned Action and Planned Behavior

The two closely associated theories the Theory of Reasoned Action (TRA) developed by Martin Fishbein and Icek Ajzen in 1967 and the Theory of Planned Behavior (TPB) by(Ajzen, 1991) suggest that a person's intention to perform a behavior (behavioral intention) is predicted by 1) a person's attitude toward the behavior, and 2) subjective norms regarding the behavior. Subjective norms are the outcome of a person's perceived control over their conduct as well as their social and environmental circumstances. In general, positive attitudes and subjective norms improve perceived control and the likelihood of intentions influencing behavioral changes.

2.3 Perceived usefulness (PU)

This was defined by Fred Davis (Davis, 1989) as the degree to which a person believes that using a particular system would enhance their job performance. It denotes whether or not someone considers the technology to be beneficial for the task at hand. Within the context of online purchasing, we may infer that the individual behavioral intention of online purchasers believes that the system will be advantageous in their personal life.

(Davis, 1989) showed that the capacity to attract and keep online customers is mostly dependent on the technology's usability and usefulness. The perceived utility of online shopping applications or tools can also impact whether online customers continue to purchase online or rely on traditional ways. When it comes to online purchasing, perceived usefulness refers to how much a person feels that engaging in online transactions would improve his or her performance.

Many literatures including (Akar & Nasir, 2015; Aziz & Wahid, 2018; Bauerová & Klepek, 2018; Ofori & Appiah-Nimo, 2019) (Hanjaya et al., 2019)(Dewi et al., 2020a) (LAI, 2016) (Athapaththu & Kulathunga, 2018) (Sin et al., 2012) showed the effect of perceived usefulness on online purchase intentions. Hence based on TAM and empirical evidence from literature the following hypothesis has been done.

H1: Perceived usefulness positively and significantly affects online purchase intention of consumers.

2.4 Perceived ease-of-use (PEOU)

Davis described this as the degree to which a person feels that utilizing a specific system will be painless (Davis, 1989). When technology is simple to use, obstacles are reduced. No one is going to like it if it is difficult to use and has a complicated feature. In an online purchasing setting, we may infer that the unique behavioral purpose of customers buying online reflects their trust in the ease with which they can order products online.

Davis stated that PEOU has a causal effect on PU, which is confirmed by several studies (Venkatesh & Davis, 2000)(Athapaththu & Kulathunga, 2018; Celik, 2014). PEOU can have a substantial direct influence on PU since, when choosing between two systems that execute the same set of operations, a user should prefer the one that is simpler to use (Davis, 1989).

According to Davis, among the multiple components mentioned in his model, judgments of ease of use have a considerable effect on perceived usefulness; a crucial predictor of one's inclination to adopt new technology (Venkatesh et al., 2003b). Simply said, perceived ease of use impacts perceived utility, which influences the consumer's propensity to purchase online. Online consumers consider online buying to be simple if it gives sufficient product information, facilitates product/service comparison, has simple to learn and use apps, and has sites that are simple to use and flexible to engage with.

Many literatures including (Akar & Nasir, 2015; Aziz & Wahid, 2018; Bauerová & Klepek, 2018; Ofori & Appiah-Nimo, 2019) (Hanjaya et al., 2019) (Dewi et al., 2020a) (LAI, 2016)(Athapaththu & Kulathunga, 2018) (Sin et al., 2012) showed the significant effect of perceived ease of use on online purchase intentions and Perceived Usefulness. Therefore, based on literature review and TAM the following hypnoses had been formulated.

H2: Perceived Ease of Use has a positive effect on online purchase intention

H3: Perceived Ease of Use has a positive effect on Perceived Usefulness.

2.5 Subjective Norm

The subjective norm is the belief that the majority of people accept or disapprove of the activity. It refers to a person's opinions regarding whether peers and important individuals in his or her life believe he or she should engage in the conduct(Ajzen, 1991). According to Icek Ajezen’s theory of planned behavior, subjective norm affects the behavioral intention of an individual. Many researchers including(Dewi et al., 2020c)(Sin et al., 2012) studied the effect of subjective norm on purchase intention of consumers. Hence the following hypotheses have been done.

H4: Subjective norm affects online purchase intention of consumers positively and significantly.

H5: Subjective norm affects perceived usefulness

2.6 Trust

Because the core of commerce is the building of mutual trust, trust is a key aspect in every partnership. As consumers and sellers perform transactions without seeing each other in the online platform business, trust plays an important part in the implementation of the shopping as well as the longevity of the connection. The process of developing trust concludes with the maintenance of the achieved security. At this point, visitors are increasingly interested with brand and search mechanism technology, which eventually leads to intrinsic trust as part of buy intent (Athapaththu & Kulathunga, 2018).

In the context of online commerce, trust is defined as a consumer's readiness to rely on the seller and act in situations when such action renders the consumer susceptible to the seller. Because the degree of uncertainty in a transaction in a virtual environment is larger than in a traditional one, trust becomes a crucial consideration. Trust should be an especially important feature in an online situation when the consumer has no direct influence over the vendor's behavior. One of the primary reasons people avoid conducting commercial transactions on the internet is a lack of trust in online firms. As a result, the buyer's sentiments of trust toward an online vendor are a key determinant in determining his/her intents to use and online usage patterns.

Many researchers including (Athapaththu & Kulathunga, 2018) studied the positive relationship between trust and online purchase intention of consumers. Accordingly, the following hypothesis has been made.

H6: Trust affects the consumers’ online purchase intention positively and significantly.

H7: Trust affects the consumers’ perceived ease of use of online platform

H8: Trust affects consumer’s perceived usefulness of the online platform.

2.7 Website Design

Website is the main communication channel between online service providers and consumers. A high-quality website can result in satisfied customers and help turn web browsers into buyers. According to a study (Afshardost, 2013), the point of view of customers about website quality is based on features in a website that meet customers’ needs, requirement and attracted to the total excellence of that website. Website design refers to the qualities and appearance of a website that fulfill and meet the expectations of online consumers (Hasanov & Khalid, 2015). The influence of web design and elements influencing online purchasing behavior, as well as issues preventing consumers from purchasing online. If the web design is not user pleasant, difficult to access, or complicated, the buyer may seek out another user-friendly web design online shopping website.

Based on a group of researchers, website features have an important role on online purchase decision (Mansori et al., 2012). An informative website helps and assists customers to differentiate and evaluate product choices, differences and will increase customer satisfaction and contributing to online purchase decision (Masoudi et al., 2015).

In the e-commerce market, web design has been an important role in positively influencing consumers to shop online with satisfaction and more confidently(Hanjaya et al., 2019)(Wang Nee, 2019)(Ali, 2016)(LAI, 2016) excluding (Halim, 2013). Based on the empirical evidence in the literature the following hypotheses have been formulated

H9: Web Design has a positive impact on consumers’ perceived usefulness of online platform

H10: Web Design has a positive impact on consumers’ perceived ease of usefulness of online platform.

2.8 Purchase intention

Purchase intention is the likelihood that a buyer will purchase a product or service. Evaluating purchase intention entails combining data from several sources to determine which variables have the most influence. The information is utilized to develop marketing plans as well as to fine-tune messaging across various communication and marketing platforms. One significant purpose of measuring purchase intent is to learn how effective an offline or online media campaign is and whether the money spent on marketing activities provides an acceptable return on investment.

Purchase intention of the online shopper is the final stage, after various prompts of the online shopper (Athapaththu & Kulathunga, 2018). According to (Indiani & Fahik, 2020), the final stage of online transaction is intention to use a website and purchase a product. As a result, online purchase intent is critical in determining online customer behavior. Purchase intent is a state that exists between a consumer and a seller when the customer is ready to enter into a transaction with the seller. Purchase intention is described as a consumer's desire to purchase an item or service because the shopper has an intention to locate a certain item or benefit or has a favorable attitude toward, or even a favorable opinion of the product or service.

This section discusses the conceptual research model, sample size, sampling technique, research instrument, study area, time frame, data analysis method, data collection, and processing procedures.

3.1. Conceptual Model

Figure 3.1 depicts the conceptual research model created in this study. Based on prior literature, the theory of Technology Acceptance Model (TAM), and the theory of Reasoned Action (TRA), this model hypothesized that consumers' online market platform purchase intention is influenced by Platform Design (PD), Trust (TR), Subjective Norm (SN), Perceived Usefulness (PU), and Perceived Ease of Use (PEOU). In analyzing the link between dependent and independent factors, the dependent variable is Purchase Intention (PI) on the online platform, while the independent variables are PD, TR, SN, PU, and PEOU. Furthermore, PD and TR are exogenous factors, but SN, PU, and PEOU are endogenous and exogenous (see Figure 3.1).

3.2 Sample size and Sampling procedures

The sampling technique applied for this research is purposive sampling. The key informants for this study were selected based on their online purchase experience. To determine the minimum number of data required for analysis using PLS-SEM(Kock & Hadaya, 2018; Ranatunga et al., 2020) Produced a table for researchers to determine the sample size of their research using partial least square (PLS) structural equation modeling (SEM). This procedure was given the name R-Squared minimum technique. The minimal sample size is determined by three factors. The first component is the number of arrows pointing at the latent variable. The second element is the chosen significance level, while the third element is the model's minimal R². There are four arrows pointing to a construct in this study model (see Fig. 3.1). The significance level used is 5%. The minimum R² in this model is 0. 25. Therefore, referring Table 3.1 this research required 65 samples to reach 80% prediction power for identifying R² values of at least 0.25 with a 5% chance of error. The researchers believe that the collected 100 data is pretty good more than the minimum requirement.

Table 3 1: Reduced version of the table presented by Hair et al., (2014) for estimating minimum sample size on “Minimum R-Squared Method”

Source: (Ranatunga & Priyanath, 2020)

3.3 Research Instrument

A close ended 5-Point Likert scale questionnaire was utilized as the major research instrument to obtain pertinent data concerning the identification of the factors of consumers' online digital platform purchasing intention. The questionnaire was adapted from previously used well tested questionnaires including (LAI, 2016), (McKinney, 2004),(Venkatesh et al., 2003a)(Melorose et al., 2015)(Phuong, 2020), (Dewi et al., 2020b), (Ahmad Wani & Wajid Ali, 2016), (Xu et al., 2021),(Celik, 2014) in the literature concerning various determinants of consumers’ online purchase intention. The questionnaire was completed by final-year engineering students at Addis Ababa University in Ethiopia who had purchased online or intend to purchase.

The goal of the study was made apparent throughout data collection, and respondents' agreement was ensured using official letters issued from the School of Mechanical and Industrial Engineering, Addis Ababa Institute of Technology, Addis Ababa University, Ethiopia. The questionnaire was peer reviewed and pilot tested prior to data collection. Based on the input, the questionnaire was adjusted and enhanced, such as by removing unnecessary questions and enhancing question clarity. Finally, the measurement model's reliability was verified using SPSS version 26 software, and the results reveal that Cronbach's alpha is larger than 0.70 and above, which is over the permissible cut-off mark (Cronbach, 1951). In the end, the final questionnaire (see Appendix- A) was distributed to the target sample of 115 respondents. Among distributed questionnaires 100 collected showing the response rate to be 86.95%.

3.4 The study area and timeframe

The research was carried out in Addis Ababa, Ethiopia. It took into account the 115 final-year engineering students from Addis Ababa University in Ethiopia. The data gathering period lasted from May 2022 through June 2022.

3.5. Data collection and processing

Following the creation of the conceptual model for the research (Fig. 3.1), the questionnaire for data collection was designed. The questionnaire is divided into two pieces. Section 1 contains information regarding the respondents' demographic characteristics, whereas Section 2 addresses questions about the factors of consumers' online purchase intention from digital market platform. There was a total of 100 valid replies. Following that, the data was analyzed using the Partial least squares structural equation modeling (PLS-SEM) approach. PLS-SEM has exceptional capability for working with small samples(Hair et al., 2021). It is one of the most widely used methods of multivariate data analysis.

Table 4.1 contains the respondent profile and associated information, Table 4.2 has the mean and standard deviations, and Table 4.3 contains the correlations between the variables. Furthermore, because there were several independent factors (exogenous) and dependent variables (endogenous), Partial least Square Structural equation modeling (PLS-SEM) path analysis was utilized in this study to investigate the main effect and mediation hypotheses.

As a consequence, Table 5.1 displays the results of the construct reliability, validity, and collinearity tests. Table 5.2 displays the results of the Discriminant Validity test. Path coefficients are shown in Table 5.3. Table 5.4 displays the Path Analysis Total Effects findings, Table 5.5 displays the Path Analysis Total Indirect Effects results, and Table 5.6 displays the Special Indirect Effects results. Table 5.7 displays the R² and R² corrected findings, Table 5.8 displays the f² values, and Table 5.9 displays the Q² values. The data was analyzed in this manner using SPSS Version 26 and Smart PLS3.0 software.

4.1 Descriptive statistics

4.1.1 Profile of respondents

The descriptive data which includes Gender, Age, and department are shown in Table 2.

Table 4.1:

Profile of respondents
Profile		Number of Respondents	% Respondents
Gender	Male	76	76.0
Gender	Female	24	24.0
Sub-Total		100	100.0
Age	20–25	96	96.0
	26–30	3	3.0
	31–35	1	1.0
Sub-Total		100	10.0
Department	Mechanical	49	49.0
Department	Electrical	51	51.0
Sub-Total		100	100.0

As indicated in Table 4.1, the majority of respondents, 76 (76.0%), were males, with the remaining 24 (24.0%) females. The majority of students, 96 (96.0%), were between the ages of 20 and 25, 3 (3.0%) were between the ages of 26 and 30, and the remaining 1 (1.0%) were between the ages of 31 and 35. The majority of the students, 51 (51.0%), were from the electrical engineering department, with the remaining 49 (49.0%) from the mechanical engineering department.

Table 4.2:

Descriptive Statistics ((N, Minimum, Maximum, Mean, Standard Deviation)
	N	Minimum	Maximum	Mean	Std. Deviation
PU	100	1.00	5.00	4.3840	.59352
SN	100	2.33	5.00	3.8456	.60197
PEOU	100	2.22	5.00	3.7478	.61141
WD	100	2.60	5.00	4.1250	.58247
PI	100	3.00	5.00	4.0673	.53986
TR	100	2.10	5.00	3.9880	.54741
Valid N (listwise)	100

As demonstrated in Table 4.2, the mean values for all variables are greater than the five-point Likert scale's midpoint of 3 and have modest standard deviations. This demonstrates the respondents' high degree of agreement on the factors of consumers’ purchase intention from online digital market platform.

Table 4.3:

Correlations
		PU	SN	PEOU	WD	PI
PU	Pearson Correlation
	Sig. (2-tailed)
	N
SN	Pearson Correlation	.403^**
	Sig. (2-tailed)	.000
	N	100
PEOU	Pearson Correlation	.367^**	.375^**
	Sig. (2-tailed)	.000	.000
	N	100	100
WD	Pearson Correlation	.403^**	.287^**	.463^**
	Sig. (2-tailed)	.000	.004	.000
	N	100	100	100
PI	Pearson Correlation	.508^**	.575^**	.669^**	.395^**
	Sig. (2-tailed)	.000	.000	.000	.000
	N	100	100	100	100
TR	Pearson Correlation	.370^**	.357^**	.678^**	.488^**	.643^**
	Sig. (2-tailed)	.000	.000	.000	.000	.000
	N	100	100	100	100	100
**. Correlation is significant at the 0.01 level (2-tailed).

Table 4.3 shows that all of the correlations between the determinate factors and the customers' online digital market platform purchase intention are significant at 0.01 level. This demonstrates that the determinate characteristics are effective predictors of customers' online purchasing intentions.

4.2 Inferential statistics analysis

4.2.1 Evaluation of Measurement model

The outer model is evaluated first in PLS-SEM analysis (or measurement model). The goal is to see how effectively the items (questions) load on the hypothetical concept. The outer model is evaluated by looking at the reliability of individual items (indicator reliability), the reliability of each latent variable, internal consistency (Cronbach alpha and composite reliability), construct validity (loading and cross-loading), convergent validity (average variance extracted, (AVE), and discriminant validity. Cross loading (Fornell-Larcker criterion, HTMT criterion) (Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, 2017).

To assess the measurement model's internal consistency and convergent validity, composite reliability, individual indicator reliability, and average variance extracted (AVE) were all employed. To measure discriminant validity, the Fornell-Larcker criteria and cross-loadings were utilized.

a. Indicator reliability

According to a common rule of thumb for indicator reliability, a latent variable should explain a significant part, usually at least 50%, of each indicator's variance (Joseph F. Hair et al., 2013). Therefore, the outer loading of an indicator should be more than 0.7082, because that value squared (0.7082) equals 0.50. As shown in Table 4.4 and Fig. 4.1, Except for WD5 (0.657), TR1(0.672), TR2 (0.651), SN5(0.609), and SN6(0.614) all the indicators for the constructs in this model were well above the minimum acceptable level for outer loadings.

Table 4.4: Construct Reliability, Validity, and collinearity Test result

Latent Variable	Indicators	Loadings	Collinearity Statistics (VIF)	Construct Reliability Cronbach’s Alpha (a)	Composite Reliability (CR)	Average Variance Extracted (AVE)
Web Design	WD1	0.778	1.654	0.841	0.887	0.613
	WD3	0.804	2.342
	WD4	0.786	2.048
	WD5	0.657	1.402
	WD6	0.875	2.510
Perceived Usefulness	PU1	0.792	2.653	0.920	0.924	0.668
	PU3	0.808	2.369
	PU4	0.816	2.223
	PU5	0.875	2.790
	PU6	0.806	2.360
	PU7	0.807	1.899
Perceived Ease of Use	PEOU2	0712	1.656	0.824	0.876	0.586
	PEOU3	0.740	1.659
	PEOU6	0.771	1.851
	PEOU7	0.836	2.119
	PEOU8	0.764	1.758
Trust	TR1	0.672	1.216	0.815	0.870	0.575
	TR2	0.651	1.426
	TR3	0.810	2.170
	TR4	0.810	2.161
	TR5	0.828	2.425
Subjective Norm	SN1	0.706	1.942	0.768	0.840	0.516
	SN2	0.779	2.100
	SN3	0.852	2.122
	SN5	0.609	2.628
	SN6	0.614	3.110
Purchase Intention	PI1	0.807	1.849	0.824	0.876	0.586
	PI2	0.801	1.816
	PI3	0.813	1.960
	PI5	0.741	1.706
	PI6	0.760	1. 681

b. Internal Consistency

The most common measurement used for internal consistency is Cronbach’s alpha and composite reliability, in which it measures the reliability based on the interrelationship of the observed items variables. In PLS-SEM, the values are organized according to their indicator’s individual reliability (Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, 2017). The values range from 0 to 1, where a higher value indicates higher reliability level. Cronbach’s Alpha and composite reliability value > 0.70 is acceptable(Cronbach, 1951)(Jum C. Nunnally, 1978)(Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, 2017). As shown in Table 4.4. Cronbach’s Alpha values for all constructs is > 0.70 and the composite reliability (CR) of all variables are > 0.70 showing the internal consistency of the measurement items.

c. Convergent Validity

Convergent validity is the assessment to measure the level of correlation of multiple indicators of the same construct that are in agreement (Batool et al., 2015). To establish convergent validity, the factor loading of the indicator, composite reliability (CR) and the average variance extracted (AVE) have to be considered (Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, 2017). The value ranges from 0 to 1. AVE value should exceed 0.50, Composite Reliability (CR) and the indicator's outer loadings should be higher than 0.708 so that it is adequate for convergent validity (Henseler et al., 2009a)(Ab Hamid et al., 2017)(Joseph F. Hair et al., 2013).As shown in Table 4.4 the values of AVE are greater than 0.5. The factor loading and Composite Reliability (CR)values are > 0.708 showing the convergent validity of the measurement model.

d. Discriminant Validity

Discriminant validity is the extent to which a construct is truly distinct from other constructs by empirical standards. Thus, establishing discriminant validity implies that a construct is unique and captures phenomena not represented by other constructs in the model. The Fornell-Larcker criterion is a conservative approach to assessing discriminant validity. It compares the square root of the AVE values with the latent variable correlations. Specifically, the square root of each construct's AVE should be greater than its highest correlation with any other construct. This criterion can also be stated as the AVE should exceed the squared correlation with any other construct. The logic of this method is based on the idea that a construct shares more variance with its associated indicators than with any other construct (Joseph F. Hair et al., 2013)(Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, 2017). As shown in Table 4.5: The diagonal bold values are \(\sqrt{AVE}\) and the other values are correlations. The square root of each construct’s AVE is greater than its highest correlation with any other construct. Therefore, discriminate validity criteria are fulfilled.

Table 4.5:

Discriminant Validity. The diagonal (bold) values are\(\sqrt{AVE}\)
	PEOU	PI	PU	SN	TR	WD
PEOU	0.766
PI	0.671	0.785
PU	0.394	0.603	0.818
SN	0.444	0.661	0.461	0.718
TR	0.620	0.556	0.396	0.444	0.758
WD	0.471	0.456	0.452	0.384	0.469	0.783

4.2.2. Assessment of structural model

Once we have confirmed that the construct measures are reliable and valid, the next step addresses the assessment of the structural model results. The structural model is evaluated by examining at its predictive capabilities as well as the relationships between the constructs. The significance of the path coefficients, level of R² values, f² effect size, predictive relevance, and Q² effect size are the key criteria for evaluating the structural model in PLS-SEM. According to(Joseph F. Hair et al., 2013) assessment of structural model have five steps including: Assessment of structural model for collinearity issues, Assessment of the significance and relevance of the structural model relationships using structural model path coefficients, Assessment of the level of R², Assessment of the effect sizes f², Assessment of the predictive relevance Q² and the q² effect sizes.

a. Collinearity Assessment

Before conducting the analyses, the structural model must be examined for collinearity. The path coefficients might be biased if the estimation involves significant levels of collinearity among the predictor constructs. If the level of collinearity is extremely high (as indicated by a Variance Inflation Factor or VIF value of 5 or higher), one should consider removing one of the corresponding indicators(s)(Joseph F. Hair et al., 2013). As shown in Table 4.4 all constructs have a VIF value of less than 5 showing there is no collinearity issue. SN6 has a relatively highest value of VIF (3.11) but still within the limit.

b. Structural Model Path Coefficients

After running the PLS-SEM algorithm, estimates are obtained for the structural model relationships (i.e., the path coefficients), which represent the hypothesized relationships among the constructs. The path coefficients have standardized values between − 1 and + 1. Estimated path coefficients close to + 1 represent strong positive relationships (and vice versa for negative values) that are almost always statistically significant (i.e., different from zero in the population). The closer the estimated coefficients are to 0, the weaker the relationships. Very low values close to 0 are usually non-significant (i.e., not significantly different from zero)(Joseph F. Hair et al., 2013). Whether a coefficient is significant ultimately depends on its standard error that is obtained by means of bootstrapping. When the empirical “t” value is larger than the critical value, we say that the coefficient is significant at a certain error probability (i.e., significance level). Commonly used critical values for two tailed tests are 1 .65 (significance level = 10%), 1.96 (significance level = 5% ), and 2.57 (significance level = 1 % )(Joseph F. Hair et al., 2013) for this research the ritical value used was 1.96 (significance level = 5%).

The path analysis result shows that all paths have a positive relationship with their dependent variable (See Fig. 4.1 and Table 4.6); however, not all variables are statistically significant. As indicated by the bootstrapping results of PLS-SEM in Table 4.6: The effect of PU, PEOU, SN, TR, and WD on PI is positive and significant whereas the effect of PEOU and TR on PU are insignificantly positive. The effect of SN& WD on PU is positive and significant. The effect of TR&WD ON PEOU is significantly positive (see Table 4.6). The hypotheses H1, H2, H4, H5, H6, H8, H9, and H10 were supported but hypotheses H3 and H7 were not supported.

Table 4. 6: Path analysis result: Path coefficients

Hypothesis	Path	Path coefficient	t-statistics	p-values	Remark
H1	PUàPI	0.282	3.569	0.000	Accepted
H2	PEOUàPI	0.403	5.065	0.000	Accepted
H3	PEOUàPU	0.089	0.983	0.326	Rejected
H4	SNàPI	0.352	4.467	0.000	Accepted
H5	SNàPU	0.280	2.925	0.004	>>
H6	TRàPI	0.078	0.716	0.474	Rejected
H7	TRàPU	0.095	0.879	0.380	Rejected
H8	TRàPEOU	0.512	4.589	0.000	>>
H9	WDàPU	0.258	2.627	0.009	Accepted
H10	WDàPEOU	0.232	2.325	0.020	Accepted

As shown in Table 4.6, all the path coefficients are positive which shows the positive impact of all determinants on purchase intention of consumers. Except PEOUàPU (p=0.326), TRàPI (p=0.474), and (TRàPU) (p=0.380) all the hypothesis are accepted.

Table 4. 7: Path Analysis result: Total Effects

Hypothesis	Path	t-statistics	p-values	Remark
H1	PUàPI	3.236	0.000	Accepted
H2	PEOUàPI	3.750	0.000	Accepted
H3	PEOUàPU	0.928	0.354	Rejected
H4	SNàPI	4.865	0.000	Accepted
H5	SNàPU	2.780	0.006	>>
H6	TRàPI	2.587	0.010	>>
H7	TRàPU	1.089	0.277	Rejected
H8	TRàPEOU	4.286	0.000	Accepted
H9	WDàPU	2.815	0.005	Accepted
H10	WDàPEOU	2.298	0.022	Accepted
	WDàPI	2.831	0.005	Accepted

As shown in table 4.7 except H3(PEOUàPU, p=0.354) and H7 (TRàPU, p=0.277) all the variables have a positive and significant effect on consumers’ purchase intention supporting hypotheses H1, H2, H4, H5, H6, H8, H9, and H10.

Table 4. 8: Path analysis results, Total indirect effect

Path	t-statistics	p-values	Remark
PEOUàPI	0.820	0.413	Insignificant effect
PEOUàPU
PUàPI
SNàPI	1.899	0.058	Insignificant effect
SNàPU
TRàPEOU
TRàPI	3.00	0.003	Significant effect
TRàPU	0.813	0.417	Insignificant effect
WDàPEOU
WDàPI	2.831	0.005	Significant
WDàPU	0.859	0.391	Insignificant effect

As shown in table 4.8 the variables that have positive and significant total indirect effect on consumers’ online digital market platform purchase intention are only Trust (TR) and Website design (WD).

Table 4. 9: Special indirect effects

Path	t-statistics	p-values	Remark
WDàPEOUàPI	1.883	0.060	Insignificant
SNàPUàPI	1.899	0.058	>>
TRàPEOUàPU	0.813	0.417	>>
TRàPUàPI	0.589	0.556	>>
WDàPEOUàPU	0.859	0.391	>>
WDàPUàPI	2.454	0.014	Significant
TRàPEOUàPI	3.275	0.001	>>
PEOUàPUàPI	0.820	0.413	Insignificant
WDàPEOUàPUàPI	0.746	0.456	>>
TRàPEOUàPUàPI	0.726	0.468	>>

As shown in Table 4.9, among the variables only Website Design (WD) and trust (TR) have significant and positive special indirect effect on consumer’s online purchase intention as mediated by perceived usefulness (PU) and Perceived ease of use (PEOU) respectively.

c. Coefficient of Determination (R ² Value)

The most commonly used measure to evaluate the structural model is the coefficient of determination (R² value). This coefficient is a measure of the model's predictive accuracy and is calculated as the squared correlation between a specific endogenous construct's actual and predicted values. The coefficient represents the exogenous latent variables' combined effects on the endogenous latent variable. Because the coefficient is the squared correlation of actual and predicted values, it also represents the amount of variance in the endogenous constructs explained by all of the exogenous constructs linked to it. The R² value ranges from 0 to 1 with higher levels indicating higher levels of predictive accuracy in general, R² values of 0.75, 0.50, or 0.25 for the endogenous constructs can be described as respectively substantial, moderate, and weak (Joseph F. Hair et al., 2013)(Hair et al., 2021; Henseler et al., 2009b) Table 4.10 shows the R² values for all endogenous variables. The Purchase Intention (PI) have substantial R² value (0.676) that is 67.6 % of the variance of PI is predicted by the cumulative effect of exognous variables and the remaining 32.4 % is explained by some other unknown variables. Similarly; PEOU (0.42), PU (0.317) have close to moderate R²values.

Table 4.10:

R² and R² adjusted results
Latent Variable	R²	R² Adjusted
PEOU	0.426	0.414
PI	0.676	0.662
PU	0.317	0.288

d. Effect Size f ²

In addition to evaluating the R² values of all endogenous constructs, the change in the R² value when a specified exogenous construct is omitted from the model can be used to evaluate whether the omitted construct has a substantive impact on the endogenous constructs. This measure is referred to as the f² effect size. Guidelines for assessing f² are that values of 0.02, 0.15, and 0.35, respectively, represent small, medium, and large effects (Salkind, 2012)of the exogenous latent variable.

As shown in Table 4.11 the f² values of PEOUàPI (0.234), PUàPI (0.155), SNàPI (0.237), and TRàPEOU (0.356) has large effect. The f² values of SNàPU (0.083), WDàPEOU (0.075), and WDàPU will have medium effect. The f² value of PEOUàPU (0.006), TRàPI (0.011), and TRàPU (0.008) will have small effect on the endogenous corresponding variables if removed from the model.

Table 4. 11: f² values

	PEOU	PI	PU	SN	TR	WD
PEOU		0.234	0.006
PI
PU		0.155
SN		0.237	0.083
TR	0.356	0.011	0.008
WD	0.075		0.070

e. Blindfolding and Predictive Relevance Q²

In addition to evaluating the magnitude of the R² values as a criterion of predictive accuracy, researchers should also examine Stone-Geisser's Q² value (Geisser, 1974). This measure is an indicator of the model's predictive relevance.

The Q² values that are greater than 0 indicate that the exogenous constructs have predictive relevance for the endogenous construct under consideration. The values of 0.02, 0.15, and 0.35, respectively, imply that an exogenous construct has a small, medium, or large predictive relevance for a given endogenous construct as a relative measure of predictive relevance (Q²)(Geisser, 1974)(Joseph F. Hair et al., 2013).

Table 4. 12: Q² values

PEOU	0.234
PI	0.398
PU	0.180
SN
TR
WD

By interpreting these results, we can identify the key constructs with the highest relevance to explain the endogenous latent variable(s) in the structural model. Accordingly, from Table 4.12 we see that PI (0.398) and PEOU (0.234) have large relevance and PU (0.180) has medium relevance to the endogenous variable.

5.1 Conclusion

The major finding of this research provides empirical evidence towards determinates of consumers’ purchase intention on digital market. The data were collected from 100 Addis Ababa University final year engineering students who have online purchase experience. It was analyzed using descriptive statistics and PLS-SEM.

From the findings of descriptive statistics many of the respondents were male and aged between 20–25 from electrical engineering department. The multivariate inferential statistics analysis using Partial Lease Square Structural equation modeling (PLS-SEM) showed that the major determinants of consumers’ purchase intention on digital market platform are Subjective Norm (SN), Perceived Ease of Use (PEOU), Perceived (PU), Website Design (WD), and Trust (TR) consecutively. Subjective norm was found to be the major determinate. The least effective determinant was Trust. The effect of all determinants was significantly positive. The coefficient of determinant (R² = 0.676) to PI shows that the proposed research model was able to explain 67.6% of the variation on consumers’ online digital platform purchase intention (PI) showing the hypothetical model have high prediction power.

By identifying the crucial variables impacting Ethiopian consumers' desire to purchase on online digital market platforms, this study closed the conceptual gap in the context of online market platform purchasing.

The novelty of this study lies in its detailed examination of the relationship between determinants of consumers’ purchase intention as Ethiopian context by developing a new conceptual model quite different from the other researchers. The usage of PLS-SEM analysis method for inferential statistics analysis to determine the direct, indirect, and total effect of independent variables on dependent variables makes this study unique as well.

5.2 Recommendation

With the emergence of digital market platform systems, many firms are confronted with critical difficulties regarding their customers' technological acceptability. Because customers are so important to an organization's bottom line, companies must examine consumers' perspectives on their desire to utilize the digital market platform system.

These research findings have several management ramifications for various online digital market platform businesses in Ethiopia. Respondents in this study want to utilize the online digital market platform because their key people use it, they believe the online platform is easy to use, the website is of excellent quality, and they trust the digital market platform.

Understanding the major factors of customer purchasing behavior is a valuable management decision-making input. With the use of this information, manufacturers, online marketers, and retailers can create effective market strategies, enhance technology, and make smart marketing choices that will help them gain global competitive advantage.

The major contribution of this study is providing an improved understanding of the consumers’ decision-making process as it relates to online digital market platform shopping decisions in Ethiopian context using a new conceptual model based on previous literatures and theories. A theoretical research model of the factors that are predicted to influence consumers’ decisions to shop or not shop online has been developed for this study.

The information on the decision factors obtained from the empirical analysis will benefit future researchers who study consumer behavior in the online digital market platform industry. Moreover, this contribution is especially important as there is only limited published empirical research on the online shopping behavior of Ethiopian consumers.

The limitation of this research is the small sample size. Due to limitation of time, budget and effort this research focused on 100 purposely selected final year Addis Ababa university engineering students who have online purchase experience. Accordingly, it is difficult to generalize the research finding to the other sectors. Only five variables including Subjective norm (SN), perceived ease of use (PEOU), perceived usefulness (PU), website design (WD), and trust (TR) have been tested to see the effect of the variables on consumers’ intention to purchase on online digital market platform.

Future studies should focus on enlarging the coverage of the respondents within appropriated sampling technique to get generalizable result. More variables should be tested to clearly understand consumers’ behavior towards online digital market platform purchase intention.

AVE Average Variance Extracted

BI Behavioral Intention

CR Composite Reliability

PD Platform Design

PEOU Perceived Ease of Usefulness

PI Purchase Intention

PLS-SEM Partial Least Square Structural Equation Modeling

PU Perceived Usefulness

SN Subjective Norm

TAM Technology Acceptance Model

TPB Theory of Planned Behavior

TR Trust

TRA Theory of Reasoned Action

VIF Variance Inflation Factor

WD Website Design

Acknowledgement

First and foremost, praises and thanks to God, the Almighty, for His showers of blessings throughout our work that helped us to complete this Article successfully. we would like to express our deep and sincere gratitude to Addis Ababa University graduating class engineering students for their time in responding to the questionnaires.

Authors Contribution

Mulatu Tilahun Gelaw is the principal author. He has been at each level of the conception: brainstorming, writing, analysis, and discussions. Eshetie Berhan Atanaw is an associate professor. He has made great support during the writing, analysis and discussion of all the results. Gezahegn Tesfaye Dadi is an assistant professor. He has made great support during the data collection, analysis, and discussion of all the results. All authors read and approved the final manuscript.

Funding

This research hasn’t taken any funding

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

The respondents have agreed before filling the questionnaires and the concerned department wrote a letter to collect data.

Consent for publication

The researchers agreed to publish in the journal.

Competing interests

The author declares that there are no competing interests.

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Determinants of Consumers’ Purchase Intention on Digital Business Model Platform: Evidence from Ethiopia Using Partial Least Square Structural Equation Model (PLS-SEM) Technique

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Abstract

Figures

1. Introduction

2. Theoretical Framework, Related Literature, And Hypothesis Development

2.1 Technology Acceptance Model (TAM)

2.2 Theories of Reasoned Action and Planned Behavior

2.3 Perceived usefulness (PU)

2.4 Perceived ease-of-use (PEOU)

2.5 Subjective Norm

2.6 Trust

2.7 Website Design

2.8 Purchase intention

3. Research Methodology

3.1. Conceptual Model

3.2 Sample size and Sampling procedures

3.3 Research Instrument

3.4 The study area and timeframe

3.5. Data collection and processing

4. Results And Discussion

4.1 Descriptive statistics

4.1.1 Profile of respondents

4.2 Inferential statistics analysis

4.2.1 Evaluation of Measurement model

4.2.2. Assessment of structural model

5. Conclusion And Recommendation

5.1 Conclusion

5.2 Recommendation

6. Research Contribution

7. Limitation And Future Study

Abbreviations

Declarations

References

Supplementary Files

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