The impact of online store specifications on enhancing the attractiveness of customer perception of the product: An analytical study of the opinions of a sample of Iraqi virtual store customers

Background

1.1

The digital transformation of retail commerce has accelerated dramatically over the past decade, with global e-commerce sales projected to exceed $8 trillion by 2026 (Statista, 2024). Online shopping offers consumers convenience, price transparency, product variety, and access to global markets (Habes et al., 2022). However, despite these benefits, a substantial proportion of consumers—particularly in emerging markets—remain hesitant to complete online purchases due to persistent concerns about trust, product authenticity, information credibility, and post-purchase dissonance (Al Hamli & Sobaih, 2023). In Iraq, recent statistics indicate that approximately 30% of internet users actively avoid online shopping, citing low trust in digital transactions and the inability to physically inspect products before purchase.

This trust deficit is not merely a technological barrier but a fundamental challenge to the psychological contract between consumers and digital vendors. Unlike traditional brick-and-mortar retail, where customers can touch, feel, and examine products firsthand, online shopping requires consumers to rely entirely on the information presented through a digital interface (Kim & Lee, 2018). Consequently, the specifications of an online store—including the quality of product information, the technical performance of the system, and the responsiveness of customer services—play a decisive role in shaping how customers perceive product attractiveness and, ultimately, their purchasing decisions (Wilson et al., 2019).

Theoretical frameworks

1.2

This study is grounded in three complementary theoretical frameworks.

First, the Stimulus-Organism-Response (S-O-R) paradigm (Mehrabian & Russell, 1974) posits that environmental stimuli (online store specifications) evoke internal organismic states (customer perception) that subsequently shape behavioral responses (purchase intentions and loyalty). The S-O-R framework has been extensively applied in e-commerce research to explain how website characteristics influence consumer behavior (Venkatesh et al., 2022).

Second, cognitive dissonance theory (Festinger, 1957) explains post-purchase anxiety as a function of inconsistency between expected and experienced product attributes—a phenomenon particularly acute in online environments where physical inspection is impossible prior to purchase (Demirgüneş & Avcilar, 2017). Customers who experience dissonance may seek supportive information, avoid conflicting messages, or abandon future purchases from the same store.

Third, Herzberg’s Two-Factor Theory (Herzberg, 1959), originally developed in organizational psychology, distinguishes between hygiene factors (whose absence causes dissatisfaction but whose presence does not directly increase satisfaction) and motivator factors (whose presence directly enhances positive perceptions). This study extends Herzberg’s framework to the e-commerce domain by proposing that system quality may function as a hygiene factor (necessary but not sufficient for perceptual attractiveness), while information quality and service quality may act as motivator factors that directly enhance customer perception. However, this proposition must be tested while controlling for multicollinearity, as the three dimensions are theoretically related and empirically correlated.

Conceptual definitions of key variables

1.3

Online store specifications are defined as the multidimensional characteristics of an e-commerce website that determine user experience, comprising three dimensions (Burman & Iqbal, 2019; Riyadi, 2021):

Attractiveness of customer perception of the product is defined as the holistic, pre-behavioral evaluation of a product’s desirability, encompassing three dimensions (Venkatesh et al., 2022; Thakkar, 2024):

Research problem and gap identification

1.4

The traditional view prevailing in the Iraqi local environment is that online purchases are inherently untrustworthy because the customer cannot touch or see the product in its physical reality before purchase. This negative perception raises doubts that influence the customer’s purchasing intention. Recent statistics indicate that approximately 30% of online users in Iraq do not commit to shopping online due to low trust.

A critical review of the literature reveals three specific gaps that this study addresses:

Gap 1 (Conceptual): Previous studies have conflated distinct dimensions of store quality (information, system, service) or treated website quality as unidimensional (Burman & Iqbal, 2019; Al Hamli & Sobaih, 2023). This conflation obscures potentially differential effects. Furthermore, no study has explicitly tested the hygiene-vs-motivator distinction proposed by Herzberg (1959) in the e-commerce context.

Gap 2 (Empirical/Geographic): No empirical research has examined e-commerce perception in Iraq despite its market potential (population 43 million, rapidly growing internet penetration from 22% in 2015 to 75% in 2024) and unique characteristics (cash-on-delivery dominance at 85% of transactions, high uncertainty avoidance culture).

Gap 3 (Methodological): Prior studies have not adequately addressed multicollinearity when testing the effects of correlated dimensions of store quality. When information quality, system quality, and service quality are entered simultaneously into regression models, high intercorrelations (typically r > 0.60) can suppress or distort individual coefficients. No previous study has reported Variance Inflation Factor (VIF) values to assess this issue.

Research questions

1.5

Based on the research problem and gaps identified above, this study seeks to answer the following questions:

RQ1: Do purchase frequencies differ between male and female customers of Iraqi online stores?

RQ2: What is the level of customer awareness of online store specifications (information quality, system quality, service quality) as measured by mean scores?

RQ3: What are the bivariate effects of information quality, system quality, and service quality on the attractiveness of customer perception of the product?

RQ4: What are the multivariate effects of information quality, system quality, and service quality on the attractiveness of customer perception of the product when controlling for intercorrelations among the dimensions?

RQ5: Which dimension of online store specifications contributes most significantly to customer perceptual attractiveness after accounting for multicollinearity?

Research objectives

1.6

Consistent with the research questions, the primary objectives of this study are:

Research hypotheses

1.7

Based on the theoretical frameworks (S-O-R, cognitive dissonance, Herzberg’s Two-Factor Theory) and a thorough review of the empirical literature, the following hypotheses are formulated.

Figure 1. Conceptual framework of the study.

Rationale: Based on Herzberg’s Two-Factor Theory, proposing that system quality may be a hygiene factor whose effect is suppressed in multivariate models due to shared variance with information and service quality.

Summary of Hypotheses

Significance and contribution of the research

1.8

This study makes three distinct contributions:

Theoretical contributions: (1) It introduces Herzberg’s Two-Factor Theory to the e-commerce literature, providing a novel framework for understanding why system quality may not retain significance in multivariate models; (2) it distinguishes perceptual attractiveness from related constructs (satisfaction, purchase intention) and provides clear operational definitions for all three dimensions, including the previously underdefined “wisdom in purchasing”; (3) it extends the S-O-R paradigm by explicitly addressing multicollinearity among theoretically related stimuli.

Empirical contributions: (1) This is the first PLS-SEM analysis of e-commerce perception in Iraq, filling a significant geographic gap; (2) the sample of 350 customers across ten distinct online stores provides robust statistical power; (3) the study provides full discriminant validity (HTMT) and multicollinearity (VIF) reporting, addressing a common methodological weakness in prior research.

Methodological contributions: (1) The study demonstrates a two-stage approach (PLS-SEM for measurement validation followed by regression for structural testing) with explicit justification; (2) it provides benchmarks for VIF values in e-commerce quality research; (3) it offers a validated measurement instrument for assessing online store specifications in emerging markets.

Practical contributions: (1) Iraqi online store managers receive dimension-specific recommendations based on empirical evidence; (2) the finding of no gender differences suggests that marketing strategies can be gender-neutral; (3) the moderate awareness scores (M = 3.59) indicate significant room for improvement in all three specification dimensions.

Structure of the Paper

1.9

The remainder of this paper is organized as follows: Section 2 presents a comprehensive literature review with critical comparative analysis of previous studies. Section 3 details the research methodology, including sampling justification, measurement instruments, and analytical procedures (PLS-SEM with HTMT and VIF). Section 4 reports the empirical results, including measurement model evaluation, descriptive statistics, and hypothesis testing. Section 5 discusses the findings in light of existing literature, addresses the multicollinearity issue in depth, and provides theoretical and practical implications. Section 6 concludes with recommendations, limitations, and future research directions.

Stimulus-Organism-Response (S-O-R) paradigm

2.1

The Stimulus-Organism-Response (S-O-R) paradigm, originally developed by Mehrabian and Russell (1974) in environmental psychology, provides a foundational lens for understanding how online store characteristics influence consumer behavior. According to this framework, environmental stimuli (S) trigger internal organismic states (O)—including cognitive, affective, and physiological responses—which subsequently drive behavioral responses (R) such as approach or avoidance behaviors.

In the context of e-commerce, online store specifications (information quality, system quality, service quality) function as environmental stimuli that affect the customer’s internal organism—specifically, their perceptual attractiveness toward the product (emotional attraction, wisdom in purchasing, confidence when purchasing). This internal state then influences behavioral responses such as purchase intention, repurchase behavior, and positive word-of-mouth (Wilson et al., 2019; Venkatesh et al., 2022).

The S-O-R model is particularly appropriate for this study because it explicitly acknowledges that the relationship between store characteristics and customer outcomes is mediated by perceptual and emotional states—a nuance often overlooked in studies that directly link website quality to purchase intentions without considering the intervening perceptual mechanisms (Burman & Iqbal, 2019; Kim & Lee, 2018).

Cognitive dissonance theory

2.2

Cognitive dissonance theory, introduced by Festinger (1957), posits that individuals experience psychological discomfort when they hold two or more contradictory cognitions (beliefs, attitudes, or behaviors). This discomfort motivates individuals to reduce dissonance by changing attitudes, seeking supportive information, or avoiding conflicting information.

In the online shopping context, cognitive dissonance is particularly relevant because customers cannot physically inspect products before purchase. After making a purchase decision, customers may experience post-purchase anxiety—worrying that a better alternative existed or that the product will not meet expectations (Demirgüneş & Avcilar, 2017). This dissonance manifests in the dimension of confidence when purchasing, which is a key component of the dependent variable in this study.

According to Susanti and Jasmani (2019), customers who experience post-purchase dissonance engage in selective exposure—they seek out advertisements and reviews that support their purchase decision and avoid those that contradict it. Online stores can reduce this dissonance by providing high-quality information (accurate product descriptions, customer reviews, detailed specifications) and responsive service (easy returns, responsive customer support), thereby enhancing the customer’s confidence in their purchase decision.

Herzberg’s Two-Factor Theory (Motivator-Hygiene Theory)

2.3

Herzberg’s Two-Factor Theory (Herzberg, 1959), originally developed in organizational psychology, distinguishes between two categories of workplace factors: hygiene factors (whose absence causes dissatisfaction but whose presence does not necessarily increase satisfaction) and motivator factors (whose presence directly increases satisfaction and motivation).

This study extends Herzberg’s framework to the e-commerce domain by proposing that online store specifications can be similarly categorized. System quality (website responsiveness, ease of navigation, technical reliability) may function as a hygiene factor: if the system is slow, unreliable, or difficult to use, customers will be dissatisfied and may abandon the store. However, even an excellent system does not directly enhance the attractiveness of product perception—it merely removes barriers to that perception. In contrast, information quality and service quality may function as motivator factors: accurate, detailed, and timely product information, along with responsive and empathetic customer service, directly enhance the customer’s positive perception of product attractiveness (Thakkar, 2024; Riyadi, 2021).

Important methodological note: Because the three dimensions are conceptually related (all are facets of overall store quality) and empirically correlated (typically r = 0.60–0.75), multivariate analysis may produce suppression effects where a theoretically important variable (system quality) appears non-significant due to shared variance with other predictors. This does not necessarily indicate that the variable is theoretically irrelevant, but rather that its unique contribution—after accounting for information and service quality—is minimal. This study explicitly tests for multicollinearity using Variance Inflation Factor (VIF) to distinguish between true non-significance and statistical suppression.

Online store specifications: Dimensions and indicators

2.4

Attractiveness of customer perception of the product

2.5

Previous empirical studies: Critical comparative analysis

2.6

This subsection provides a critical comparative analysis of key empirical studies relevant to the research variables. Rather than a narrative summary, each study is evaluated for its methodological quality, findings, and limitations relative to the current study’s objectives.

Summary table of previous studies with critical evaluation

2.7

Research gap synthesis

2.8

Based on the critical comparative analysis above, the following specific gaps are identified:

Study population

3.1

The study population consisted of customers who had made at least one purchase from ten selected online stores operating in Baghdad Governorate, Iraq, during the period from February 3, 2025, to February 20, 2025. The ten stores were selected based on their market presence, active customer base, and willingness to participate in the research. Table 8 presents the list of stores.

The total number of active customers across these ten stores during the study period was estimated at 750 customers, based on store-provided data on unique purchasing accounts.

Sampling strategy and sample size

3.2

Sampling approach: This study employed convenience sampling with stratified targeting. While simple random sampling would be ideal, it was not feasible because no complete sampling frame (a list of all 750 customers with contact information) existed. Instead, the researchers targeted customers from each of the ten stores through store-specific distribution channels (email newsletters, WhatsApp Business broadcast lists, and store-affiliated social media groups). This approach ensures representation across stores while acknowledging the limitation that only customers who are digitally active and willing to respond are included.

Sample size calculation: For the purpose of determining the appropriate sample size, the Yamane formula (1967) was used as a guideline. For a population of 750 with a 5% margin of error (95% confidence level):

Accordingly, the minimum required sample size is 261 participants. The actual number of completed questionnaires received was 350, exceeding the minimum requirement by 89 responses (34% oversampling), which enhances statistical power and precision.

Data collection procedures

3.3

Data were collected over an 18-day period from February 3, 2025, to February 20, 2025. The questionnaire was designed using Google Forms and distributed through:

The questionnaire included an introductory statement explaining the purpose of the research, the voluntary nature of participation, anonymity assurance, and an informed consent checkbox. Only respondents who provided consent were allowed to proceed.

Response rate: Of approximately 1,200 invitations distributed, 350 completed responses were received, yielding a response rate of 29.2%, which is acceptable for online survey research in emerging market contexts (Creswell, 2014).

Research instrument

3.4

The questionnaire consisted of four sections:

Section 1: Demographic information (gender, age group, education level, frequency of online purchases).

Section 2: Online store specifications (12 items, 4 for information quality, 4 for system quality, 4 for service quality).

Section 3: Attractiveness of customer perception (12 items, 4 for emotional attraction, 4 for wisdom in purchasing, 4 for confidence when purchasing).

Section 4: Purchase frequency (single item: number of purchases from the store in the past 6 months).

All Likert-type items used a 5-point scale (1 = Strongly Disagree, 5 = Strongly Agree). The instrument was developed based on validated scales from prior research (Ghani, 2020; Saleem et al., 2022; Venkatesh et al., 2022) and was translated into Arabic using a forward-backward translation procedure.

Analytical strategy

3.5

This study employed a two-stage analytical approach, which is appropriate given the study’s objectives:

Stage 1: Measurement model validation (PLS-SEM using SmartPLS 4.0)

Stage 2: Structural model testing (Multiple regression using SPSS V.28).

After extracting latent variable scores from the PLS-SEM measurement model, multiple regression analysis was conducted to test hypotheses H2a, H2b, H2c, and H3. Key metrics include:

Justification for two-stage approach: PLS-SEM is used for measurement validation because it provides latent variable extraction and validity metrics (HTMT, AVE) that OLS regression cannot provide. Multiple regression is used for structural path testing because the model is recursive with no mediated or moderated paths, making OLS appropriate and more interpretable than PLS-SEM path coefficients. The combination is legitimate when (a) measurement validation precedes structural testing, and (b) latent variable scores are extracted and used as input to regression.

Ethical considerations

3.6

This study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Scientific Research Ethics Committee, University of Fallujah, Iraq (Approval No. HOF.HUM.2025.001). Written informed consent was obtained from all participants prior to participation. Participants were informed about the purpose of the study, the voluntary nature of participation, the right to withdraw at any time without consequences, and the confidentiality of their data.

Demographic profile of the sample

4.1

Comment on Table 9: The sample is reasonably balanced by gender (46.9% male, 53.1% female). The majority of respondents are in the 26–35 age group (40.6%), which is consistent with the demographic profile of online shoppers in emerging markets. Most respondents hold a bachelor’s degree (60.0%), reflecting the digital literacy required for online shopping. The majority made 3–5 purchases in the past 6 months (44.6%), indicating moderate engagement with online shopping.

Measurement model evaluation (Stage 1: PLS-SEM)

4.2

Descriptive statistics

4.3

Comment on Table 13:

Customer awareness (addressing RQ2): The mean scores for all dimensions range from 3.547 to 3.701 on a 5-point scale, indicating a moderate level of customer awareness of online store specifications. The highest-rated dimension is information quality (M = 3.613), while service quality received the lowest rating (M = 3.565). The coefficient of variation values (21.37%–23.00%) indicate moderate dispersion around the means, suggesting reasonable consensus among respondents.

Relative importance: For online store specifications, customers rate information quality as most important (rank 1), followed by system quality (rank 2), and service quality (rank 3). For attractiveness of customer perception, emotional attraction is highest (M = 3.701), followed by wisdom in purchasing (M = 3.652), and confidence when purchasing (M = 3.547). The lower score for confidence when purchasing (M = 3.547, SD = 0.938, CV = 26.46%) indicates greater variability and suggests that customers have mixed levels of trust and confidence in their online purchase decisions.

Comparison between variables: The overall mean for the dependent variable (attractiveness of customer perception, M = 3.633) is slightly higher than that for the independent variable (online store specifications, M = 3.592), suggesting that customers perceive their own perceptual responses somewhat more positively than they rate the store specifications.

Hypothesis testing

4.4

Summary of key findings

5.1

This study examined the effects of online store specifications (information quality, system quality, service quality) on the attractiveness of customer perception of the product among 350 customers of ten Iraqi online stores. The key findings are:

Discussion of findings in light of previous studies

5.2

Theoretical implications

5.3

Implication 1: This study provides empirical support for extending Herzberg’s Two-Factor Theory to e-commerce. Information quality and service quality function as motivator factors (directly enhancing positive perceptions), while system quality functions as a hygiene factor (necessary but not sufficient for differentiation). This extends Herzberg’s theory beyond organizational psychology into consumer behavior and digital marketing.

Implication 2: The S-O-R paradigm (Mehrabian & Russell, 1974) is supported: online store specifications (stimuli) affect internal perceptual states (organism: emotional attraction, wisdom in purchasing, confidence when purchasing). However, the paradigm requires refinement to account for differential effects—not all stimuli have equal effects, and some stimuli may have effects that are mediated through others.

Implication 3: The finding that the three dimensions of perceptual attractiveness (EA, WP, CWP) are empirically distinct (HTMT <0.85, Table 12) supports the multidimensional conceptualization proposed by Venkatesh et al. (2022). Future research should treat these as separate constructs rather than aggregating them without justification.

Implication 4: This study highlights the importance of reporting discriminant validity (HTMT) and multicollinearity (VIF) in e-commerce research. Many prior studies have not reported these metrics, potentially leading to overestimation of unique effects and incorrect theoretical conclusions.

Practical implications for Iraqi online store managers

5.4

Based on the findings, the following dimension-specific recommendations are provided:

For information quality (strongest unique predictor, β = 0.367, p < 0.001):

For service quality (largest standardized coefficient in multivariate, β = 0.431):

For system quality (not significant in multivariate, but still important as hygiene factor):

For marketing strategy (based on H₁ finding of no gender difference):

Limitations

5.5

Conclusions

6.1

This study examined the impact of online store specifications (information quality, system quality, service quality) on the attractiveness of customer perception of the product among 350 customers of ten Iraqi online stores. Using a two-stage analytical approach (PLS-SEM for measurement validation, multiple regression with VIF for hypothesis testing), the study reached the following conclusions:

Recommendations (Linked Directly to Findings)

6.2

Future research directions

6.3

Reporting guidelines

This study is an observational survey-based research and follows the STROBE reporting guidelines. No CONSORT or ARRIVE checklists are required, as the study does not involve clinical trials or animal experiments.