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Developing a Quantitative Research Design Quasi-Proposal

Developing a Quantitative Research Design Quasi-Proposal

Internet-based technologies used in e-commerce have changed how businesses operate, from marketing transactions to service delivery. It is an essential platform for international competitiveness, particularly for small and medium-sized enterprises (SMEs) with limited resources and market reach (Barroso et al., 2019). In the context of SMEs, several benefits are associated with embracing e-commerce, such as reduced operational costs, increased efficiency, greater market reach, better customer satisfaction, and a culture of innovation, among others (Zain et al., 2020). Despite this fact, e-commerce integration also presents numerous complicated features like technicalities in the systems used, security threats on online platforms of exchange, legal ambiguities pertaining to digital business transactions across borders or nations on the web, lack of trust and organizational inertia to digitization processes (Nurlinda et al., 2020). In this regard, there are many hurdles that make it difficult for small businesses to go through this process. Given this backdrop of complexity, it is critical to understand the multi-faceted implications of integrating electronic commerce into different SME operations across industries and countries. They encompass sales growth rates, profit levels, productivity ratios, consumer loyalty measures, competitive positioning indices, etc. (Povolná 2019). Although prior research has focused on disjointed aspects of the issue under examination, such as customer satisfaction levels only, holistic empirical analysis remains missing from the literature, which justifies the relevance of this study.

Statement of the Problem

This quantitative research study addresses some knowledge gaps regarding the extent of assimilation outcomes across contexts by providing an extensive empirical analysis of the interrelationship between e-commerce adoption and SME performance. E-commerce has witnessed exponential growth, leading to significant disruptions in SME activities in both developed and developing countries globally (Hussain et al., 2022). However, despite growing interest in this area of research, no holistic generalizable insights are available from the literature yet.

These persistent research gaps are related to several aspects. First, most investigations have been limited to a few countries that do not represent other areas (Nurlinda et al., 2020; Hussain et al., 2022). Nevertheless, country-specific contextual conditions such as regulations, infrastructure, and culture influence incorporation strategies, and their consequences require caution when applying such findings (Alzahrani, 2019; Nurlinda et al., 2020). Therefore, wider scopes of these studies across regions are missing.

In addition, contemporary literature mainly focuses on discrete e-commerce formats, including B2C (business-to-consumer) or B2B (business-to-business), without consideration for mixed assimilation at the firm level (Alzahrani, 2019). Nonetheless, many SMEs follow a combination of these options, making disaggregated examinations impossible. Ignoring this aspect limits the understanding of digitalization’s performance implications.

Furthermore, the present body of knowledge mainly concentrates on a single industrial sector like manufacturing or narrow service provision genres (Gamage et al., 2020). This is not true because digitization and output in agriculture might be different from those in healthcare, hospitality or even technology sectors. Hence, a broader cross-sector examination is needed.

Lastly, the linkages between intermediate and ultimate performance measures are rarely modeled holistically (Alzahrani, 2019). The definition of various parameters, such as productivity and profitability, calls for a more elaborate analysis. Consequently, despite this upsurge in digital technology applications that relate to online trading via e-commerce platforms, no profound universal insights are available for SMEs concerning the diverse impacts of e-commerce.

To achieve this purpose, this study will undertake stratified multivariate analysis that makes it possible to relate e-commerce adoption with its performance across locations, industries and hierarchical outcome indicators. As such, it bridges persisting gaps by generating applicable, enriching and nuanced academic and practical conclusions.

Purpose Statement

The main objective of this quantitative study is to determine the impact of e-commerce adoption on SME performance, controlling for some contextual factors like geographical location, industry sector, and organizational size. The independent variable in this study is e-commerce adoption. It will be measured by the level and quality of assimilation in small business enterprises (SMEs). The dependent variables include various areas of SME performance, such as sales growth, profitability, productivity, customer loyalty and competitive advantage. This paper will use robust statistical tools to investigate the association between e-commerce adoption and SME performance by conducting a stratified analysis based on regional classifications, industry affiliations, and enterprise dimensions.

Moreover, the research will also look at the mediating role that contextual elements such as cultural nuances, infrastructure, regulatory frameworks, and market conditions play. The methodical exploration of my survey and multivariate modeling tries to create clarity regarding these goals, making it easier to comprehend how small firms in various operational environments are impacted by e-commerce adoption. This research finding will show possible benefits or losses and, therefore, serve as a basis for making decisions about better positioning in relation to dynamic online commerce. Ultimately, I seek to expand the body of knowledge within academic circles and offer helpful pointers regarding the various ways that the acceptance of e-commerce affects the ability of SMEs to operate in diverse situations.

Research Questions

Research Question 1 (RQ1): To what extent does e-commerce adoption influence SME performance across distinct regions?

Research Question 2 (RQ2): Does e-commerce adoption have the same impact on the performance of SMEs across different industries and sectors?

These research questions attempt to show variations that may exist in the relationship between e-commerce adoption and SME performance with respect to geographical location and industry. One of the research questions is aimed at regional differences, whether the impact of e-commerce integration on SME performance is similar regardless of where they are based. The other one is looking into industrial dimensions that determine if factors specific to the industry influence the effects of assimilating e-commerce on SME performance indicators. All these inquisitions together aim at providing a well-rounded insight into how e-commerce is intertwined with the activities of small and medium-sized enterprises (SMEs), thereby determining different outcomes on operations according to geographical locations (regions) and industries. Finally, the findings will benefit academicians and practitioners involved in navigating the complexities brought about by adopting e-commerce across different regions and industries.


The hypotheses for the research questions include:

Research Question 1

Null Hypothesis 1 (H10): There is no significant difference in SME performance outcomes across regions that can be attributed to levels or types of e-commerce adoption.

Alternative Hypothesis 1 (H11): SME performance outcomes demonstrate significant region-specific variances that are influenced by differing levels and assimilation models of e-commerce adoption.

Research Question 2

Null Hypothesis 2 (H20): There is no substantial deviation in SME performance across industries that is contingent on the nature or scale of e-commerce adoption.

Alternative Hypothesis 2 (H21): Notable performance deviations exist among SMEs across diverse industry verticals, influenced by the disparate levels and approaches of e-commerce integration.

The null hypothesis states that the impact of e-commerce adoption on SME performance does not vary significantly according to geographical context or industry setting. On the other hand, alternative hypotheses suggest that local idiosyncrasies and sectoral factors influence the relationship between e-commerce integration and SME performance. These hypotheses provide significant knowledge about the dynamics that are contextual regarding e-commerce adoption and its effects on SMEs in different locations and industries.


Research Design

The research will involve a quantitative, non-experimental cross-sectional survey to study the association between e-commerce adoption and SME performance across different regions and industries. This will be conducted through an online self-administered questionnaire that collects information from SMEs that are selected by geographical location and industry sectors.

A few factors make this research design stand out. To begin with, the study is aimed at examining measurable relationships among quantifiable variables of e-commerce technology and systems assimilation on SME performance improvement based on sales, profits, customer loyalty, competitiveness, productivity, etc. Such facets that follow standardized scales allow for the use of robust statistical tests as well as multivariate model procedures to measure associations (Creswell, 2014). Therefore, to answer the question of what explains variation in performance, adopting a quantitative approach to predictors’ value would be most applicable.

Moreover, online questionnaires facilitate efficient data collection from a large yet widely distributed representative sample of SMEs within diverse geographical locations and sectors (Sreejesh et al., 2014). The standardization of the approach helps in making comparisons of findings between strata groups to identify patterns that are context-specific. Further, with a non-experimental survey design, it is possible to obtain responses from a wider scope than with qualitative means, increasing generalizability while maintaining realistic goals (Watson, 2015). Additionally, geographic barriers that could obstruct interview-based data gathering across international borders can be overcome.

Other than this, there are several reasons why the non-experimental survey design is most suitable for my dissertation (Punch, 2014). It ensures ecological validity, which is difficult to achieve when dealing with manipulated settings, hence generating externally valid recommendations (Handley et al., 2018). On the other hand, introducing adoption simulations under experimental control may result in some artificial distortions. Moreover, this methodology avoids the ethical complications inherent in randomizing subjects to intervention by measuring various levels of adoption rather than prescribing how integration should be done. Thus, this kind of unobtrusive approach leads to more authentic results.

Moreover, cross-sectional, one-time data collection provides fast yet comprehensive snapshots of the current state of affairs in terms of present-day adoption types and related performance (Sreejesh et al., 2014). This is another way to avoid longitudinal costs as well as maturation and testing threats that would otherwise lead to ambiguity in causal explanations over time. To this end, by utilizing questionnaires and statistical tests more effectively, this design is expected to provide an optimal manner of examining research questions that are robust, ethical, and feasible (Creswell, 2014).

Further, different studies have shown how useful this methodology is for looking at e-commerce and SME performance across different settings, hence justifying its selection (Hussain et al., 2022; Nurlinda et al., 2020). It combines scaling standardization with multivariate tools that make it possible to carry out a wide-ranging study on the complex adoption-performance linkages as well as contingency factors. In doing so, the proposed methodology will address unanswered research questions within existing frameworks, thus filling a knowledge gap on specific regional and industrial aspects affecting e-commerce integration decisions (Handley et al., 2018). Eventually, we hope that the findings will help bridge the gap between practice and theory through broad yet context-sensitive insights.

Operationalization of Variables

The independent variable is e-commerce adoption.

Variable Name: E-commerce adoption

E-commerce adoption is the merging and using of internet-enabled technologies and processes in carrying out business activities online, which include transactions, communications, information sharing, service delivery and relationship management (Rahayu & Day, 2015).

Variable Source: Primary data collected by survey questionnaire

Variable Scoring: E-commerce adoption will be measured through a 5-point Likert scale comprising 12 questionnaire items adapted from Grandon and Pearson (2004) on the degree of adoption in the following areas: Online transactions/sales, online purchasing, website information dissemination, customer analytics utilization, online marketing activities, social media integration, and cloud-based systems and data storage,

Level of Measurement: Interval

Score Range and Interpretation:

1 = No Adoption

2 = Considering Adoption

3 = Early stages of adoption

4 = Moderate adoption

5 = Advanced adoption

Dependent Variable 1: Sales Performance  

Variable Name: Sales performance

Sales performance means the financial outcomes related to business revenues and sales volume generated from selling products and services for a specific period (Katsikeas et al., 2016).

Source of Variable: Primary data collected by survey questionnaire

Scoring of Variable: A 5-point Likert Scale will be used with 6 items adapted from Chen & Tsou (2006) testing sales performance across sales volume, sales revenue, sales growth, profitability, return on investment (ROI), and achievement of sales objectives

Level of Measurement: Interval

Score Range and Interpretation:

1 = Strongly decreased/dissatisfied

2= Somewhat Decreased/ Dissatisfied

3= No change

4= Somewhat Increased/Satisfied

5= Strongly Increased/Satisfied

Dependent Variable 2: Productivity

Variable Name: Productivity

Productivity is the measure of efficiency in using resources such as manpower, capital, time and technology to increase operational output while minimizing costs (Rahman et al., 2018).

Source of Variable: Primary data collected by survey questionnaire

Scoring of Variable: A 5-point Likert Scale adapted from Rahman et al. (2018) will be used with 6 items indicating improvements done in labor productivity, operational efficiency,  capacity utilization, inventory management, technological proficiency, and cost reduction.

Level of Measurement: Interval

Score Range and Interpretation:

1= Strongly Decreased/Dissatisfied

2= Some What Decreased/Dissatisfied

3= No Change

4= Some What Increased/Satisfied

5= Strongly Increased/Satisfied

Dependent Variable 3: Customer Loyalty

Variable Name: Customer loyalty

Customer loyalty is the inclination for purchasers to repeatedly patronize a seller’s offered products that arise out of an intense positive resonance with the brand (Mandhachitara & Poolthong, 2011).

  • Source of Variable: Primary data collected by survey questionnaire

Scoring of Variable: Customer loyalty will be measured using a 5-point Likert scale with 6 items adapted from Mandhachitara & Poolthong (2011): product repurchase intention, brand recommendation, resistance to competitor offerings, long-term preference for the brand, and overall customer retention.

Level of Measurement: Interval

Score Range and Interpretation:

1 = Strongly decreased/dissatisfied

2 = Somewhat decreased/dissatisfied

3 = No change

4 = Somewhat increased/satisfied

5 = Strongly increased/satisfied

Dependent Variable 4: Competitive Advantage  

Variable Name: Competitive advantage

Competitive advantage is a strategic market positioning gain over competitors which is captured in the increased perceived consumer value of products (Murthy, 2022).

Source of Variable: survey data collected in the form of a questionnaire

Scoring of Variable: The 5-item Likert scale with five items adapted from Chen and Tsou (2006) will be used to measure competitive advantage with respect to the product’s superiority, cost competitiveness, uniqueness of selling point, brand image, and reputation.

Level of measurement: Interval

Range and Interpretation of Scores:

1=Strongly decreased/dissatisfied

2=Somewhat decreased/dissatisfied

3=No change

4=Somewhat increased/satisfied

5= Strongly increased/satisfied.

The conceptual definition, measurement tool, scoring technique and level of quantification for these variables constitute the basis upon which the survey development and ultimate analysis of data will be formulated. This multi-tiered measurement approach captures the complexity within both constructs, e-commerce adoption as well as SME performance, thereby permitting a wide-ranging examination of their connection.

Population and Sampling

Specification of Target Population

The study will primarily capture small and medium enterprises across all its industries and locations. Small and medium enterprises (SMEs) are non-subsidiary and independent firms that employ fewer than 200 employees. The research will include both product- and service-based SMEs representing a wide range of industries such as manufacturing, retailing, hospitality, healthcare, financial services, logistics, and technology. Regarding the geographic coverage for the identification of SMEs in the five major regions in North America, South America, Europe, Asia, and Africa. Thus, population specification is defined by organizational parameters of size and sector on one hand and geographical location on another, thus allowing for stratified comparison during analysis.

This research targets owners, presidents, directors, managers, and supervisors responsible for the management of various departmental operations like marketing, production/operations management, and HRM, among others. It is important to note that this study includes SME personnel who hold leadership or functional positions closely associated with main business determinants concerning e-commerce implementation plans (decision-making groups), strategic directions (objectives) as well as performance indicators that cover such aspects as profit margins (efficiency performance measure), sales or revenues (effectiveness measure) level of productivity per employee (efficiency measure), customer loyalty index (effectiveness measure) and competitiveness in general. Therefore, all these variables represent a much larger population framework that provides insights into how e-commerce can be embraced by companies from different angles, considering the perspectives of different nations or continents.

Recruitment Methodology

Judgment and snowball sampling will be used to find organizations and participants for this research. This will involve the use of judgment sampling to identify SMEs that meet criteria in terms of area, sector, scale, and chains of management (Etikan, 2016). Specific firms will be approached on a selective basis based on their fit, such as sectorial segments, locales, and positions. After that, snowball sampling is employed whereby references are given by the already recruited ones (Etikan, 2016). The target group consists of appropriate SMEs within determined categories across professional networks, business associations, and referral chains. Thus, it blends the two techniques, enabling the creation of a rich data set with the diversity required for stratified analysis. Given contextual constraints, it is necessary to work through third parties, such as industry groups, to gain better cooperation from global regions. Therefore, recruitment will be facilitated using these methods along with digital communication channels to generate an adequate sample for rigorous quantitative analysis.

Justification for Target Sample Size

To attain adequate power for significance detection through univariate analyses such as correlation and regression procedures, the G*Power statistical software suggests a target sample size of about 200 SMEs. For correlation analysis, assuming a medium effect size of 0.3, an error probability (α) of 0.05, and power (1–β) of 0.95, we get a recommended sample size of 191 participants; while for linear multiple regression modeling, it is 197 participants (Faul et al., 2009). On the other hand, factorial procedures require larger samples; hence, the individual cell sizes must surpass at least 100 respondents (Wilson Van Voorhis & Morgan, 2007). Since this study spans regions and sectors multi-tiredly, approximately 210 SMEs divided into stratified groups will offer strong statistical analyses and reliable conclusions regarding variances. Therefore, the best target sample would consist of about 30-35 SMEs per sector by region, resulting in approximately 200-210 organizations. This size meets the conditions for multivariate testing and is consistent with feasibility considerations.

Data Collection Procedures

A self-administered questionnaire will be the main tool for collecting standardized data on the adoption of e-commerce and its associated performance outcomes. For dissemination purposes, the questionnaire will be sent out through the online survey platform Qualtrics and made available to the intended respondents via electronic systems, hence improving the effectiveness of the method.

Before circulation can begin, there will be a rigorous procedure for coming up with a valid and reliable questionnaire. As part of this process, previous research on e-commerce adoption and SME performance has been explored by going through various written materials that exist in this field in which scales of measurement have been used. This means that some items were generated from existing instruments to develop an item pool consisting of about forty questions capturing both independent and dependent variables and certain characteristics of participants and organizations (DeVellis, 2017).

Afterwards, an evaluative panel consisting of five experts could validate this draft instrument. Specifically, they are all academicians with areas of expertise in scale development, organizational behavior, performance management, and e-commerce. Such reviewers evaluate each item in terms of how it is relevant to aspects within its domain, including clarity, brevity, comprehensiveness, representativeness based on theoretical framework, reliability, validity, sensitivity, specificity, and feasibility, especially for a particular population. Through such feedback, such questions that would require removal or modification emanate. This is, therefore, an increase in content validity at this stage.

In order to carry out subsequent quantitative scale refinement, issues shall be subjected to exploratory factor analysis using a small sample size pilot study involving 30 representatives from SMEs. The principal investigator made a weak loadings determination to register questions needing deletion, thereby refining the questionnaire further for final confirmation with another pilot group of 30 SME participants regarding its structure and form. Consequently, there will be a need for small sets of robust measures validated through exploratory factor analysis (EFA) that could guarantee sufficient variability in the operationalization of e-commerce adoption and performance outcomes.

In order to cater to the multi-lingual populace, the survey will be translated into French, Spanish, and Arabic, after which validation will be done by back-translation. However, bilingually conducted rigorous back translation will be a verification process to confirm consistency. This will be followed by an ethical review board examining issues of consent, anonymity, confidentiality and data security in the study.

The final step will involve distributing questionnaires online to the target population through email campaigns and other business intermediaries who can facilitate the snowball effect of forwarding messages. The research period will last for two months. To yield a complete analysis, this timeframe will allow us to gather enough responses to reach 15% of the total invitations estimated at 3000, despite excluding duplicate entries blocked by IP addresses. All responses are anonymous when using the survey software and then downloaded for processing with SPSS predictive analytics tools. Missing values or extreme outliers will be checked before hypothesis testing through stringent data screening procedures. Thus, a phased methodology ensures a systematic approach to developing and administering a reliable survey instrument capable of capturing all data needed to answer our study’s empirical questions.

Data Analysis Plan

The first stage of this study will employ a strong quantitative analysis plan that will ensure valid hypothesis testing to find data-driven answers to research questions on the impact of e-commerce adoption on SME performance across regions and industries. To test for variability, the Statistical Package for the Social Sciences (SPSS) predictive analytics software will be used, and its modeler function will have robust multivariate analysis tools. Complexity in addressing core investigation threads is highlighted by the fact that preliminary statistics will involve correlation matrices, two-way multivariate analysis of variance (MANOVA) tests, and multiple linear regression procedures.

Descriptive statistics such as frequencies, percentages, means, and standard deviations will analyze participant demographic variables and organizational attributes that define a baseline understanding of sampled characteristics (Watson, 2015; Trafimow, 2022). Composite scores will also be generated by averaging responses across Likert-scaled statements for multi-item constructs linked to adoption and performance. For validation purposes regarding normal distributions, linearity, reliability, homoscedasticity, and collinearity, these summative continuous variables should be tested (Osborne, 2015).

Pearson’s correlation analysis can then ascertain whether there are any significant linear relationships between adoption predictors and performance outcomes, which might indicate positive/negative/neutral connections within individual datasets (McDonald, 2014). Thus, basic empirical insights may represent assimilation into productivity, sales, loyalty, or competitiveness. Bootstrap resampling methods are used to assess whether these correlations are statistically significant.

For more comprehensive evaluation purposes, multiple linear regression modeling will explore the aggregated and individual predictive efficacy of different facets of adoption when explaining variations in performance (Draper & Smith, 2014). A change in the explanatory power of predictors will show how important they are through hierarchical modeling. Multicollinearity diagnostics must confirm inputs’ independence. In addition to predictive evaluations, effect sizes like Cohen’s f2 depict the magnitude of relationships better (Cohen, 2013). As such, there is empirical verification with regard to adoption variables that show performance relevance using robust statistical modelling.

These questions will be answered through two-way MANOVA tests, which allow for simultaneous comparisons across multiple dependent variables based on two grouping factors – region and sector (Field, 2018). If composite adoption-performance correlations vary statistically by strata, then this demonstrates localization and industry-specific divergence. Following one-way univariate ANOVAs will help to determine individual constructs that have been impacted in terms of the origin of these differences. Descriptive stratified analyses will investigate how macro-environmental and sectoral factors moderate assimilation effects to generate context-specific insights (Field, 2018).

This tabular, graphical, and advanced analytical plan includes descriptive summaries, correlation inspection, predictive modelling and multi-factorial assessments all together. This can lead to actionable findings about how e-commerce usage affects SME performance. Finally, analytic tools should be considered as part of a more detailed research examination as they encompass all levels (contextualized or comprehensive) of analysis, which can give an opportunity to understand the dynamics underlying e-commerce adoption processes in different SME environments.


Internal Validity Threats

This study’s internal validity is threatened because there are a number of possible dangers that might make its derived causal links between e-commerce assimilation and performance differences questionable.  Selection biases constitute one of the main worries, as disproportionate attritions among adoption versus non-adoption groups may skew comparisons (Liu et al., 2022). To maintain representativeness, mitigation strategies include persistent engagement through reminders and personalized communication to avoid inequitable dropout rates.  This issue must also be considered in terms of testing effects since participants may experience pre-survey notification that accidentally increases their mindfulness, leading to their reactive behavior rather than natural occurrences for the research (Handley et al., 2018). In order to minimize reactivity, diversionary language is used in the pre-notification.

Experimental mortality and maturation threats do not have any bearing on this cross-sectional survey. However, subjectivity can be introduced by the self-reported nature of data collection vis-à-vis actual adoption status or performance, although confidentiality is ensured so that honest responses will be given (Rindfleisch et al., 2008; Slocum et al., 2022). Nevertheless, common method biases pose a challenge as they cause procedural remedies such as scale triangulation through incorporating multi-item measures with different construct types – reflective vis-à-vis formative (Flannelly et al., 2018). In addition, electronic data collection avoids interviewer bias or inconsistencies. Consequently, as asserted by Leviton and Trujillo (2016), it requires concerted efforts aimed at promoting internal validity, including rigorous scale validation, randomized ordering, anonymous digital interfaces, and stratified sampling, thereby minimizing any confounding factors that may affect its results.

External Validity Threats

The study’s external validity is threatened by population representativeness. The likelihood of self-selection biases because of willingness to participate could lead to variability in the propensity for adoption or variations in performance between volunteers and the general population (Lynn & He, 2022). Given this possibility, sampling limitations need to be acknowledged in extrapolating insights. Moreover, the priori targeting of participants across sectors and hierarchies is aimed at maintaining breadth. Alternatively, it is possible that those with extremely bad or good views have been selected because of the voluntary nature of the survey, thus leading to biased data. However, confidentiality can help to minimize this risk by encouraging sharing across the spectrum.

Environmental interactions may also pose threats as cultural orientations towards technology, market readiness, and regulatory policies differ between regions, thereby exerting externalities that are not captured within this research (Dong & Peng, 2013). Despite failing to isolate these interaction effects entirely, however, the geographical range of respondents provides a balanced perspective on this issue. Therefore, while external validity is problematic, scale validation has mitigated its challenges in several ways, including anonymity policies and stratified sampling through moderation that enable insights into how adoption and performance are related dynamically, though not linearly.

In summary, selection biases, testing effects, and subjectivity issues remain relevant here despite all attempts to ensure survey accuracy, such as rigorous scale validation, randomized ordering, anonymous digital interfaces, and stratified sampling. External validity could be limited by population representativeness, while environmental interaction might hinder generalization across all regions. To contextualize these limitations allows for more specific findings rather than generalized conclusions (Dehejia et al., 2019). Nonetheless, adopted safeguards protect against confounding variables so that overall patterns regarding assimilation and performance can emerge from them. Therefore, while other validity threats still apply here, strategic mitigation measures enhance the paramount importance of established empirical relationships, suggesting meaningful contributions toward closing these knowledge gaps pertaining to this business-critical concern.


In conclusion, the study aims to apply a quantitative approach to fill the knowledge gap in understanding e-commerce adoption and SME performance across geographical and industrial boundaries. The in-depth analysis is aimed at transcending fragmented literature which lacks contextual applicability. This will be achieved by applying correlational and multivariate analytic techniques that cut across regions and sectors, which would give an explanation of strategic orientations and optimization pathways within the complex domain of digitization.

The basis of this study will be on survey methodology with the intention of collecting standardized data from a large and diverse sample. In addition, the proposed methods included secure scale development, sampling tactics, and statistical testing procedures leading to generalizable empirical statements. The research findings have academic implications, entailing enriching scholarly platforms for sharing contemporaneous information while assisting practitioners who are involved in subtle processes of integrating e-commerce into strategic preparations guiding competition. Despite the inability of the above-mentioned methodology to completely minimize validity threats, it is clear that adopted controls strengthen the accuracy of results. This means that this quantitative framework ultimately provides a model by which an evidence base can be enhanced from which digitization strategies are formulated considering localization requirements, industry drivers, and business capabilities that ensure long-term sustainability in operational landscapes. As such, it addressed current gaps where actionable insights for emerging SMEs coping with globalizing e-commerce must be reconciled with context exigencies. These issues remain dynamic. Consequently, future research has to endeavor to close knowledge gaps through continued inquiry utilizing multidimensional data analytic approaches commensurate with complexities observable during real-world technological disruption and performance transformations.


Alzahrani, J. (2019). The impact of e-commerce adoption on business strategy in Saudi Arabian small and medium enterprises (SMEs). Review of Economics and Political Science, 4(1), 73–88.

Barroso, R. M. R., Ferreira, F. A. F., Meidutė-Kavaliauskienė, I., Banaitienė, N., F. Falcão, P., & Rosa, Á. A. (2019). Analyzing the determinants of e-commerce in small and medium-sized enterprises: A cognition-driven framework. Technological and Economic Development of Economy, 25(3), 496–518.

Chen, J., & Tsou, H. (2006). Information technology adoption for service innovation practices and competitive advantage: The case of financial firms. Information Research, 12(3), 314.

Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Routledge.

Creswell, J. W. (2014). Research design: Qualitative, quantitative and mixed methods approaches (4th ed.). SAGE Publications.

Dehejia, R., Pop-Eleches, C., & Samii, C. (2019). From local to global: External validity in a fertility natural experiment. Journal of Business & Economic Statistics, 37(4), 545–558.

DeVellis, R. F. (2017). Scale development: Theory and applications (4th ed.). SAGE Publications.

Draper, N. R., & Smith, H. (2014). Applied regression analysis (3rd ed.). John Wiley & Sons.

Etikan, I. (2016). Sampling and sampling methods. Biometrics & Biostatistics International Journal, 5(6).

Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160.

Field, A. (2018). Discovering statistics using IBM SPSS statistics. SAGE Publications.

Flannelly, K. J., Flannelly, L. T., & Jankowski, K. B. (2018). Threats to internal validity in experimental and quasi-experimental research in healthcare. Journal of Health Care Chaplaincy, 24(3), 107–130.

Gamage, S. K. N., Ekanayake, E., Abeyrathne, G., Prasanna, R., Jayasundara, J., & Rajapakshe, P. (2020). A review of global challenges and survival strategies of small and medium enterprises (SMEs). Economies, 8(4), 79. MDPI.

Grandon, E. E., & Pearson, J. M. (2004). Electronic commerce adoption: An empirical study of small and medium US businesses. Information & Management, 42(1), 197–216.

Handley, M. A., Lyles, C. R., McCulloch, C., & Cattamanchi, A. (2018). Selecting and improving quasi-experimental designs in effectiveness and implementation. Annual Review of Public Health, 39(1), 5–25.

Hussain, A., Akbar, M., Shahzad, A., Poulova, P., Akbar, A., & Hassan, R. (2022). E-commerce and SME performance: The moderating influence of entrepreneurial competencies. Administrative Sciences, 12(1), 13.

Katsikeas, C. S., Morgan, N. A., Leonidou, L. C., & Hult, G. T. M. (2016). Assessing performance outcomes in marketing. Journal of Marketing, 80(2), 1–20.

Leviton, L. C., & Trujillo, M. D. (2016). Interacting threats to internal and external validity in intervention studies. Evaluation Review, 40(2), 196–231.×16659650

Liu, M., Lee, S., & Conrad, F. G. (2022). Accounting for nonresponse bias in web survey data collection. Social Science Computer Review, 40(1), 57–77.

Lynn, P., & He, L. (2022). Representativeness of volunteers for probability-based online panels: Analysis of socio-demographics, lifestyles, attitudes, and behavior in Great Britain. Methods, Data, Analyses: A Journal for Quantitative Methods and Survey Methodology (mda), 16(1), 125–148.

Mandhachitara, R., & Poolthong, Y. (2011). A model of customer loyalty and corporate social responsibility. Journal of Services Marketing, 25(2), 122–133.

McDonald, J. H. (2014). Handbook of biological statistics (3rd ed.). Sparky House Publishing.

Murthy, V. (2022). Modern competitive strategy (1st ed.). SAGE Publications.

Nurlinda, N., Napitupulu, I. H., Wardayani, W., Azlina, A., Andina, A., Ulfah, A., & Supriyanto, S. (2020). Can e-commerce adoption improve SME’s performance? (Case studies on micro, small, and medium enterprises with Gojek services in Indonesia). Proceedings of the Third Workshop on Multidisciplinary and Its Applications.

Osborne, J. (2019). What is rotating in exploratory factor analysis? Practical Assessment, Research, and Evaluation, 20(1).

Povolná, L. (2019). Innovation strategy in small and medium-sized enterprises (SMEs) in the context of growth and recession indicators. Journal of Open Innovation: Technology, Market, and Complexity, 5(2), 32.

Punch, K. F. (2014). Introduction to social research: Quantitative and qualitative approaches. SAGE Publications.

Rahayu, R., & Day, J. (2015). Determinant factors of e-commerce adoption by SMEs in developing country: Evidence from Indonesia. Procedia – Social and Behavioral Sciences, 195, 142–150.

Rahman, S., Yaacob, Z., & Radzi, R. (2018). An empirical investigation of Muslim entrepreneurs’ efficiency in Malaysia. International Journal of Bank Marketing, 36(2), 306–324.

Rindfleisch, A., Malter, A. J., Ganesan, S., & Moorman, C. (2008). Cross-sectional versus longitudinal survey research: Concepts, findings, and guidelines. Journal of Marketing Research, 45(3), 261–279.

Sreejesh, S., Mohapatra, S., & Anusree, M. (2014). Business research methods: An applied orientation. Springer.

Slocum, T. A., Pinkelman, S. E., Joslyn, P. R., & Nichols, B. (2022). Threats to internal validity in multiple-baseline design variations. Perspectives on Behavior Science, 45.

Trafimow, D. (2022). Confounds, manipulations, levels of treatment, and matched random assignment in experimental research. Social Sciences, 11(7), 284.

Watson, J. (2015). Descriptive statistics. Wiley StatsRef: Statistics Reference Online

Wilson Van Voorhis, C., & Morgan, B. (2007). Understanding power and rules of thumb for determining sample sizes. Tutorials in Quantitative Methods for Psychology, 3(2), 43–50.

Zain, Z. M., Jusoh, A. A., Ros, & Putit, L. (2020). Drivers of e-commerce adoption amongst small & medium-sized enterprises (SMEs) in the business service sector. Journal of International Business, Economics and Entrepreneurship, 5(1), 50–58.


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develop and present a quasi-proposal for your intended research or quantitative example that you have been working with for this course.

While the most important elements in any proposal are the fundamentals of the problem, purpose, and research questions, the bulk of the assignment will be the methodology. Organize your quasi-proposal for this assignment by subheadings. The actual headings and information that need to be included depend on the method used to collect the data. In studies involving the collection of primary data, you want to be sure to include the details of your sampling plan, measurement of variables, the actual data collection procedure, plan of analysis, and justification for your decisions.

Developing a Quantitative Research Design Quasi-Proposal

Developing a Quantitative Research Design Quasi-Proposal

Include the following information, using these headings, in your quasi-proposal:

Statement of the Problem
Purpose Statement
Research Questions
Null and Alternative for each research question
Research Design: Specific quantitative method to be used and rationale. Cite works related to your decision.
Operationalization of Variable: Specification of the concepts to be measured for each variable/construct.
State variable/construct name,
Define the variable/construct,
Identify the source of the variable/construct,
Describe how the variable/construct is scored,
Identify the level of measurement of the variable/construct,
Identify the range and interpretation or group classification of the variable/construct.
Specification of the population, recruitment method, and target sample size with justification.
Data collection procedure: Explanation of how the data will be collected.
Intended data analysis: Explain how the data will be analyzed to test the hypotheses and provide answers to the research questions. Provide your rationale.
Validity: Any plausible threats to internal and external validity.
In this assignment, you are expected to incorporate all previous instructor feedback. Your prospectus must be in APA format and be of the quality expected of doctoral-level work. All research elements must be in alignment and reflect a cohesive and comprehensive research study.

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