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COVID-19 Pandemic and Research on the Mediating 
Role of Absorptive Capacity on the Relationship 
between Business Analytics Capability and Small-to-
Medium-Sized Enterprise Performance 
Benjamin A. Holman | Founder, Saving David, Inc. 

Christine C. Whitaker | Columbia Southern University, Orange Beach, Alabama 

Contact: christine.whitaker@columbiasouthern.edu  

Abstract 
This initial study's key purpose was to investigate whether absorptive capacity performed a 
mediating role on the relationship between business analytics capability and small-to-medium-
sized enterprises (SMEs) performance in the United States. The study's findings are more relevant 
now as the COVID-19 pandemic continues to impact small-to-medium-sized enterprise (SME) 
businesses in the United States. The study derived from the resource-based view approach, the 
dynamic capabilities dimension perspective, through the theoretical lens of the reconceptualization 
of absorptive capacity by Zahra and George (2002). The study empirically tested mediation 
through a series of regressions based on Baron and Kenny (1996). As recent extensions of the 
resource-based view theory, absorptive capacity and business analytics embody the dynamic 
capabilities, business intelligence, and knowledge management for modern business firms to align, 
modify, and reconfigure during a dynamic and volatile global business environment. Throughout 
the pandemic, it appears businesses have had to continually regain economic ground. This paper is 
an effort to apply the findings of research on the importance of absorptive capacity and business 
analytics capability as it pertains to the reality of the COVID-19 pandemic on SMEs. The study 
contributed to the theoretical body of knowledge on dynamic capabilities by empirically 
demonstrating how SMEs may manifest improvement in performance in the twenty-first century 
by utilizing data in collaboration with business analytics at the realized absorptive capacity 
dimension. In wake of the ongoing COVID-19 pandemic, exploring the research study's findings 
may provide insight to help SMEs survive during these difficult times, and even thrive, by 
increasing their absorptive capacity to utilize data analytics effectively. 

Keywords: Resource-Based View, Absorptive Capacity, Business Analytics, Dynamic 
Capabilities, Information Technology, Small-To-Medium-Sized Enterprise (SME), COVID-19 

mailto:christine.whitaker@columbiasouthern.edu


 
38 January 2022 | Volume 1, Number 1 

Introduction 
With the catastrophic global health crisis caused by the COVID-19 pandemic, a catastrophic 
global economic crisis has also occurred, bringing severe repercussions for small-to-medium 
enterprises (SMEs). Studies have revealed that SMEs struggled through the shut-down due to 
quarantine restrictions across every business sector with a loss of revenue, loss of workers, and 
many closing their doors altogether (Adam & Alaifi, 2021). To survive the COVID 19 pandemic, 
many SMEs have been forced to adapt to digital technologies and move to online platforms to 
keep their current customer base while seeking to gain new ones (Gudandovskaya & Liniņa, 
2021). Understanding how to utilize digital data might be considered the holy grail of business 
management in the twenty-first century: the digital technology era where everywhere people go, 
there is a digital trace recorded, stored, and retrievable (English & Hoffman, 2018; Mahmood & 
Mubarik, 2020). Dramatic technology changes were already taking place with how businesses are 
conducted with upwards of forty billion Digital data devices connected digitally by 2021 (Tang, 
2018). A decline in the SME share in the economy of five percent year-over-year was already 
occurring before the pandemic, with the reduction amounting to a potential loss of five percent of 
five trillion dollars in the gross domestic product (GDP) (Kobe & Schwin, 2018). The COVID-19 
pandemic brought even more dramatic change with the mandated quarantine on a global scale 
resulting in a struggle for many SMEs. With people remaining in their homes, earning less, 
spending less, many SMEs’ survival depended on the level of absorptive capacity and the ability to 
utilize digital technology (Adam & Alaifi, 2021). Absorptive capacity is an SME's ability to 
identify the value of new information, assimilate it, and apply knowledge from external sources 
(Ajeeli, 2018). Guo et al. (2020) found that SMEs who can identify and adopt digital technology 
are more able to survive the pandemic. COVID-19 warrants revisiting research examining the 
mediating role of absorptive capacity on business analytics to potentially improve performance in 
SMEs during the pandemic and into the digital future. In presenting the research study, 
methodology, findings, results, and applications to the current COVID-19 pandemic SME 
environment, insights may be gleaned to assist SMEs in navigating the tumultuous environment by 
utilizing business analytics for knowledge and other tools through increasing absorptive capacity. 

Research 
In a modern era with data from various modes, it is incumbent upon a modern business to possess 
varying degrees of capacity for creating value from digitized data (Ajeeli, 2018; Alessandra et al., 
2016; Sakhdari, 2016). The purpose of the initial research study was to address the gaps existing in 
the literature on whether absorptive capacity performed a mediating role on the relationship 
between business analytics capability and small-to-medium-sized enterprises (SMEs) performance 
(Qian & Jung, 2017; Turulja et. al., 2017). The theory postulated that in the twenty-first century 
absorbing new knowledge helps organizations create value; the inference is value creation is 
restricted if absorptive capacity is low or non-existent (Božič et al., 2016). 



 

 

39 Business Management & Research Applications: A Cross-Disciplinary Journal 

Research Significance 
By showing the connection through a microlevel process in line with the dimensions presented by 
Zara and George (2002), the study attempted to unlock the black box on how SME ownership 
elicited more data transformation into knowledge capability through business analytics and, 
thereby, enhancing performance. First, it identified the role absorptive capacity among ownership 
plays in fostering knowledge, revealing critical capability antecedents, and providing new 
theoretical arguments regarding how such precursors are linked. Second, the research responded to 
calls for research on capabilities among small-to-medium-sized enterprises (SME) and 
entrepreneurs from a resource-based view approach (Ajeeli, 2018; Qian & Jung, 2017; Vidgen et 
al., 2017). Finally, the study contributed to research on SME absorptive capacity apparatuses as 
essential building blocks for knowledge generation. 

Congruent to prior points is the application to the COVID-19 pandemic period. Thus, the 
contribution of this paper is three-fold. First, it adds to the theoretical literature regarding dynamic 
capabilities by empirically demonstrating the mediating role absorptive capacity plays on business 
analytics capabilities among SME members for improving performance. The study's findings 
exposed the extent to which SMEs collected and processed data and managed knowledge for better 
decision-making for improving performance. Second, this paper demonstrates how business 
analytics capabilities and absorptive capacity shape the value creation and value capture processes 
of businesses. In particular, the study postulates that the ability to exploit opportunities during a 
crisis is by imitating and innovating using modern technologies to extract knowledge from data. 
Third, with billions of devices connected digitally, a lifeline for most organizations during the 
COVID 19 pandemic was in utilizing technology for extracting knowledge from data. 

Moreover, by extracting knowledge from digital data through business analytics at the realized 
absorptive capacity dimension, SMEs can innovate, adjust resources quickly, seize new 
opportunities, tap into viable new networks, change information technology systems, and 
reconfigure organizational structures. In times of crisis, lack of preparedness and low level of 
innovativeness often force companies to close, lay off employees, defer new products, services, or 
restrict market expansion. Thus, the dynamic capabilities offered through realized absorptive 
capacity identified in the study confer benefits to SMEs for weathering the COVID-19 pandemic. 
Therefore, in presenting the research study, methodology, findings, results, and applications to the 
current COVID-19 pandemic SME environment, insights gleaned could assist SMEs towards the 
significance of business analytics and absorptive capacity. 

Research Objective 
The global COVID-19 pandemic brought an economic crisis, along with the catastrophic health 
crisis, which changed the way people buy and spend, which brings about severe repercussions for 
SMEs not using technology (Adam & Alaifi, 2021). Indeed, to survive the COVID 19 pandemic, 
many small-to-medium-sized enterprises (SMEs) companies have had to adopt digital technologies 
and move to online platforms to keep their current customers while seeking to gain new customers 
(Gudandovskaya & Liniņa, 2021). Studies reveal an upward trend in SME failures and, 
correspondingly, a decline in their share of the economy of five percent year-over-year (Kobe & 
Schwin, 2018). This reduction amounts to a loss of five percent of five trillion dollars overall in 



 
40 January 2022 | Volume 1, Number 1 

GDP of over two billion dollars and brings into question the future of SMEs. At the same time, 
large or big businesses have increased their data analytics investment by spending over one 
hundred billion dollars towards big data in 2016 (Trelewicz, 2017). 

Before the pandemic, research had already exposed the high failure rate of SMEs compared to 
large businesses. At the same time, the research highlighted the fact that larger businesses 
embraced business analytics technologies at a greater than many SMEs. Thus, the researchers 
aimed to focus on applying the study's findings to assist SMEs' performance during the crisis and 
into the future. In this regard, it seemed applicable to revisit as a result of the COVID-19 pandemic 
(Kobe & Schwin, 2018; U.S. Small Business Administration, 2020). The research considered 
business analytics as a range of technological tools, applications, and abilities for transforming 
data into insightful knowledge. In the research literature, business analytics was widely accepted 
as an essential resource for businesses of all sizes (English & Hoffmann, 2018; Bayrak, 2015). 
Two areas for further inquiry were discovered: the low level of adoption of technology in the form 
of business analytics and the lack of absorptive capacity among SMEs. 

Literature Review 
Before delving into the main focus of the research study, it is important to mention that since the 
outbreak of COVID-19 and the Delta variant, there have been many instances of human suffering, 
historically pinned as one of the most significant global health crises in modern times (Lu, Wu, 
Peng, & Lu, 2020). Furthermore, it is established that along with this human suffering, the 
coronavirus has also brought significant severe or devastating effects on all businesses, especially 
SMEs. This is witnessed in the fluctuating business environment in terms of market volatility, 
supply chain restraints, and drastic fluctuations in demand, respectively (Gong, Hassink, Tan, & 
Huang, 2020). Hence, it is the focus of this paper and the applicability of the research to 
understand the various ways SMEs can adapt to sustaining their business operations and enhancing 
their business performance during the COVID-19 pandemic. 

Addressing the Gap 
It can be duly stated that any crisis, irrespective of its origin, i.e., human-made or natural causes, 
carries the potential for damage on any business in terms of survival (Donthu & Gustafsson, 
2020). Research cites the failure rate of SMEs remains significantly high compared to larger 
businesses that often embrace business analytics (Kobe & Schwin, 2018; U.S. Small Business 
Administration, 2020). COVID-19, the Delta variant, or any crisis serves as a nefarious purpose 
for exerting pressure on a business restricting their capability to change in a short period to crisis 
appropriately. In congruence with such points, reduction in both sales along with economic 
difficulties of the times, SMEs can face issues in cash flow, inventory supply, operations, human 
resources, and strategy that levy the type of stress to their potential demise (Omar et al., 2020). In 
times of general crisis, and the current COVID-19 pandemic, many SMEs are in danger of closing 
their doors (Guo et al., 2020). Examining the research study's findings may provide further 
insights and tools to contribute to lowering the current failure rate of SMEs. 



 

 

41 Business Management & Research Applications: A Cross-Disciplinary Journal 

Hypothesis of the Research 
In times of crisis, a SME’s survival can depend on the level of absorptive capacity and the ability 
to utilize digital technology (Adam & Alaifi, 2021, Guo et al., 2020). The research questions 
examine if potential and realized absorptive capacity mediates the relationship between business 
analytics capability and SMEs performance. Examining the findings can potentially provide 
insight into increasing absorptive capacity and improving SME performance. Based on the 
literature review conducted for the initial study, the following research questions and hypotheses 
were constructed: 

RQ1: Does potential absorptive capacity mediate the relationship between 
business analytics capability and SME performance in the United States? 

- H1o: Potential absorptive capacity does not mediate the relationship between 
business analytics capability and SME performance in the United States. 

- H1a: Potential absorptive capacity does mediate the relationship between business 
analytics capability and SME performance in the United States. 

RQ2: Does realized absorptive capacity mediate the relationship between 
business analytics capability and SMEs performance in the United States? 

- H2o: Realized absorptive capacity does not mediate the relationship between 
business analytics capability and SME performance in the United States. 

- H2a: Realized absorptive capacity does mediate the relationship between business 
analytics capability and SME performance in the United States. 

Theoretical Framework 
The purpose of the initial research study was to address the gaps existing in the literature on 
whether absorptive capacity performed a mediating role on the relationship between business 
analytics capability and small-to-medium-sized enterprises (SMEs) performance (Qian & Jung, 
2017; Turulja & Bajgorić, 2018; Vidgen et Al., 2017). Several theories identified conditions, 
behaviors, resources, and capabilities leading to competitiveness or competitive advantage 
(English & Hoffman, 2018; Fillingim, 2018; Ghasemaghaei, 2019; Garg & Khullar, 2020). Among 
them was the absorptive capacity theory (Djerdjouri, 2020; Duan et al., 2020; Vidgen et al., 2017). 
Using the absorptive capacity framework, SMEs were able to display their business analytics 
capabilities—how information was processed and efficiently used (Qian & Jung, 2017; Turulja & 
Bajgorić, 2018; Vidgen et al., 2017; Zahra & George, 2002). 

Published research identified two areas for further inquiry, the low level of adoption of technology 
in the form of business analytics and the absence of absorptive capacity by SMEs (Ajeeli, 2018). 
With digitization such as radio frequency identification (RFID), wireless sensor networks (WSN), 
the internet, cloud computing, and internet of things application software with electromagnetic 
fields spurring major technological breakthroughs, the need for knowledge generation from 
business analytics for SMEs seem paramount (Bayrak, 2015; English & Hoffmann, 2018; Lee & 
Lee, 2015; Tang et al., 2018). The resource-based view (RBV) approach is among the approaches 



 
42 January 2022 | Volume 1, Number 1 

used to investigate business phenomena and frequently cited in the literature (Ajeeli, 2018; Islami 
et al., 2020). 

Recent extensions of RBV include dynamic capabilities, business intelligence, and knowledge 
management perspectives, which attempt to address the need for a firm to align, modify, and 
reconfigure in a dynamic and volatile global business environment (Solesvik, 2018). The theory 
supports the importance of business analytics capability and promotes the idea of unique resources 
that contribute to competitive advantage (Wójcik, 2015). Current thought puts forth that such 
special resources have certain fundamental qualities: valuable by providing opportunities or 
neutralizing threats, rare, imperfectly imitable, and non-substitutable no strategic equivalent 
substitute for, neither rare nor imperfectly imitable (Solesvik, 2018). Emphasis on rare emerges 
because it is a key element of the meaning of valuable within digitized data; valuable and rare 
resources afford a significant competitive advantage. 

Business Analytics Defined 
Business analytics refers to the scientific process, including technologies and practices, to collect 
and translate data into knowledge for strategic decision-making (Mahmood & Mubarik, 2020; 
Tang et al., 2018). Large companies use business analytics for improving performance: improve 
decision-making, enable the creation of innovative and valuable products and services, reduce 
costs, increase sales, or expand into new markets (Fillingim, 2018; Ghasemaghaei, 2019; Garg & 
Khullar, 2020). For business intelligence or knowledge, it is widely accepted as crucial and 
considered a key factor in large business domination over smaller ones as many smaller SMEs 
lack the resources or capability to use business analytics successfully (English & Hoffman, 2018; 
Fillingim, 2018; Ghasemaghaei, 2019; Garg & Khullar, 2020). A limited number of studies had 
focused on the paradigm involving SMEs' performance and the association with a deficiency in 
knowledge due to deficiency in business analytics capability and the lack of absorptive capacity 
(Ajeeli, 2018). As such, this study examined the mediating role of absorptive capacity on the 
relationship between business analytics capability and small-to-medium-sized enterprises (SMEs) 
performance (Qian & Jung, 2017; Turulja & Bajgorić, 2018; Vidgen et al., 2017). 

Business analytics encompasses methodologies, databases, machine architectures, analytical 
algorithms, skills, and processes to facilitate analyzing data for making pragmatic and strategic 
business decisions. It is more of a process rather than a product encompassing a range of tools and 
applications for transforming data into insightful knowledge (English & Hoffmann, 2018). 
Business analytic technologies and software platforms include metadata collection, business 
reporting, spreadsheets, search tools, online analytical processing (OLAP), data mining, modeling, 
predicting, or forecasting, performance management, customer relationship management (CRM), 
management information systems (MIS), and data extraction, transformation and loading (ETL) 
(Bayrak, 2015). 

The Cost of Low Absorptive Capacity 
The term, absorptive capacity, is credited to Cohen and Levinthal (1990) based on positing 
research and development (R&D) activity creates new knowledge and innovation and that 



 

 

43 Business Management & Research Applications: A Cross-Disciplinary Journal 

absorptive capacity improves an organization's ability to identify, assimilate, and exploit 
knowledge from external sources (Ajeeli, 2018; Cohen & Levinthal, 1989; Daspit et al., 2016; 
Sakhdari, 2016). The theory postulates that absorbing new knowledge helps organizations create 
value, then, the inverse is value is not created when absorptive capacity is restricted (Božič & 
Dimovski, 2019; Sakhdari, 2016). The problem identified in the literature supports the need for the 
study to address challenges with SMEs ability to process not only information but also the ability 
to recognize and create value (Ghasemaghaei, 2019; Sjödin et al., 2019; Turulja & Bajgorić, 2018; 
Vidgen et al., 2017; Yang & Tsai, 2019). 

Studies have established, when absorptive capacity is low, the ability to acquire, assimilate, 
transform, and exploit knowledge is restricted (Diaz-Molina, 2019; Jiménez-Barrionuevo et al., 
2019). Hence, the problem was two-fold. First, the issue involved implementing business analytics 
capability to extract valuable information from data. Second, the problem consisted of having the 
absorptive capacity needed to recognize the value in new information and create useful products 
and services, spur operational savings, or spearhead market expansion and, thereby, enhance 
small-to-medium-sized enterprises’ (SMEs’) performance. Thus, added to the challenge of 
collecting and utilizing technology in the form of business analytics is, once adopting such 
techniques, having the ability to recognize the value of information extracted from the data 
(Cenamor et al., 2019). 

Crucial Connections to Data 
In the current business environment, it is critical for SMEs to leveraging technology to analyze 
data and possessing a level of absorptive capacity for improving performance and surviving 
(Jiménez-Barrionuevo, García-Morales, and Molina, 2019). As Pape (2016) explains, the problem 
is exacerbated by the difficulties with processing vast volumes of data simultaneously at the high 
velocity at which data arises in different forms (e.g., text, video, numerical, images) in the digital 
age, along with the need to filter out irrelevant information (Tang et al., 2018). There were 
2.5x106 terabytes of data in 2016 created or generated every day with an estimation of data 
generation doubling every forty months (Coleman et al., 2016). By the end of 2021, estimates 
project upwards of forty billion devices will be connected digitally (Tang et al., 2018). That is 
hardware connecting devices to networks and servers, software applications providing access and 
control of connected systems. Practically everything that could be connected will be connected 
(Tang et al., 2018). 

Conceptual model 
The model can be hypothesized as follows: the independent construct was business analytics 
capability, whereas the dependent constructs was performance of small-to-medium-sized 
enterprises (SMEs) with the mediating variables of potential and realized absorptive capacity. 
Measurement of data use, technology, and people impacting operations, innovation, competitive 
advantage, and organizational performance were considered. 



 
44 January 2022 | Volume 1, Number 1 

Methodology Materials and Methods 
The research methodology encompasses approaches, philosophies, and designs to address the 
research aim and objectives. Creswell and Creswell (2019) present methodology as the roadmap 
for answering research questions with several options available in dealing with the research 
philosophy. The study took on the post-positivist worldview, which rejects the notion truth is 
absolute, and what is known about reality can be challenged through specific observations and 
measurements with identification of causes that influence effects and testing. The quantitative 
approach supports the post-positivist traditional scientific research method through the chosen 
deductive process. 

Framework and Research Design 
From the dynamic capability's perspective, knowledge through business analytics capability 
merges to combustible effect at the realized absorptive capacity dimension and, thereby changes, 
as Cohen and Levinthal (1990) and Zahra and George (2002) mention, the trajectory of knowledge 
into new ways. Hence, the absorptive capacity framework shed light on contextual factors, 
external and internal to an organization (Ajeeli, 2018; Djerdjouri, 2020; Coleman et al., 2016; 
Turulja & Bajgorić, 2018). The framework captured the contextual factor of realized absorptive 
capacity's role in seeing real results from data. In other words, if SMEs instituted or empowered 
data transformation through realized absorptive capacity— transformation and exploitation, it 
could indirectly affect performance in remarkable ways (See Figure 6). 

The study advanced small business research by conceptually identifying and empirically 
examining tangible and non-tangible aspects of SMEs that function as precursors of organizational 
capability. The researcher employed a quantitative non-experimental correlational survey design 
approach to gather numerical data to find patterns, trends, averages, make predictions, and test the 
causal relationships between the independent and dependent variables. The population was limited 
to a minimum sample size of eighty-five respondents of SMEs, classified by the NAIC, owners or 
core knowledge workers, personnel with specialized technological knowledge and owner-level 
management impact or authority, that were based in the United States (Creswell & Creswell, 2019; 
Jenkins & Quintana-Ascencio, 2020; Paltridge & Phakiti, 2018). 

Population 
SMEs comprise over sixty percent of businesses in the U.S. (Kobe & Schwin, 2018). The current 
study was limited to small-to-medium-sized enterprises (SMEs) as defined by and in accordance 
with NAICS, owners or core knowledge workers, personnel with specialized technological 
knowledge and owner-level management impact or authority, based in the United States. From a 
pool of owners or core knowledge workers, a representative sample was selected utilizing a 
random selection method. The population sample consisted of only data according to the following 
three factors in determining sample size: (a) margin of error, (b) effect size, and (c) statistical 
power. Of the 1048 questionnaires sent, 52 questionnaires were received from SMEs owner, 
owner-level management, or core knowledge workers. The percentage of industry sectors include 



 

 

45 Business Management & Research Applications: A Cross-Disciplinary Journal 

the most frequently observed Industry(s) as Non-traditional and Real Estate, each with an observed 
frequency of (20%). Other industries include energy (4.0%), food and beverage (8.0 %), healthcare 
(12.0%), government (4.0%), education (8.0%), media (8.0%), information technology (IT) 
systems (8.0%), and professional services (8.0). 

Methods of Analysis 
The underlying assumptions driving the research were data, evidence, and rational thought shaping 
knowledge, and evidence leading to the null hypothesis's acceptance or rejection. The study 
collected data using a published assessment and a validated tool that applied multilevel mediation 
to account for top-down (i.e., owner, individuals, etc.) organizational capability interactions 
(Ajeeli, 2018; Brannen, 2017; Jenkins & Quintana-Ascencio, 2020). A Pearson correlation 
analysis was conducted among Business Analytics (BA), Performance (PF), Potential Absorptive 
Capacity (PAC), and Realized Absorptive Capacity (RAC). Cohen's standard was used to evaluate 
the strength of the relationships, where coefficients between .10 and .29 represent a small effect 
size, coefficients between .30 and .49 represent a moderate effect size, and coefficients above .50 
indicate a large effect size (Cohen, 1988). The alternative method of bootstrapping was 
incorporated for the smaller sample size a statistical procedure that resamples through simulation 
(Peeters, 2016). Table 1 presents the results of the correlations. 

Table 1 

Pearson Correlation Results Among BA, PF, PAC, and RAC 

 

Note. n = 25. Holm corrections used to adjust p-values. 

The observations for BA had an average of 4.93 (SD = 1.36, SEM = 0.27, Min = 1.75, Max = 
7.00, Mdn = 5.14, Skewness = -0.81, Kurtosis = -0.08). The observations for PF had an average of 
4.28 (SD = 1.43, SEM = 0.29, Min = 1.00, Max = 6.00, Mdn = 4.60, Skewness = -0.70, Kurtosis = 
-0.55). The observations for PAC had an average of 4.72 (SD = 1.74, SEM = 0.35, Min = 1.00, 
Max = 7.00, Mdn = 5.30, Skewness = -0.98, Kurtosis = -0.32). The observations for RAC had an 
average of 4.70 (SD = 1.62, SEM = 0.32, Min = 1.00, Max = 7.00, Mdn = 5.00, Skewness = -0.90, 
Kurtosis = -0.10). 

Table 2 presents the observed correlations with the bootstrapped results for the standard error and 
the 98% confidence interval of each correlation. The observed correlations with the bootstrapped 
results for the standard error and the 98% confidence interval of each correlation include a 



 
46 January 2022 | Volume 1, Number 1 

correlation coefficient between PF and PAC of 0.57, indicating a large effect size. This correlation 
indicates that as PF increases, PAC tends to increase. A significant positive correlation was 
observed between PF and RAC (rp = 0.79, p < .001, 95% CI [0.57, 0.90]). The correlation 
coefficient between PF and RAC was 0.79, indicating a large effect size. This correlation indicates 
that as PF increases, RAC tends to increase. A significant positive correlation was observed 
between PAC and RAC (rp = 0.83, p < .001, 95% CI [0.65, 0.92]). The correlation coefficient 
between PAC and RAC was 0.83, indicating a large effect size. This correlation indicates that as 
PAC increases, RAC tends to increase. 

Table 2 

Observed Correlations with Bootstrapped Results for the Standard Error and the Confidence Interval 

 

Results and Interpretation 
The following results and interpretations of the study were based on 1048 questionnaires sent with 
52 questionnaires of owners, owner-level management, and core knowledge workers accepted. 
The variables for business analytic capability were data use, technology, and people (Vidgen et al., 
2017). The absorptive capacity variables used were potential and realized absorptive capacity 
based on Zahra and George's (2002) acquisition, assimilation, transformation, and exploitation 
(Ajeeli, 2018; Vidgen et al., 2019). The variables for performance were both financial and non-
financial measurements—market share, sales, profit, and human resource growth (Ajeeli, 2018; 
Wood et al., 2015). The following results determined whether the research questions and 
hypothesis were rejected or failed to reject the null hypothesis. 

For research question (RQ1), does potential absorptive capacity mediate the relationship between 
business analytics capability and SMEs performance in the United States, the null hypothesis 
(H1o) was not rejected. A Baron and Kenny mediation analysis was conducted to assess if PAC 
mediated the relationship between BA and PF (Baron & Kenny, 1986). Four steps and three 
regressions were conducted. The results indicated potential absorptive capacity did not mediate the 
relationship between business analytics capability and SMEs performance in the United States. 
Based on an alpha of 0.05, the first regression with BA predicting PF results was significant, F(1, 
23) = 30.21, p < .001 and showing that BA was a significant predictor of PF, B = 0.79, satisfied 
the first criterion. 



 

 

47 Business Management & Research Applications: A Cross-Disciplinary Journal 

Table 3 

Unstandardized Loadings (Standard Errors), Standardized Loadings, and Significance Levels for Each Pa-
rameter in the path analysis Model (N = 25) 

 

Note. χ2 could not be calculated; -- indicates the test was not conducted as the observed variance/covari-
ance values were used. 

The second regression of BA predicting PAC was significant, F(1, 23) = 37.57, p < .001, showing 
BA was a significant predictor of PAC, B = 1.01, which satisfied the second criterion for 
mediation. The third regression with BA and PAC predicting PF was not a significant predictor of 
PF when BA was included in the model, B = -0.06, did not satisfy the third criterion for mediation. 
The fourth result showed that BA was a significant predictor of PF when PAC was included in the 
model, B = 0.85, which did not satisfy the fourth criterion for mediation. Since item 3 and item 4 
were not met, mediation was not supported. Hence, the null hypothesis was not rejected. 

Table 4 

Unstandardized Loadings (Standard Errors), Standardized Loadings, and Significance Levels for Each Pa-
rameter in the path analysis Model (N = 25) 

 



 
48 January 2022 | Volume 1, Number 1 

Note. χ2 could not be calculated; -- indicates the test was not conducted as the observed variance/covariance 
values were used. 

For research question (RQ2), does realized absorptive capacity mediate the relationship between 
business analytics capability and SMEs performance in the United States, the null hypothesis 
(H2o) was rejected. A Baron and Kenny mediation analysis was conducted to assess if RAC 
mediated the relationship between BA and PF (Baron & Kenny, 1986). Three regressions were 
conducted. The results indicate realized absorptive capacity does mediate the relationship between 
business analytics capability and SMEs performance in the United States. Based on an alpha of 
0.05, the first regression with BA predicting PF was significant, F(1, 23) = 30.21, p < .001 
showing that BA was a significant predictor of PF, B = 0.79, which satisfied the first criterion for 
mediation. 

The second regression with BA predicting RAC with the regression of RAC on BA as significant, 
F(1, 23) = 65.37, p < .001 and results showed that BA was a significant predictor of RAC, B = 
1.03, which satisfied the second criterion for mediation. The third regression with BA and RAC 
predicting PF with results showing that RAC was a significant predictor of PF when BA was 
included in the model, B = 0.47, which satisfied the third criterion for mediation. The fourth test 
showed that BA was not a significant predictor of PF when RAC was included in the model, B = 
0.31, which satisfied the fourth criterion for mediation was satisfied. All four criteria were 
satisfied indicating complete mediation was supported. Hence, the null hypothesis was rejected. 

Summary of hypotheses  
No.  Hypothesis Status 
1 H1 Potential absorptive capacity does not mediate the relationship between 

business analytics capability and SME performance in the United States  
Accepted 

2 H1a Potential absorptive capacity does mediate the relationship between busi-
ness analytics capability and SME performance in the United States.   

Rejected  

3 H2 Realized absorptive capacity does not mediate the relationship between 
business analytics capability and SME performance in the United States .  

Rejected 

4 H2a Realized absorptive capacity does mediate the relationship between busi-
ness analytics capability and SME performance in the United States.  

Accepted 

 

Discussion of Initial Research Findings 
Realized absorptive capacity was accepted as a SME’s ability to transform newly acquired 
knowledge and exploit it for commercial ends through knowledge transformation and knowledge 
exploitation (Zahra and George, 2002). The null hypothesis (H2o) was realized absorptive capacity 
does not mediate the relationship between business analytics capability and SME performance in 
the United States. The alternative hypothesis (H2a) was realized absorptive capacity does mediate 
the relationship between business analytics capability and SME performance in the United States. 
The results showed realized absorptive capacity does mediate the relationship between business 



 

 

49 Business Management & Research Applications: A Cross-Disciplinary Journal 

analytics capability and SME performance in the United States. Theoretically, the study has 
significant implications for SMEs in strategic management, knowledge management, 
entrepreneurship, or information technology during the COVID-19 pandemic. In line with recent 
theoretical extensions of dynamic capabilities, business intelligence, and knowledge management, 
indeed, the study’s findings point to SMEs better able to align, modify and reconfigure in a 
dynamic and volatile business environment by using data effectively through business analytics 
and realized absorptive capacity (Ajeeli, 2018; Jiménez-Barrionuevo et al., 2019; Solesvik, 2018). 
The study incorporated three theories: Duan et al., 2020; Jiménez-Barrionuevo et al., 2019; Vidgen 
et al., 2017; Solesvik, 2018; Yang & Tsai, 2019. 

First, the primary theory was the absorptive capacity theoretical framework, which focused on 
SMEs’ mechanics for capturing and exploiting knowledge. Secondarily and thirdly, it incorporates 
the principles of the resource-based view and dynamic capability within the structural capital 
(S.C.) dimension of intellectual capital, which includes business analytics capability (Ajeeli, 2018; 
Daspit et al., 2016; Djerdjouri, 2020; Jiménez-Barrionuevo et al., 2019; Sakhdari, 2016). Business 
analytics capability from the dynamic capability perspective speaks directly to what SMEs need to 
weather the current challenges of the pandemic, utilizing technology such as business analytics for 
processing a vast amount of digital information in the twenty-first century to adjust dynamically. 

Furthermore, the theory postulates the significance of absorbing new knowledge—transforming 
and exploiting knowledge helps organizations create value. The inference suggests value creation 
is restricted when absorptive capacity, especially realized absorptive capacity, is absent or low 
(Božič & Dimovski, 2019; Jiménez-Barrionuevo et al., 2019; Sakhdari, 2016). Herein lies a 
potential implication regarding the study’s findings: utilizing the framework and mediation 
analysis helped discover the nexus between realized absorptive capacity and transforming and 
exploiting knowledge for improving SMEs’ performance through business analytics capability. 

Figure 1 

Venn Diagram of Absorptive Capacity Dimensions 

 

Note: A Venn diagram shows the logical relation between sets. 



 
50 January 2022 | Volume 1, Number 1 

Observe in Figure 1, the merging of colors or components reaching zenith proportion toward even 
greater performance. Findings demonstrate knowledge through business analytics capability 
merges to combustible effect at the realized absorptive capacity dimension. Thereby, as Cohen and 
Levinthal (1990) and Zahra and George (2002) mention, the trajectory of knowledge changes into 
new ways. The framework captured the contextual factor of realized absorptive capacity’s role in 
seeing real results from data. If SMEs institute or empower data transformation through realized 
absorptive capacity, transformation, and exploitation, it could indirectly affect the SME’s 
performance in remarkable ways. Realized absorptive capacity entails the transformation and 
exploitation of data (Vidgen et al., 2017). It encompasses the intentionality of allowing knowledge 
to flow freely throughout the organization to achieve ongoing improved performance. 

In concluding the results, this study sought to fill the gap in the literature regarding the lack of data 
usage by SMEs as well as the low adoption of technology such as business analytics among SMEs 
compared to big businesses. Data alone does not necessarily provide knowledge; realistically, one 
does not acquire knowledge simply by holding it (Daspit et al., 2016). Added is having the 
uncanny ability to recognize the value of new information. There is potential misreading in Cohen 
and Levinthal’s (1990) original work positing a firm’s competitiveness is its ability to exploit new 
technological developments. They called it, absorptive capacity, the capability that not only 
enabled exploitation of new extramural knowledge but predicts more accurately the future. 

In Cohen and Levinthal’s (1990) deliberation is the power of cumulativeness, a seemingly 
neglected concept within the literature regarding absorptive capacity. The accumulation of 
building on existing knowledge affords more efficient accumulation subsequently—hence, 
exponential reoccurrence. Dynamic progression has the potential to bring about remarkable 
performance. Succinctly, erudition becomes more precise over time and benefits the erudite. The 
study supports Zahra and George’s (2002) reconceptualization about realized absorptive capacity 
(e.g., transformation and exploitation), by demonstrating how not embracing technology for 
analyzing data, not implementing current trends in organizational methods and visual analytics, 
nor ensuring organizational designs are aligned with the realized absorptive capacity, the study 
shows how SMEs are following a recipe for disaster. 

Examining Cohen and Levinthal’s (1994) statement in Fortune Favors the Prepared Firm 
regarding the then-premier economic rivalry closely: “…between Japan and the United States, 
that, in large part, Japan’s competitive advantage is due to American industry’s apparent inability 
to match Japan’s quick and effective use of external [knowledge]” (227). Herein lies solecism: 
focusing on just knowledge through business analytics and not including realized absorptive 
capacity will not materialize improved performance. Cohen and Levinthal (1994) misses the 
essence of Zahra and George (2002) for today: all the knowledge in the world will not bring 
transformative results. Realized absorptive capacity offers transformative and exploitative power. 
Hence, revamp Cohen and Levinthal (1994): Perceive, in a world [of so much data] and 
uncertainty, there is a benefit to [joining business analytics and realized absorptive capacity], it is 
called [the privilege] to exploit and perform better, which SMEs can. 
 



 

 

51 Business Management & Research Applications: A Cross-Disciplinary Journal 

COVID-19 Pandemic and Research Findings 
Discussion 
The COVID-19 pandemic brought a crisis challenge of Mt. Everest proportions to many small-to-
medium-sized enterprises (SMEs), and this had a large effect as SMEs are an important part of our 
worldwide economy supporting local economic growth, providing jobs, and meeting the needs of 
their customers (Chan et al., 2019). A study examining the effects of disruptive digital innovation 
(DDI), a discovery that increases breakthrough capabilities, explained how small businesses must 
respond and change or risk becoming obsolete. Chan et. al. (2019) shared how Kodak Company 
failed to keep up with the digital market and died away. Conversely, another example is the rental 
vacation home service Airbnb, using an online platform, has thrived during COVID-19 by 
adapting to new demands. 

While crisis and threats to business can be commonplace, COVID-19 has brought a much higher 
level of crisis with a global impact and how long this could go on. Fasth, Elliot, and Styhre (2021) 
conducted a study of 1,000 SME business leaders in Sweden to understand their crisis 
management plans. These research findings indicated that many SMEs rarely had a crisis plan and 
were subject to large revenue losses. The research also suggested, in addition to having a general 
crisis plan, including analytic methods can help generate new ideas and solutions. 

Positive Examples of Absorptive Capacity During the 
Pandemic 
The COVID-19 pandemic has also brought some positive influence on SMEs and entrepreneur 
endeavors that utilized several factors to build their business including data analytics. Davidsson, 
Recker, and von Briel (2021) presented a study analyzing the positive effects of using virtual 
collaboration tools, new customer needs, data analytics, and leveraged technological advances that 
created booming businesses during the COVID-19 pandemic including the Peloton In-home 
fitness system to provide workouts from home and customizable meal-delivery services such as 
Hello Fresh. 

In providing further discussion regarding the impact of COVID-19, and in light of the findings of 
the presented research, SMEs can benefit from pursuing efforts to improve their realized 
absorptive capacity, thereby increasing their business analytics methods capabilities during 
COVID-19 and beyond with transforming results (Gudandovskaya & Liniņa, 2021). While many 
SMEs have struggled during the COVID-19 pandemic, a few have thrived. As SMEs continue to 
navigate their way through this time of crisis, hopefully, they will find new opportunities. The 
research study sought to fill the gap in the literature regarding the lack of data usage by SMEs as 
well as the low adoption of technology such as business analytics among SMEs compared to big 
businesses. Data alone does not necessarily provide knowledge; realistically, one does not acquire 
knowledge simply by holding it (Daspit et al., 2016). SME's who make more efforts towards 
digitalization and adopting of digital technologies can help them better respond to public crises 
and the current COVID-19 pandemic (Gru et al., 2020). 



 
52 January 2022 | Volume 1, Number 1 

These results of this study can confirm that knowledge alone may not assist SME survival in times 
of crisis. Transformative results, like SME's experienced when they exhibited absorptive capacity 
through using virtual-collaboration tools, data analytics, and leveraged technological advances to 
develop thriving businesses during the COVID-19 pandemic (Briel, 2021). Adapting Cohen and 
Levinthal's (1994) statement, “In a world [of so much data], uncertainty, and competition, there is 
a benefit to merging business analytics capability with realized absorptive capacity. It is called [the 
privilege] to exploit and perform better” SMEs who can align, modify and reconfigure in a 
dynamic and volatile business environment, by using data effectively through business analytics 
and increasing absorptive capacity, have greater potential to provide a competitive advantage and 
improved business performance. This is especially true when responding to the ongoing COVID-
19 pandemic. 

  



 

 

53 Business Management & Research Applications: A Cross-Disciplinary Journal 

References 
Adam, N. A., & Alarifi, G. (2021). Innovation practices for survival of small and medium 
enterprises (SMEs) in the COVID-19 times: The role of external support. Journal of Innovation & 
Entrepreneurship, 10(1), 1–22. https://doi.org/10.1186/s13731-021-00156-6  

Agler, R., & De Boeck, P. (2017). On the interpretation and use of mediation: Multiple 
perspectives on mediation analysis. Frontiers in Psychology, 8, 1-11. 
https://doi.org/10.3389/fpsyg.2017.01984  

Ajeeli, S. (2018). The mediating role of absorptive capacity on the relationship between 
intellectual capital and firm performance in high-tech SMEs, U.K. (Order No. 13875792) Doctoral 
dissertation, Bangor University. ProQuest Dissertations & Theses Global. 

Alessandra, C., Cláudio, R. G., & Roberto, L. R. (2016). Redefining the relationship between 
intellectual capital and innovation: The mediating role of absorptive capacity. Bar-Brazilian 
Administration Review, 13, 4. https://doi.org/10.1590/1807-7692bar2016150067  

Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social 
psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality 
and Social Psychology, 51, 1173-1182. https://doi.org/10.1037/0022-3514.51.6.1173  

Bentler, P. M., & Chou, C. P. (1987). Practical issues in structural modeling. Sociological Methods 
& Research, 16(1), 78-117. https://doi.org/10.1177/0049124187016001004    

Brannen, J. (2017). Mixing methods: Qualitative and quantitative research. Routledge. 
https://doi.org/10.4324/9781315248813  

Bayrak, T. (2015). A review of business analytics: A business enabler or another passing fad. 
Procedia - Social and Behavioral Sciences, 195, 230-239. 
https://doi.org/10.1016/j.sbspro.2015.06.354  

Božič, K., & Dimovski, V. (2019). Business intelligence and analytics for value creation: The role 
of absorptive capacity. International Journal of Information Management, 46, 93–103. 
https://doi:10.1016/j.ijinfomgt.2018.11.020  

Cenamor, J., Parida, V., & Wincent, J. (2019). How entrepreneurial SMEs compete through digital 
platforms: The roles of digital platform capability, network capability, and ambidexterity. Journal 
of Business Research, 100, 196-206. https://doi:10.1016/j.jbusres.2019.03.035  

Chan, C. M. L., Teoh, S. Y., Yeow, A., & Pan, G. (2019). Agility in responding to disruptive 
digital innovation: Case study of a SME. Information Systems Journal, 29(2), 436–455. 
https://doi.org/10.1111/isj.12215  

Cohen, W. M., & Levinthal, D. A. (1989). Innovation and learning: The two faces of R & D. The 
Economic Journal, 99(397), 569-596. https://doi.org/10.2307/2233763  

https://doi.org/10.1186/s13731-021-00156-6
https://doi.org/10.3389/fpsyg.2017.01984
https://doi.org/10.1590/1807-7692bar2016150067
https://doi.org/10.1037/0022-3514.51.6.1173
https://doi.org/10.1177/0049124187016001004
https://doi.org/10.4324/9781315248813
https://doi.org/10.1016/j.sbspro.2015.06.354
https://doi:10.1016/j.ijinfomgt.2018.11.020
https://doi:10.1016/j.jbusres.2019.03.035
https://doi.org/10.1111/isj.12215
https://doi.org/10.2307/2233763


 
54 January 2022 | Volume 1, Number 1 

Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and 
innovation. Administrative Science Quarterly, 35(1), 128-152. https://doi.org/10.2307/2393553  

Cohen, W. M., & Levinthal, D. A. (1994). Fortune favors the prepared firm. Management Science, 
40(2), 227-251. https://doi.org/10.1287/mnsc.40.2.227  

Coleman, S., Göb, R., Manco, G., Pievatolo, A., Tort-Martorell, X., & Reis, M. S. (2016). How 
can SMEs benefit from big data? Challenges and a path forward. Quality and Reliability 
Engineering International, 32(6), 2151-2164. https://doi.org/10.1002/qre.2008  

Coroban, L., & Gavrila, A. A. (2019). Exploring the relations between business intelligence and 
the learning organization. Review of International Comparative Management / Revista de 
Management Comparat International, 20(2), 198. Retrieved from http://www.rmci.ase.ro/  

Cupiał, M., Szeląg-Sikora, A., Sikora, J., Rorat, J., & Niemiec, M. (2018). Information technology 
tools in corporate knowledge management. Ekonomia i Prawo, 17(1), 5-15. 
https://doi:10.12775/EiP.2018.001  

Daspit, J. J., D'Souza, D. E., & Dicke, L. A. (2016). The value-creating role of firm capabilities: 
Mapping relationships among absorptive capacity, ordinary capabilities, and performance. Journal 
of Managerial Issues, 28, 9-29. Retrieved from 
https://www.pittstate.edu/business/journals/journal-of-managerial-issues.html  

Davidsson, P., Recker, J., & von Briel, F. (2021). COVID-19 as External Enabler of 
entrepreneurship practice and research. Business Research Quarterly, 24(3), 214–223. 
https://doi.org/10.1177/23409444211008902  

Djerdjouri, M. (2020). Data and business intelligence systems for competitive advantage: 
prospects, challenges, and real-world applications. Mercados y Negocios, 1(21), 5-18. Retrieved 
from http://www.revistascientificas.udg.mx/index.php/MYN/  

Diaz-Molina, I. (2019). The role of strategic and operational absorptive capacity in organizational 
ambidexterity (Order No. 13863072) Doctoral dissertation, Temple University. ProQuest 
Dissertations & Theses Global. 

Donthu, N., & Gustafsson, A. (2020). Effects of covid-19 on business and research. Journal of 
Business Research, 117, 284–289. https://doi.org/10.1016/j.jbusres.2020.06.008  

English, V. & Hoffmann, M. (2018). Business intelligence as a source of competitive advantage in 
SMEs: A systematic review. DBS Business Review, 2, 10-32. https://doi.org/10.22375/dbr.v2i0.23  

Emerson, R. (2015). Convenience sampling, random sampling, and snowball sampling: How does 
sampling affect the validity of research? Journal of Visual Impairment and Blindness, 109(2), 164-
168. https://doi.org/10.1177/0145482X1510900215  

https://doi.org/10.2307/2393553
https://doi.org/10.1287/mnsc.40.2.227
https://doi.org/10.1002/qre.2008
http://www.rmci.ase.ro/
https://doi:10.12775/EiP.2018.001
https://www.pittstate.edu/business/journals/journal-of-managerial-issues.html
https://doi.org/10.1177/23409444211008902
http://www.revistascientificas.udg.mx/index.php/MYN/
https://doi.org/10.1016/j.jbusres.2020.06.008
https://doi.org/10.22375/dbr.v2i0.23
https://doi.org/10.1177/0145482X1510900215


 

 

55 Business Management & Research Applications: A Cross-Disciplinary Journal 

Fasth, J., Elliot, V. & Styhre, A. (2021). Crisis management as practice in small- and medium-
sized enterprises during the first period of COVID-19. Journal of Contingencies and Crisis 
Management,10. https://doi.org/10.1111/1468-5973.12371  

Field, A. (2017). Discovering statistics using IBM SPSS statistics: North American edition. Sage 
Publications. 

Fillingim, W. A. (2018). Small business sustainability strategies (Order No. 10937083) Doctoral 
dissertation, Walden University. ProQuest Dissertations & Theses Global. 

Garg, T., & Khullar, S. (2020). Big data analytics: Applications, challenges & future directions. 
2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends 
and Future Directions) (ICRITO), Reliability, Infocom Technologies and Optimization (Trends 
and Future Directions) (ICRITO), 923–928. https://doi-
org.revproxy.brown.edu/10.1109/ICRITO48877.2020.9197797  

Ghasemaghaei, M. (2019). Does data analytics use improve firm decision-making quality? the role 
of knowledge sharing and data analytics competency. Decision Support Systems, 120, 14-24. 
https://doi:10.1016/j.dss.2019.03.004  

Gong, H., Hassink, R., Tan, J., & Huang, D. (2020). Regional resilience in times of a pandemic 
crisis: The case of COVID-19 in China. Journal of Economic and Human Geography, 111(3), 
497–512. https://doi.org/10.1111/tesg.12447  

Gudovskaya, V., & Liniņa, I. (2021). Implementation of Digital Technologies in a Crisis 
Management Model of Small Businesses during the COVID-19. Acta Prosperitatis, 12, 8–23. 
https://doi:10.37804/1691-6077-2021-12-8-23  

Guo, H., Yang, Z., Huang, R., & Guo, A. (2020). The digitalization and public crisis responses of 
small and medium enterprises: Implications from a COVID-19 survey. Frontiers of Business 
Research in China, 14(1), N.PAG. https://doi.org/10.1186/s11782-020-00087-1  

Islami, X., Mustafa, N. & Topuzovska Latkovikj, M. (2020). Linking porter's generic strategies to 
firm performance. Future Business Journal, 6(3), 1-15. https://doi.org/10.1186/s43093-020-0009-
1  

Jenkins, D. G., & Quintana-Ascencio, P. F. (2020). A solution to minimum sample size for 
regressions. PLoS ONE, 15(2), 1–15. https://doi-
org.revproxy.brown.edu/10.1371/journal.pone.0229345  

Jiménez-Barrionuevo, M. M., García-Morales, V. J., & Molina, L. M. (2011). Validation of an 
instrument to measure absorptive capacity. Technovation, 31(5-6), 190-202. 
https://doi.org/10.1016/j.technovation.2010.12.002  

Jiménez-Barrionuevo, M., Molina, L. M., & García-Morales, V. J. (2019). The combined 
influence of absorptive capacity and corporate entrepreneurship on performance. Sustainability, 
11(11), 3034. https://doi:10.3390/su11113034  

https://doi.org/10.1111/1468-5973.12371
https://doi-org.revproxy.brown.edu/10.1109/ICRITO48877.2020.9197797
https://doi-org.revproxy.brown.edu/10.1109/ICRITO48877.2020.9197797
https://doi:10.1016/j.dss.2019.03.004
https://doi.org/10.1111/tesg.12447
https://doi:10.37804/1691-6077-2021-12-8-23
https://doi.org/10.1186/s11782-020-00087-1
https://doi.org/10.1186/s43093-020-0009-1
https://doi.org/10.1186/s43093-020-0009-1
https://doi-org.revproxy.brown.edu/10.1371/journal.pone.0229345
https://doi-org.revproxy.brown.edu/10.1371/journal.pone.0229345
https://doi.org/10.1016/j.technovation.2010.12.002
https://doi:10.3390/su11113034


 
56 January 2022 | Volume 1, Number 1 

Kobe, K. & Schwin, R. (2018). Small business GDP: 1998-2014 (Order No. 444) Small Business 
Research Summary. U.S. Small Business Administration Office of Advocacy. 

Lee, I., & Lee, K. (2015). The internet of things (IOT): Applications, investments, and challenges 
for enterprises. Business Horizons, 58(4), 431–440. https://doi.org/10.1016/j.bushor.2015.03.008  

Lu, Y., Wu, J., Peng, J., & Lu, L. (2020). The perceived impact of the COVID-19 epidemic: 
Evidence from a sample of 4807 SMEs in Sichuan province, China. Environmental Hazards, 
19(4), 323–340. https://doi.org/10.1080/17477891.2020.1763902  

Memon, M. A., Ting, H., Cheah, J.-H., Thurasamy, R., Chuah, F., & Cham, T. H. (2020). Sample 
size for survey research: Review and recommendations. Journal of Applied Structural Equation 
Modeling, 4(2), 1-20. https://doi.org/10.47263/JASEM.4(2)01  

Mahmood, T., & Mubarik, M. S. (2020). Balancing innovation and exploitation in the fourth 
industrial revolution: Role of intellectual capital and technology absorptive capacity. 
Technological Forecasting and Social Change, 160, 1-9. 
https://doi.org/10.1016/j.techfore.2020.120248  

Omar, A. R. C., Ishak, S., & Jusoh, M. A. (2020). The impact of COVID-19 movement control 
order on SMEs’ businesses and survival strategies. Malaysian Journal of Society and Space, 16(2), 
139-150. https://doi.org/10.17576/geo-2020-1602-11  

Özdil, S. Ö., & Kutlu, Ö. (2019). Investigation of the mediator variable effect using bk, sobel and 
bootstrap methods (mathematical literacy case). International Journal of Progressive Education, 
15(2), 30-43. https://doi.org/10.29329/ijpe.2019.189.3  

Paltridge, B., & Phakiti, A. (2018). Research methods in applied linguistics: A practical resource 
(pp.101-117). London: Bloomsbury Academic. 

Pape, T. (2016). Prioritizing data items for business analytics: Framework and application to 
human resources. European Journal of Operational Research, 252(2), 687-698. 
https://doi:10.1016/j.ejor.2016.01.052  

Peeters, M. J. (2016). Practical significance: Moving beyond statistical significance. Currents in 
Pharmacy Teaching and Learning, 8(1), 83-89. https://doi.org/10.1016/j.cptl.2015.09.001  

Qian, H., & Jung, H. (2017). Solving the knowledge filter puzzle: Absorptive capacity, 
entrepreneurship and regional development. Small Business Economics, 48(1), 99. 
https://doi:10.1007/s11187-016-9769-y  

Sakhdari, K. (2016). Absorptive capacity: Review and research agenda. Journal of Organizational 
Studies & Innovation, 3(1), 34-50. Retrieved from 
http://www.mbacademy.org.uk/index.php/home-josi  

https://doi.org/10.1016/j.bushor.2015.03.008
https://doi.org/10.1080/17477891.2020.1763902
https://doi.org/10.47263/JASEM.4(2)01
https://doi.org/10.1016/j.techfore.2020.120248
https://doi.org/10.17576/geo-2020-1602-11
https://doi.org/10.29329/ijpe.2019.189.3
https://doi:10.1016/j.ejor.2016.01.052
https://doi.org/10.1016/j.cptl.2015.09.001
https://doi:10.1007/s11187-016-9769-y
http://www.mbacademy.org.uk/index.php/home-josi


 

 

57 Business Management & Research Applications: A Cross-Disciplinary Journal 

Saputra, N., & Grace Herlina, M. (2021). Double-Sided Perspective of Business Resilience: 
Leading SME Rationally and Irrationally During COVID-19. Journal of Management & 
Marketing Review (JMMR), 6(2), 125–136. https://doi.org/10.35609/jmmr.2021.6.2(4)  

Solesvik, M. (2018). The rise and fall of the resource-based view: Paradigm shift in strategic 
management. Journal of New Economy, 19(4), 5–18. https://doi:10.29141/2073-1019-2018-19-4-1  

Tang, C. P., Huang, T. C. K., & Wang, S. T. (2018). The impact of internet of things 
implementation on firm performance. Telematics and Informatics, 35(7), 2038-2053. 
https://doi.org/10.1016/j.tele.2018.07.007  

Turulja, L., & Bajgorić, N. (2018). Knowing means existing: Organizational learning dimensions 
and knowledge management capability. Business Systems Research, 9(1), 1-18. 
https://doi:10.2478/bsrj-2018-0001  

U.S. Small Business Administration. (2020). Economic bulletin: Small business at a glance. U.S. 
Small Business Administration Office of Advocacy. 

Vasconcelos, A. C., Martins, J. T., Ellis, D., & Fontainha, E. (2019). Absorptive capacity: A 
process and structure approach. Journal of Information Science, 45(1), 68–83. 
https://doi.org/10.1177/0165551518775306  

Valentim, L., Lisboa, J., & Franco, M. (2016). Knowledge management practices and absorptive 
capacity in small and medium-sized enterprises: is there really a linkage? R&D Management, 
46(4), 711–725. https://doi-org.revproxy.brown.edu/10.1111/radm.12108  

Vidgen, R., Kirshner, S., & Tan, F. (2019). Business analytics: A management approach. London: 
Red Globe Press. 

Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from 
business analytics. European Journal of Operational Research, 261(2), 626-639. 
https://doi.org/10.1016/j.ejor.2017.02.023  

Wang, S., Yeoh, W., Richards, G., Wong, S. F., & Chang, Y. (2019). Harnessing business 
analytics value through organizational absorptive capacity. Information & Management, 56(7). 
https://doi.org/10.1016/j.im.2019.02.007  

Wójcik, P. (2015). Exploring links between dynamic capabilities perspective and resource-based 
view: A literature overview. International Journal of Management and Economics, 45(1), 83-107. 
https://doi.org/10.1515/ijme-2015-0017  

Wood, M. S., Bradley, S. W., & Artz, K. (2015). Roots, reasons, and resources: Situated optimism 
and firm growth in subsistence economies. Journal of Business Research, 68(1), 127-136. 
https://doi.org/10.1016/j.jbusres.2014.04.008  

https://doi.org/10.35609/jmmr.2021.6.2(4)
https://doi:10.29141/2073-1019-2018-19-4-1
https://doi.org/10.1016/j.tele.2018.07.007
https://doi:10.2478/bsrj-2018-0001
https://doi.org/10.1177/0165551518775306
https://doi-org.revproxy.brown.edu/10.1111/radm.12108
https://doi.org/10.1016/j.ejor.2017.02.023
https://doi.org/10.1016/j.im.2019.02.007
https://doi.org/10.1515/ijme-2015-0017
https://doi.org/10.1016/j.jbusres.2014.04.008


 
58 January 2022 | Volume 1, Number 1 

Yeoh, W., & Popovič, A. (2016). Extending the understanding of critical success factors for 
implementing business intelligence systems. Journal of the Association for Information Science & 
Technology, 67(1), 134–147. https://doi.org/10.1002/asi.23366  

Yang, S.-Y., & Tsai, K.-H. (2019). Lifting the veil on the link between absorptive capacity and 
innovation: The roles of cross-functional integration and customer orientation. Industrial 
Marketing Management, 82, 117–130. https://doi:10.1016/j.indmarman.2019.02.006  

Zahra, S. A., & George, G. (2002). Absorptive Capacity: A review, reconceptualization, and 
extension. Academy of Management Review, 27(2), 185-203. https://doi:10.2307/4134351  

Żogała-Siudem, B., & Jaroszewicz, S. (2021). Fast stepwise regression based on multidimensional 
indexes. Information Sciences, 549, 288-309. https://doi.org/10.1016/j.ins.2020.11.031  

  

https://doi.org/10.1002/asi.23366
https://doi:10.1016/j.indmarman.2019.02.006
https://doi:10.2307/4134351
https://doi.org/10.1016/j.ins.2020.11.031


 

 

59 Business Management & Research Applications: A Cross-Disciplinary Journal 

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	COVID-19 Pandemic and Research on the Mediating Role of Absorptive Capacity on the Relationship between Business Analytics Capability and Small-to-Medium-Sized Enterprise Performance
	Abstract
	Introduction
	Research
	Research Significance
	Research Objective

	Literature Review
	Addressing the Gap
	Hypothesis of the Research
	Theoretical Framework
	Business Analytics Defined
	The Cost of Low Absorptive Capacity
	Crucial Connections to Data
	Conceptual model

	Methodology Materials and Methods
	Framework and Research Design
	Population
	Methods of Analysis

	Results and Interpretation
	Summary of hypotheses
	Discussion of Initial Research Findings
	COVID-19 Pandemic and Research Findings Discussion
	Positive Examples of Absorptive Capacity During the Pandemic

	References


