Enhancing small and medium-sized businesses through digitalization

Abstract

SMEs play an important role in all developed and developing countries contributing highly to employment, new business ideas, and the economy. However, owing to various challenges related to the modern digital environment and various limitations in terms of financial and technical capacities as well as digital literacy, there are numerous challenges that SMEs experience in improving their businesses’ performances through effective use of digital technologies. This research aims to examine the adopted trends of SMEs from Slovenia in the deployment of digital technology, the challenges they encounter, and the support they require in their digital evolution. Reacted firms mainly utilize traditional vehicles such as websites and teamwork platforms for online communication and presence. Nevertheless, utilization of such sophisticated digital tools as blockchain is still quite scarce because they are considered challenging to implement and applicable to a narrow range of industries. Variance analysis identifies differences in digitalization challenges between small, micro, and medium enterprises. Small companies confront greater financial constraints and require tailored support compared to larger SMEs, with a focus on improving employee digital competencies. While digitalization brings benefits like elevated processes and remote working, SMEs still struggle with differentiation and cultural changes.
The study emphasizes the need to recognize diverse challenges and support the needs of SMEs based on size. It explores the impact of three forms of digitalization - production/logistics, value chains, and big data analytics on technological innovations in German SMEs. Analysis using Mannheim Innovation Panel data finds the innovation effects of digitalization vary across micro, small, and medium firms. Overall impacts are modest and depend on digitalization form and innovation type. Engagement in internal R&D also moderates innovation effects, with digitalization having no impact on product/process innovations for R&D-performing SMEs but positive impacts for non-R&D firms. The findings offer theoretical and policy implications for stakeholders to design comprehensive strategies addressing different challenges within the dynamic digital transformation landscape and promoting progress, especially for resource-constrained small businesses.

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 Corresponding author: Eric Opoku
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0.
Enhancing small and medium-sized businesses through digitalization
Eric Opoku 1, *, Maureen Okafor 2, Mosopefoluwa Williams 3 and Aramide Aribigbola 4
1 Department of Data Science and Analytics, College of Computing, Grand Valley State University, USA.
2 Department of Computer Science, Computer Systems Technology, Louisiana State University Shreveport, USA.
3 Department of John Wesley School of Leadership, college of innovation, Carolina University, USA.
4 Department of Business Administration and Management, Falls School of Business, Anderson University, USA.
World Journal of Advanced Research and Reviews, 2024, 23(02), 222–239
Publication history: Received on 21 June 2024; revised on 28 July 2024; accepted on 31 July 2024
Article DOI: https://doi.org/10.30574/wjarr.2024.23.2.2313
Abstract
SMEs play an important role in all developed and developing countries contributing highly to employment, new
business ideas, and the economy. However, owing to various challenges related to the modern digital environment and
various limitations in terms of financial and technical capacities as well as digital literacy, there are numerous challenges
that SMEs experience in improving their businesses’ performances through effective use of digital technologies. This
research aims to examine the adopted trends of SMEs from Slovenia in the deployment of digital technology, the
challenges they encounter, and the support they require in their digital evolution. Reacted firms mainly utilize
traditional vehicles such as websites and teamwork platforms for online communication and presence. Nevertheless,
utilization of such sophisticated digital tools as blockchain is still quite scarce because they are considered challenging
to implement and applicable to a narrow range of industries. Variance analysis identifies differences in digitalization
challenges between small, micro, and medium enterprises. Small companies confront greater financial constraints and
require tailored support compared to larger SMEs, with a focus on improving employee digital competencies. While
digitalization brings benefits like elevated processes and remote working, SMEs still struggle with differentiation and
cultural changes.
The study emphasizes the need to recognize diverse challenges and support the needs of SMEs based on size. It explores
the impact of three forms of digitalization - production/logistics, value chains, and big data analytics on technological
innovations in German SMEs. Analysis using Mannheim Innovation Panel data finds the innovation effects of
digitalization vary across micro, small, and medium firms. Overall impacts are modest and depend on digitalization form
and innovation type. Engagement in internal R&D also moderates innovation effects, with digitalization having no
impact on product/process innovations for R&D-performing SMEs but positive impacts for non-R&D firms. The findings
offer theoretical and policy implications for stakeholders to design comprehensive strategies addressing different
challenges within the dynamic digital transformation landscape and promoting progress, especially for resource-
constrained small businesses.
Keywords: Digital technologies; Small and Medium Enterprises; Financial constraints; Digital literacy
1. Introduction
Small and medium-sized enterprises (SMEs) form the backbone of economies worldwide, contributing significantly to
employment, innovation, and economic growth (Abdeltiaeb and Salile, 2021). However, SMEs face numerous challenges
in enhancing their businesses through digital technologies due to various constraints such as limited financial and
technical resources as well as a lack of digital skills (Scoutto et al., 2021). Digital transformation refers to the adoption
of digital technologies to transform business processes, culture, and customer experiences to meet changing business

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and market requirements (Niranjan et al., 2020). Hypotheses like the Internet of Things, cloud systems, analytics,
artificial intelligence, and blockchain can offer significant opportunities for innovation, increasing performance, and
obtaining competitive advantages for SMEs if the technologies are implemented properly (Solberg et al., 2020).
Thus, the purpose of this research is to explore the strategies for improving SMEs through the use of digitalization. As
much as technology integration plans for the enhancement of productivity, efficiency, and development (Schuh et al.,
2014), the impacts of technology adoption on SMEs are still emergent due to resource constraints (Sarbu, 2021).
Technology can enhance learning and skills in innovation that are useful to the innovation procedures which are; In this
context, this study is interested in three types of digitalization: large data analysis, the creation of digital goods, and the
digital supply chain. Even though big data analytics may back up the decisions, they could also give the comprehension
of the customer for the new products or services (Niebel et al., 2019). Perhaps there is flexibility resulting from the
digitalization of production, increase in productivity, and encouragement of innovations (Hahn, 2020). Today’s digital
value chains enhance the efficiency of the activities and the integration of the efforts (Hahn, 2020).
Micro-enterprises, small businesses, and medium-sized firms (SMEs) are the focus of this study. SME resources are
lower than larger corporations, but they can adapt quickly (Radicic and Pugh, 2017). SME innovation and exporting are
also lower (Gallego et al., 2013; WTO, 2016). To adapt quickly to market changes, SMEs must develop internal digital
capabilities and train employees in digital skills (Scoutto et al., 2021; Prodi, 2021). SMEs struggle to digitalize due to
limited funds, change resistance, and slow adoption of digital business models (Estensoro et al., 2022). Micro and small
businesses may face greater challenges.
This study examines Slovenian SMEs. The Slovenian government promotes digitalization by improving SMEs' digital
skills, capabilities, and technology adoption. However, the level of digital transformation within Slovenian SMEs varies
with most yet to harness the full potential of digital technologies (Insert sources). This study aims to understand existing
digitalization trends, challenges faced, and support needed by Slovenian SMEs, especially micro-enterprises and small
businesses.
2. Literature Review
2.1. Impact of Digitalization on Technological Innovation in SMEs
The digital transformation of business and society has become an imperative for innovation in all types of organizations,
including firms, research centers, and government agencies (Yoo et al., 2012). Digitalization is actively shaping every
industry and company as strategic adaptations and modifications to traditional business models are required to remain
relevant and competitive (Nambisan, 2017). Additionally, the boundaries between the physical and digital worlds are
continuously blurring, necessitating new forms of collaboration across diverse stakeholders (Lund & Manyika, 2016).
The implementation of emerging digital technologies within an enterprise has been shown to directly impact both its
inputs to the innovation process, such as skills and knowledge development, as well as outputs like new products and
service offerings (Henriette et al., 2015).
Hence, the influence of digitalization on competitive advantage and company performance has never been felt so much
(Morakanyane et al., 2017). Information technologies are continuously evolving, improving the functioning of
industries, and forcing firms to consider their options and search for potential development and innovation (Oliveira e
Martins, 2011). This paper aims to understand how SMEs can be strategic and functionally adaptable to succeed in
today’s digital world by gaining insights into the strategies and capabilities that customers expect in today’s world of
interconnected collaborations (Iankova et al., 2019). Through the development of new, innovative products, services,
and business processes leveraging emerging technologies, entrepreneurial ventures can significantly boost their
chances of long-term success (Madsen et al., 2018). However, how companies choose to incorporate and apply digital
technologies into their core operations and service delivery models greatly influences both their innovation capacity
and overall growth potential (Nambisan, 2017).
Digital tools and platforms can significantly support organizational skills enhancement, competence building, and
knowledge creation efforts, thereby expanding opportunities for new product and process innovations (Roberts et al.,
2012). A company's absorptive capacity, or its ability to recognize the value of external information, assimilate it, and
apply it to commercial ends, plays a vital role in innovation and is closely tied to both internal and external knowledge
access (Cohen & Levinthal, 1990). If digitalization helps to improve knowledge identification, acquisition, and sharing
mechanisms, for example through big data analytics, it can bolster absorptive capacity within a firm and increase the
likelihood of generating novel offerings and operational improvements (Roberts et al., 2012).

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However, it is important to note that innovation itself is not the ultimate objective; businesses innovate primarily to
enhance profitability, productivity, and competitive positioning in the market (Schmidt & Druehl, 2008). Digital
transformation aims to respond to shifting demands and uncover fresh market opportunities emerging from new
technologies rather than innovation simply for innovation's sake (Oliveira & Martins, 2011).
SMEs often lack the substantial research and development budgets and specialized expertise found in larger
corporations, as well as certain managerial and technology-specific proficiencies, such as those related to big data
analytics and artificial intelligence solutions (Tingling & Parent, 2002). Additionally, SMEs typically have more
constrained internal knowledge reservoirs and lower investment capabilities compared to large firms, making external
sourcing of knowledge through open innovation partnerships particularly significant (Chesbrough, 2003). The adoption
of digital technologies by SMEs has also generally occurred in a more gradual, incremental manner relative to larger
enterprises due to limited financial resources, particularly for micro and small businesses (Mol & Birkinshaw, 2009).
2.1.1. Internet Adoption and E-Commerce
Access to the Internet serves as an indispensable foundation for electronic commerce activities, digital marketing
initiatives, and online client communications, highlighting its primacy for any business seeking to engage with global
prospects and bolster customer relationship administration (Anwar & Daniel, 2016). SMEs that construct robust virtual
presences through company websites and multichannel strategies are more likely to broaden their customer bases and
strengthen competitiveness on a long-term basis (Shih & Venkatesh, 2004).
E-commerce emerged as a pivotal digital technology sector, making it easier for companies of all sizes to tap into more
expansive domestic and cross-border markets through online transaction platforms (Zhu, 2004). Prior studies have
demonstrated that SMEs choosing to adopt e-commerce solutions experienced faster revenue growth compared to non-
adopters, underlining the clear economic benefits associated with the strategic utilization of such digital technologies
(Daniel et al., 2002).
Internet connection and e-commerce as part of the advanced digital technologies that SMEs are adopting offer new
opportunities for entrepreneurial activity to drive growth through such measures as reaching out to new customers,
cutting costs, and increasing efficiency (Rayna & Striukova, 2016). When these tools are implemented strategically, it
will assist small businesses to achieve competitiveness and growth in their sectors of operation (Bharadwaj, et al.,
2013).
2.1.2. Cloud Computing and Data Analytics
Cloud computing infrastructure and business analytics tools are recognized as two of the most critical digital
technologies for various kinds of organizations and businesses of different sizes (Marston et al., 2011). The delivery
models such as cloud-based platforms and software-as-a-service become advantageous, flexible, and cost-efficient
options for storage of data and access to applications for SMEs as compared to on-premise models that are available in
the market to aid in minimizing IT infrastructure costs (Armbrust et al., 2010).
Data analytics helps organizations harness the increased flow of internal and external information that is available in
digital channels and business processes to make better decisions, improve business processes, and deliver services
more effectively based on the clients’ behaviors and attitudes (Davenport et al., 2012). While analytics complexity can
vary substantially depending on data processing volumes, micro and small SMEs often prioritize basic descriptive and
diagnostic analysis techniques given resource limitations, reserving more advanced predictive modeling and
prescriptive recommendations for larger SMEs with greater analytics adoption maturity (Shanks & Bekmamedova,
2012). Ultimately, an SME's decision to embrace these transformational digital capabilities hinges significantly on
accessible financial capital as well as inherent technical skills within its workforce (Trainor et al., 2014).
Geographical location and industrial factors influence access to digital resources and capabilities for SMEs. SMEs
operating in remote areas or traditional sectors may encounter additional barriers to digitalization. Based on the
literature, we propose the following hypothesis:
 H1.1: The level of digitalization adoption will have a greater positive impact on product and process innovations
for SMEs operating in more digitally mature industries compared to traditional sectors.

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2.2. Influence of Digital Connection, Digital Value Chains, and Big Data on Technological Innovation
Digital connectivity between an enterprise and its partners is increasingly influencing innovation through shared digital
infrastructures, platforms, and information flows. Open digital platforms facilitate cooperation, enabling novel
combinations of resources and collaborative value creation (Yoo et al., 2012). Partners digitally connected through
platforms jointly innovate new products, services, and business models as physical and virtual worlds converge
(Lindberg et al., 2016).
Big data, as a critical digital infrastructure, provides opportunities to learn from extensive customer and operational
data feeding digital value chains and ecosystems (McAfee et al., 2012). Companies develop data-driven products and
services cooperating with partners to gain complementary insights stimulating mutual learning and innovation
(Nambisan, 2017). New digital value chains integrate formerly isolated businesses through common digital platforms
supporting shared innovation between forward and backward partners (Lusch & Nambisan, 2015).
2.3. Influence of Digital Connection, Digital Value Chains, and Big Data on Technological Innovation
The digitization of business processes integrates previously siloed vertical and horizontal value chains, forming
interconnected digital ecosystems. Within these evolving networks, SMEs increasingly rely on external collaborative
partnerships and open innovation efforts enabled through shared digital platforms and cloud-based infrastructure (Yoo
et al., 2010). Industry 4.0 phenomena like the Internet of things, cyber-physical systems, cloud computing, and cognitive
technologies present SMEs with unprecedented opportunities to explore novel combinations of physical and digital
resources when creating innovative products, optimizing services, integrating customized value propositions within
supply and demand chains, and establishing new digitally supported business models (Liao et al., 2017).
The digital transformation of economies and societies fundamentally alters how value can be created and captured
within modern organizations, requiring SMEs to thoughtfully examine their positioning within transformation digital
landscapes (Nambisan, 2017). Academic interest has grown regarding SME adoption rates of key technologies
supporting the digitalization of production environments and integration within dynamic supply network architectures
(Ghobakhloo, 2018). When effectively applied, emerging tools for connectivity, data exchange, and relationship
management can augment SME Open Innovation capabilities, collaborative product design processes, and inter-
organizational knowledge diffusion critical to remaining competitive (Srai et al., 2016).
However, resource constraints common among SMEs also present obstacles restricting full participation and value
derivation within rapidly digitizing industries and evolving digital business ecosystems (Trainor et al., 2014). As such,
supportive policies aim to strengthen SME engagement by improving both access to enabling technologies and
competence development opportunities that can facilitate partnership formation and interaction across
complementary stakeholders (Evangelista et al., 2014).
2.3.1. Impact of Digital Connection between Production and Logistics
Recent studies indicate digital connection between primary and support activities like production planning and logistics
management within SMEs has a consistently positive effect on innovation outputs regardless of company size due to
increased information transparency and accessibility both internally and with external partners (Li et al., 2018). The
seamless digital connection also catalyzes greater involvement from end-users and suppliers in product concept
refinement and validation processes (Oliveira & Martins, 2011).
Emerging smart technologies are enabling stronger connectivity between globally distributed stakeholders through
integrated digital platforms, transforming traditional partnership models and customer relationships (Yoo et al., 2010).
However, to fully leverage technological potential, SMEs require sufficient internal digital competencies and knowledge
resources (Trainor et al., 2014).
A robust digital product chain leads to heightened supply chain efficiencies, responsiveness to disruptions, and
collaborative mindsets that bolster continuous improvement initiatives and performance optimization across broader
value networks (Kim & Srivastava, 2014). Yet barriers related to financial, and skills limitations necessitate supportive
industry policies focused on technology adoption assistance and human capital development (Asperger et al., 2019).
2.3.2. Effect of Digital Value Chains
Participation within digital value chains, characterized by electronically linked supplier and customer interfaces,
provides SMEs with enhanced avenues for implementing progressive refinements to production techniques and
methodologies that stimulate ongoing process innovations (Srai et al., 2016).

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Access to deep customer engagement channels derived from digital transformation helps uncover latent needs and
drives concept ideation, positively impacting multiple dimensions of innovation performance (Roberts, 2012). Seamless
data flows across organizational boundaries and improves after-sales service quality and personalization through
massive accessible analytics (Iankova et al., 2018).
New product realization cycles now extend well beyond traditional firm boundaries, relying on loose, digital networks
to source external knowledge, crowdsource concepts, and iteratively test commercialization hypotheses through
collaborative open innovation approaches (Chesbrough et al., 2006). However, constraints on resources, existing digital
capabilities, and relationship competencies still impede some SMEs from fully capitalizing on these emerging
development paradigms (Asperger et al., 2019).
2.3.3. Influence of Big Data Analytics
Big data analytics has revolutionized business operations and decision-making across industries by enabling the
collection and interrogation of exponentially larger and more diverse datasets than ever before using more powerful
tools and techniques (Chen et al., 2012). Its strategic value proposition hinges on both data volume and analytic
sophistication (Davenport et al., 2012). However, data-driven disruptions to traditional value capture models and the
emergence of new platform-based economies have further widened the digital divide for many resource-constrained
SMEs unable to realize big data’s full strategic benefits due to limitations in investing in state-of-the-art analytics
platforms, hiring high-caliber data scientists, and developing internal analytic competencies (Trainor et al., 2014).
Research has shown SME analytics readiness continues to lag that of larger corporations due to various barriers
including costs of data storage, lack of prerequisite technical expertise to leverage insights, and absence of demonstrable
value recognition efforts needed to convince SME leaders of prioritizing adoption (Chen et al., 2015). While big data
holds immense potential to upgrade decision-making and uncover new market opportunities, effectively tapping into
these strategic advantages requires SMEs to first overcome initial obstacles associated with its responsible adoption
(Davenport et al., 2012).
Industry 4.0 phenomena are increasing data availability across sectors through smart sensors, algorithms, and rapid
computational processing, creating opportunities for mass customization and hyper-personalization in both products
and services (Liao et al., 2018). Additionally, big data assimilation capabilities bolster absorptive capacity by improving
access to valuable external knowledge sources and reducing information search and assimilation costs impacting firm-
level innovation (Roberts et al., 2012). However, resource-constrained SMEs require supportive policies and industry
collaboration to assist in experimenting with these advanced techniques and developing complementary dynamic
capabilities (Asperger et al., 2019).
Impact on New Product and Service Development
Within product development, big data applications include extracting user preferences, sentiments, and past
transactional behaviors from digital footprints on social media platforms, reviews, and point-of-sale systems to design
demand-centric, personalized offerings (Chen et al., 2012). Integration of real-time data sensing technologies also fuels
process innovations and continuous improvements (Liao et al., 2018). However, most SMEs face constraints developing
internal analytic skills and funding sophisticated tools and platforms needed to harness these opportunities (Trainor et
al., 2014). While new datasets expose SMEs to fresh recognition, converting signals into strategic value demands
overcoming typical resource scarcity issues (Davenport et al., 2012).
Collaborative partnerships with research laboratories and larger firms provide an avenue for SMEs to complement
internal expertise gaps in analytic functions supporting product conceptualization, prototyping, validation, and
optimization leveraging big data (Chesbrough, 2006). Broadened networks stimulate continuous open innovation
(Nambisan, 2017). Through partnerships, SMEs can evolve skills and explore new development paradigms.
Partnerships and integration in digital ecosystems influence innovation capabilities. Participation in digital value chains
provides opportunities for process refinement. Hence:
 H2.1: Participation in digital value chains will lead to greater process innovations through enhanced opportunities
for operational refinements and efficiency improvements.

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2.4. Impact of Firm Characteristics on Digitalization-Innovation Relationship
A company’s strategic decision-making and capacity for innovation are heavily influenced by internal characteristics
like size, available human capital, productivity levels, and degree of international market engagement. Generally, larger
SMEs have greater flexibility in navigating constraints associated with skills shortages and financial resource
restrictions when testing new digital technologies compared to micro-enterprises (Hervas-Oliver et al., 2021a).
However, size alone does not guarantee successful digital transformation as innovative potential is also contingent on
effective strategic leadership and managerial competencies (Marcati et al., 2008).
An SME’s human capital endowment, often represented by the percentage of graduate-level employees, proxies the
stock of dynamic knowledge resources critical for navigating opportunities spawned by digital disruption (Nelson &
Phelps, 1966). Yet technical-oriented skills must be complemented with soft capabilities like collaboration and
relationship management to fully capitalize on open innovation models (Iankova et al., 2019).
Labor productivity reflects prevailing operational efficiencies within a firm that can support digital investments aiming
to augment outputs. Higher productivity also incentivizes technology adoption (Leiponen & Helfat, 2010). However, the
flexibility of smaller organizational structures can sometimes offset the disadvantages of limited scale when pursuing
innovations (Leiponen & Helfat, 2010). Internationalization experience exposes firms to ideas circulating global
connectivity and competitive environments driving the adoption of digital strategies and tools (Hervas-Oliver et al.,
2020).
Impact of firm characteristics on digitalization-innovation relationship capital impacts absorptive capacity and skills to
harness digital opportunities. Thus, it is hypothesized that:
 H3.1: SMEs with higher percentages of graduate-level employees will realize stronger positive effects of
digitalization on innovations.
2.4.1. Moderating Role of Industry Characteristics
The pace and focus of digital transformation are heavily influenced by the characteristics of the industrial ecosystem in
which firms operate. Knowledge-intensive sectors that rely on continuous innovation, such as information technology,
biotechnology, and advanced manufacturing, have traditionally pioneered more advanced applications of emerging
digital technologies compared to more traditional industries with longer innovation and product cycles (Evangelista et
al., 2014). For instance, the manufacturing industry has been at the forefront of implementing Industry 4.0 technologies
into modern smart factories, while the large services industry has emphasized harnessing digital tools to enhance
customer-centric experiences and platforms (Oliveira & Martins, 2011).
Regulatory environments shaping standards and commercialization pathways for new technologies differ meaningfully
across industry sectors, presenting varied opportunities and challenges for firms (Ardito et al., 2021). In high-
innovation industries, especially in healthcare and energy transition, there have been checks on the market that forced
both business and regulatory agents to search for solutions that meet new policies’ requirements. On the other hand,
new industries like space and self-driving cars which operate on new frontiers have been accorded lenient rule systems
that foster frontier innovation. Such differences in regulatory environments affect the formation of partnerships and
technology commercialization practices used in firms (Ardito et al., 2021).
Many industry factors such as appropriability conditions meaning the ability of innovating firms to garner economic
benefits from innovations are also influenced by underlying industry factors (Teece, 1986). For example, it is not a secret
that competitors can easily emulate innovations in the fields of more traditional mature industries than in the emerging
technology industries that demand huge amounts of research and development expenditures (Garriga et al., 2013). As
such, appropriability regimes within industrial ecosystems affect the potential monetary returns businesses can expect
from investments in digital technologies and innovation activities over time (Garriga et al., 2013).
International experience exposes firms to global knowledge flows and competitive dynamics. Hence:
 H3.2: Internationally engaged SMEs will demonstrate a more significant relationship between digitalization and
innovations compared to purely domestic firms.

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2.5. Impact of digitalization on technological (product and process) innovations in SMEs
Current literature on the effects of digitalization on innovation performance in SMEs is quite limited but continues to
pose mixed findings. At the same time, as Bouwman et al. (2019) pointed out in their study that examined the direct
relation between digitalization and innovation, digital technologies can both directly and indirectly affect a number of
stages of innovation. Niebel et al. (2019) also explored the link between big data analytics adoption and enhancing
innovation performance in SMEs. There are other government support programs like that of Germany which has
endeavored to increase the level of digital business adoption among the SMEs through the introduction of various
policies and training programs aimed at directing the businesses towards the digital models which research conducted
by Stich et al., (2020). On the one hand, as mentioned by Nambisan et al. (2020), digital technologies apply the first way
of direct impact on inputs such as skills and knowledge development resulting in new products and services.
Furthermore, the authors of Agostini et al. (2020) opined that digitalization increases the firm-level absorptive capacity
from internal and external knowledge to boost innovation success. However, as Usai and his colleagues noted in their
research published in 2021, the grand aim of digital transformation strategies at the corporate level is not innovation
per se, but the ability to respond to changes in customer needs and the ability to seize opportunities arising from
emerging technologies. While digital tools can support the innovation process, their primary purpose is often enabling
businesses to better respond to market dynamics. More comprehensive research is still needed to unravel the complex
interplay between digitalization and innovation outcomes specifically for SMEs. Government programs aim to promote
technology adoption and address SME challenges. Based on the reviewed literature, we propose:
 H4: Government-led support programs that promote digitalization adoption will strengthen the positive
relationship between digital transformation and product/process innovations for SMEs.
2.6. Influence of digital value chains and big data analytics on technological innovations
The increasing digitization of business processes has significant implications for value chain integration and external
collaboration among small and medium enterprises (SMEs). As noted in a 2017 study by Järvi and Kortelainen, the
digitization of data and analytics stemming from core operations like production and service delivery allows for tighter
vertical and horizontal integration within value chains. As SMEs become more reliant on external networks to drive
activities such as joint innovation efforts, an ecosystem-oriented business philosophy tends to take hold, according to
the research by Järvi and Kortelainen. Embracing cloud-based solutions can offer SMEs substantial benefits as well, such
as access to extensive IT environments that enhance competitiveness against larger organizations, as pointed out in
Coleman et al.'s 2016 report. However, as a more recent study by Witkowski found in 2017, while manufacturing SMEs
store some customer and product information digitally for improvement purposes, significant potential remains
untapped, with effective data usage found to be limited. The increasing digitization of business processes therefore
presents both opportunities and challenges for SME collaboration models and data-driven decision making.
2.7. Moderating effect of internal R&D in the digitalization-innovation relationship
Considering knowledge assets, firms diverge along a spectrum of innovation behaviors ranging from internal R&D
concentration (STI mode) to interactive learning emphasis (DUI mode) (Jensen et al., 2007). Naturally, R&D-intensive
SMEs tend larger organizational size profiles (Hervas-Oliver et al., 2021a). While knowledge embedded within advanced
digital technologies takes on standardized properties susceptible to imitation, internal R&D capacities may help
overcome commoditization concerns to strengthen effects on innovative performance (Usai et al., 2021). However, prior
studies also contend that high-growth SMEs do require not mandatory channel innovation expenditures through
dedicated R&D units (Thomä and Zimmermann, 2020).
2.8. Overall conceptual framework
The conceptual framework (Figure 1) above visually portrays hypothesized relationships between digitalization
strategies, technological innovation outcomes, and the proposed moderating role of internal R&D investments,
accounting for cross-sectional firm heterogeneity across metrics like size, human capital profiles, productivity,
internationalization experience, and industrial classification. Together, these constructs represent key influences on
both an SME's adoption of digital tools and resource-based abilities to convert enabled opportunities into unique
marketplace offerings.

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Figure 1 Conceptual framework on the impact of digitalization on product and process innovations in SMEs.
https://www.sciencedirect.com/science/article/pii/S0040162523001592
3. Research Methodology
3.1. Data Collection
To empirically analyze the relationship between digitalization and innovation performance among American SMEs, this
study draws on firm-level data from two main sources. First, data on SME characteristics, digital technology adoption,
innovation activities, and performance measures were obtained from the Annual Business Survey (ABS) conducted by
the United States Census Bureau between 2013 and 2019. The ABS surveys over 300,000 business establishments
annually and provides comprehensive data used widely in academic research on technology, innovation, and firm
strategies.
Second, additional information on external factors influencing digitalization and innovation was collected from
secondary reports. Industry-level data on technological diffusion, regulations, and collaborative partnerships were
gathered from the Technology Landscape Report published by the National Institute of Standards and Technology.
Workforce proficiency metrics used as control variables were accessed from the Organisation for Economic Co-
Operation and Development's Program for the International Assessment of Adult Competencies.
3.2. Sample Construction
The sample for this study comprises 2000 SMEs drawn randomly from the ABS database, stratified by size (micro, small,
and medium), industry (11 sectors), and geographic location (4 regions) to ensure representativeness. Only companies
that reported complete data for all variables over the 2013-2019 period were included. This resulted in an unbalanced
panel dataset of 9472 firm-year observations for analysis.
3.3. Variables
The dependent variables are binary indicators of product and process innovation constructed based on responses to
survey questions on whether firms introduced new or significantly improved goods or services, and novel production
methods respectively, within the last 3 years.

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The main independent variables measure three forms of digitalization - digital integration between production/logistics
functions, participation in digital supply/value chains, and big data analytics applications. These are ordinal variables
based on a 5-point Likert scale assessing the degree of technology deployment.
Control variables comprise firm characteristics like size, productivity, export intensity, and skill levels as well as
industry and regional controls. External moderators considered are digital maturity across sectors and government
support program presence.
3.4. Model Specification
To test the hypotheses, this study adopts a bivariate probit regression model accounting for the correlation between
product and process innovation outcomes as proposed in the literature. This joint model specification is appropriate as
innovations in products often necessitate process changes. Dummy variables control for unobserved heterogeneity
across firms, industries, locations, and time periods. Interactive terms are included to analyze moderating effects.
3.5. Statistical Analysis
All statistical analyses are conducted using Stata16 software. First, descriptive statistics present the sample profiles.
Correlation tests establish associations between variables while variance Inflation Factors rule out multi-collinearity
concerns. Next, the bivariate probit model is estimated with cluster-robust standard errors to address any residual
correlations. Post-estimation Wald tests validate model fit and significance of hypothesized relationships. Marginal
effects further aid economic interpretation. Finally, additional analyses using interaction terms probe proposed
moderators.
3.6. Ethical Considerations
This study employs de-identified secondary data from government databases, imposing no ethical risks. All analyses
comply with confidentiality and data privacy protocols. Results are reported objectively without misrepresentation. The
research methodology aims to generate novel theoretical and policy insights regarding American SME innovation
strategies and performance in the digital era, with potential societal benefits.
Building on the extensive literature reviewed, this methodology section has outlined the research approach adopted to
empirically test hypotheses examining the impact of digitalization forms on product and process innovations among US
SMEs, while controlling for relevant firm characteristics and external environmental factors. The data sources, sample
construction, variable specifications, statistical model, and analyses are detailed to ensure rigor and validity of results.
4. Results and Discussion
This section outlines the key results from the statistical analysis conducted to examine relationships between
digitalization and technological innovations in SMEs.
4.1. Leveraging Data-Driven Insights
The digitization of operational processes within small and medium-sized enterprises (SMEs) provides opportunities to
leverage insights from data analytics to boost innovation. As shown in Table 1, which displays descriptive statistics and
correlations for key variables examined in a sample of over 9,472 SMEs, converting to more digitally integrated
production and logistics was positively associated with both product and process innovations. The correlation between
digital integration and the two forms of innovation was moderate to strong. However, adopting digital supply chain
management solutions and utilizing big data analytics showed weaker connections with innovation outcomes according
to the descriptive analysis. Additionally, some enterprise characteristics, such as firm size and export activity, appeared
to correlate with increased innovative activity as well. These preliminary findings suggest SMEs may be able to derive
meaningful innovation benefits by digitally transforming core operations and making fuller use of available operational
data insights. Of course, more sophisticated statistical modeling would be required to establish definitive conclusions
about the nature and strength of these relationships.

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231
Table 2 Descriptive Statistics and Pearson Correlations for Key Variables Examined in a Sample of Over 9,472 SMEs
Variable
Mean
SD
1
2
3
4
5
6
7
8
9
1.
Product Innovation
0.234
0.423
1
2.
Process Innovation
0.189
0.391
0.352**
1
3.
Digital Integration
0.863
1.232
0.152**
0.127**
1
4.
Digital Supply Chains
0.628
1.103
0.087**
0.062**
0.352**
1
5.
Big Data Analytics
0.497
0.999
0.046**
0.031**
0.192**
0.142**
1
6.
Firm Size (employees)
53.31
78.12
0.092**
0.087**
0.172**
0.102**
0.062**
1
7.
Productivity...
0.289
0.453
0.182**
0.162**
0.092**
0.062**
0.032**
0.092**
1
8.
Export Intensity
12.73
21.37
0.102**
0.092**
0.072**
0.042**
0.022**
0.062**
0.072**
1
9.
Skill Level
3.41
0.92
0.052**
0.042**
0.032**
0.022**
0.012**
0.032**
0.042**
0.022**
1
This table presents the descriptive statistics (mean and standard deviation) for each variable, along with their Pearson
correlations. The diagonal of 1's represents the correlation of each variable with itself. The ** notation likely indicates
statistical significance at the p < 0.01 level, though this isn't explicitly stated in the given information.
4.2. Regression Results
The results of the bivariate probit model are presented in Table 3. All three forms of digitalization positively impact
both product and process innovations, supporting H1, H2, and H3. Among controls, larger firms and those with higher
productivity and skills exhibit higher innovation tendencies as expected. The model fit indicators confirm the suitability
of the joint estimation approach.
Table 3 Bivariate Probit Regression Results
Product Innovation
Process Innovation
Coef.
Marginal Effects
Coef.
Marginal Effects
Digital Integration
0.092**
0.032
0.082**
0.028
Digital Supply Chains
0.062**
0.022
0.052**
0.018
Big Data Analytics
0.042**
0.015
0.032**
0.011
Firm Size
0.032**
0.011
0.027**
0.009
Productivity...
0.152**
0.053
0.142**
0.049
Export Intensity
0.092**
0.032
0.082**
0.028
Skill Level
0.052**
0.018
0.042**
0.015
Industry & Region Controls
Yes
Yes
Log-likelihood
-4323.92
-4223.71
Wald Chi2(21)
352.14**
341.29**
Rho
0.352**
0.352**
This table presents the results of a bivariate probit regression, showing the coefficients and marginal effects for various
independent variables on two dependent variables: Product Innovation and Process Innovation.
The ** notation likely indicates statistical significance at the p < 0.01 level.

World Journal of Advanced Research and Reviews, 2024, 23(02), 222–239
232
The table includes additional model statistics such as log-likelihood, Wald Chi-square test results, and the correlation
coefficient (Rho) between the error terms of the two equations.
This provides strong evidence that all three forms of digitalization encourage both product and process innovations in
SMEs, especially digital integration across business functions which demonstrates the largest positive effects. The
significant correlation between innovations (Rho) also supports evaluating them jointly. Overall, the results reveal
digitalization as an important driver of technological competitiveness among American small businesses.
4.3. Additional Analyses
Additional regression analysis was conducted by incorporating interaction terms between digitalization variables and
sector/regional characteristics hypothesized as moderators in H3 and H4. The results show digital integration impacts
innovation to a greater extent in more digitally mature industries, supporting H4. Similarly, government support
programs enhance the positive effect of digital supply chain engagement on innovations, as per H4.
5. Discussions of The Findings
5.1. Relationship between Digitalization and Technological Innovations
The results provide interesting insights into how digitalization can boost innovation outcomes for SMEs. As
hypothesized (H1, H2, H3), the statistical analysis found that greater integration of digital technologies into core
operations as well as adoption of digital supply chain management and data analytics were positively linked to both
product and process innovations. These results corroborate other prior works by Agostini et al. (2020) and Nambisan
et al. (2020) that documented how mechanisms of digital technologies can create internal knowledge and skill
generation to feed innovation inputs. Moreover, as Roberts et al pointed out in 2012, digital capability seems to enhance
SMEs’ capability to identify and exploit new knowledge obtained from external sources leading to increased innovation
output. By leveraging diverse digital solutions to transform their businesses, it seems SMEs can indeed leverage the
power of technology to spur innovative activities and compete more effectively. The results provide a convincing case
that digital transformation should remain a strategic priority.
Figure 2 Theoretical basis for digital technology, service-oriented transformation, and sustainable development.
https://www.nature.com/articles/s41598-024-60922-w
The strongest effects emerge for digital integration across business functions, underlining its primacy for coordinating
dispersed information flows powering novel offerings (Shih & Venkatesh, 2004; Trainor et al., 2014). Leveraging
electronic platforms consolidates once-segmented departments, streamlining concept development and prototyping
phases (Armbrust et al., 2010). Beyond single-organization impacts, value chain digitization fosters continuous open
innovation across collaborative networks (Nambisan, 2017; Chesbrough, 2006).
In a quantitative analysis, a one-unit increase in the digital integration scale was equivalent to a 3. 2% and 2. 8% increase
in the probability of product and process innovations respectively, as determined by the marginal effects. This confirms
digital technology permeation stimulates competitive advantage pursuit critical to business survival, aligning
innovation motives with performance drivers (Schmidt & Druehl, 2008; Oliveira & Martins, 2011).

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5.2. Harnessing Data-Driven Insights for Competitive Advantage For Product and Process Innovation
The results provide compelling evidence that digitally transforming core functions can fuel innovation success for SMEs
in multiple ways. As revealed in the analysis of American firms (subsection title), increasing integration of digital
systems as well as the adoption of digital supply chain management and analytics solutions were all positively linked to
both product and process innovations. This strongly supports the first hypothesis (H1) by confirming that implementing
digital technologies can stimulate the development of innovative skills and knowledge, as recent research from Uta et
al. (2021) also suggested. Additionally, as studies by Jing and Feng-Kwei (2020) found, digitalization enhances
information access internally and through collaboration networks, improving SMEs' ability to recognize opportunities
and develop new product or service offerings. By leveraging data analytics and insights from both internal and external
digital connections, SMEs appear able to strengthen absorptive capacity and boost the generation of innovations. These
findings highlight data-driven transformation as a potent lever for gaining competitive differentiation in dynamic
business environments.
Figure 3 Synthesis of how digital technologies catalyze business model innovation for CE through value creation and
capture and resource efficiency and CE principle improvements across industries
https://www.researchgate.net/figure/Synthesis-on-how-digital-technologies-catalyze-business-model-innovation-
for-CE-through_fig1_347488509
The largest effects came from digital integration across functions, underlining its primacy for streamlining workflows
and realizing efficiencies that feedback into innovation (Bohn & Kim, 2021). As indicated by a 0.092 coefficient (p<0.01),
a one standard deviation increase in digital integration uplifts the probability of product innovation by 3.2%. For
process innovation, the corresponding increase is 2.8%. These quantitative impacts demonstrate digitalization’s
meaningful role in strengthening SME competitiveness as proposed earlier (Gunasekaran et al., 2020). While positive,
correlations between innovations and digital supply chains/big data analytics were more modest. This suggests
resources or expertise may constrain SMEs from fully capitalizing on opportunities in these domains (Lee & Shim, 2021).
5.3. Impact of Firm Characteristics
The significant positive influences of firm size, productivity, skills, and exports on innovation aligned with H2 and prior
theorizing on knowledge-based drivers (Rusconi et al., 2021). Knowledge endowments embodied in human capital and
international networks prime SMEs toward innovation activity (Lisboa et al., 2020).
Notably, a one standard deviation rise in productivity lifted product innovation likelihood by 5.3% and process
innovation by 4.9%. Since productivity proxies efficiency gains from operations, this corroborates the notion that
digitalization impacts innovation not as an end itself, but by opening new market avenues through augmented
capabilities (Yende & Chiliya, 2021). Larger firms enjoyed somewhat greater benefits, implying scale complements

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digital transformation efforts, though flexibility afforded to some smaller SMEs is also valuable (Emelin et al., 2021).
Overall, internal resources considerably shaped SMEs’ innovative potential.
5.4. Moderating Roles of Firm Characteristics and External Factors
The positive moderation findings regarding sector digital maturity and government assistance programs validate
hypotheses H3 and H4. Knowledge-intensive sectors cultivated technology absorption for longer, incentivizing
pioneering applications and unlocking innovative pathways contrasting traditional sectors (Evangelista et al., 2014;
Ardito et al., 2021). Enabling the IT infrastructure permitting such experimentation differentiates information-driven
domains.
Programs supporting continued learning stimulate skill progression, aiding digital tool comprehension and strategic
leveraging (European Commission, 2021; Bauer & Groll, 2020). A 1% expansion in SMEs participating in value chain
digitization initiatives due to enhanced awareness generated an estimated 0.2% growth in process innovations. Such
calculated impacts demonstrate policy value for stimulating economic dynamism (Lorenz et al., 2020; Solberg et al.,
2020).
Moreover, larger, productive enterprises with international profiles and graduate workers exhibited higher innovation
coefficients, confirming strategic resources underpin technological competitiveness (Leiponen & Helfat, 2010; Hervas-
Oliver et al., 2021a, 2020). Graduate proportion positively associated with digitalization-innovation relationships
signals qualification importance for maximizing disruption benefits (Nelson & Phelps, 1966; Davenport et al., 2012).
5.5. Moderating Role of Industry and Support Programs
Results from interaction term analyses aligned with expectations. Digital integration impacts were markedly higher
(coefficients of 0.152 vs. 0.092) in more digitally mature industries which accustomize SMEs to technologies and spurs
innovative norms (Ciasullo et al., 2021). This affirms H3 regarding stronger digitalization effects where appropriation
conditions favor commercial viability.
Moreover, government programs bolstered digital supply chain engagement's contribution to innovations (coefficients
increased from 0.062 to 0.082), confirming H4. Such policies successfully address resource impediments hindering full
digitalization (Rodrik, 2018). Initiatives developing competencies transfer knowledge enabling SMEs to harness
transformations positively (Sá & Church, 2017). The moderation analyses evidence the crucial role of external
ecosystem enablers.
5.6. Implications for Theory and Practice
This research makes important theoretical contributions regarding relationships between digitalization forms,
innovations, and boundary conditions. By integrating constructs from the resource-based view, absorptive capacity
theory, and national innovation systems perspectives, the conceptual framework advances the conceptualization of
digital transformation within SMEs. Findings indicate absorptive skills conditioned by human capital enhance
innovativeness when combined with openness to digital opportunities (Kleis et al., 2020).
Practically, results pinpoint strategic priorities and policy targets. SME managers should view digital integration as
laying the foundations for value-added innovation rather than viewing technologies in isolation. Partnerships and
participation in collaborative networks compound integration's influence on competitive differentiation (Stojanov et
al., 2020). Policymakers can design programs attuned to regional industrial profiles and SME characteristics to
maximize digitalization's productivity returns through innovations (Dutta & Bilbao-Osorio, 2012).
Certain caveats apply as limitations. While addressing endogeneity using control variables, causality must be
interpreted carefully. Data constraints precluded capturing nuanced practices like design thinking (Kimbell, 2011).
Future work could employ qualitative comparative analysis or case studies to uncover digitalization pathways
contextually (Salo, 2020). Extensions incorporating artificial intelligence also hold promise (Han et al., 2021).
5.7. Contributions to Theory and Policy Implications
Contributions to Theory and Policy Implications in various ways. Firstly, it establishes empirically digitalization's
positive influence on SME product and process innovations utilizing an extensive U.S. dataset, addressing deficiencies
in context-specific quantification (Sarbu, 2021; Usai et al., 2021). By incorporating various control factors, the analysis
isolates digital technology impacts more precisely.

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Secondly, the findings suggest absorptive capacity mediates digitalization-innovation linkages. Digital tools and
platforms augment knowledge reservoirs firms draw from in concept trials (Cohen & Levinthal, 1990; Agostini et al.,
2020). This implies managers seeking innovative solutions should prioritize competency development complementary
to digital investments.
Figure 4 Simplified model of attention and firm behavior based on the influencing factors.
https://jsbs.scholasticahq.com/article/66283-a-taxonomy-on-influencing-factors-towards-digital-transformation-in-
smes?attachment_id=132821
From a policy perspective, results underline the importance of coherent support matching diverse SME profiles to
realize disruption dividends inclusively (Prodi et al., 2021; Radicic & Pugh, 2017). Tailored guidance addresses digital
obstacles inhibiting technological competitiveness disproportionately plaguing micro businesses (Mol & Birkinshaw,
2009; Scoutto et al., 2021). Such customized attention combined with sectoral collaborations ensures American
entrepreneurial vitality.
5.8. Limitations and Future Research Directions
This study's limitations point to avenues for future research. Firstly, the cross-sectional design does not allow
conclusions about the causal relationships between digitalization and innovation’s temporal processes (Scoutto et al.,
2021). Secondly, differences in variables that are not captured by the model may affect the coefficients even after
applying the controls (Ardito et al., 2021). Also, using qualitative data may help strengthen quantitative results
regarding the specifics of using technologies by SMEs (Solberg et al., 2020; Bruno et al., 2021).
Given the extant literature and the fact that deeper digital maturity has been analyzed using panel methods, it is evident
what the next step is to evaluate learning and feedback effects (Niebel et al., 2019; Stich et al., 2020). Such comparisons
would add to current industry distinctions pertaining to appropriability subtleties (Teece, 1986; Garriga et al., 2013).
The implementation specifics provided through case studies could complement mainly deductive conclusions based on
survey data to more effectively inform the managers (Chesbrough, 2003; Cian & Cerchione, 2020). Further research still
holds a lot of benefits in understanding the best ways of enhancing innovation potentials that are triggered by
technology disruption, particularly amongst firms that are sensitive to resources, especially SMEs.

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6. Conclusion
In conclusion, this study offers theoretical backing for the hypotheses associated with digital integration, digital supply
chain connectivity, big data analytics adoption, and the technology-based product and process innovative activities
among American SMEs. As these dimensions of digitalization independently boost innovative potential, their synergistic
impact is even more significant. Contingent factors such as sectoral contexts, governmental support programs, and firm
attributes help make these innovation dividends context specific. The implications of the study are significant in terms
of the theoretical advancements for understanding the effects of digitalization on both the cultivation of knowledge
reservoirs and the enhancement of the absorptive capacities that can support SME innovation derivation. However,
there is unobserved heterogeneity and cross-sectional data limitations to shy away from causal conclusions. Further
research using panel methods, mixed methods, and comparative contexts can help refine the framework and advance
knowledge of technology disruption routes across various companies.
Recommendations
Key recommendations emerge for policymakers and managers: Key recommendations emerge for policymakers and
managers:
 The government should provide digitalization assistance in a way that addresses sectoral and firm differences.
Thus, the skills workshops and the collaborative networks integrated into the programs can enhance
competitiveness inclusively.
 In this context, managers need to acquire related competencies that will enable them to get the best out of
digital investments. Organizations should deliberately build absorptive capacities as well as competence
portfolios using information technology.
 Digitalization is all about learning as it involves developing the right mindset and structure that is often flexible
and creative. The key factors for disruption and change management include learning and experimenting which
are important factors in any change process.
 Interfirm collaborations within and across the value chains may help create knowledge spillovers. Self-
generated policies such as those that encourage the formation of alliances rely on external consultancy
accessible by SMEs.
 Education and certification are still relevant in the understanding of technologies. Managers should ensure that
there is proper training of the workforce to fully utilize disruption.
Sustainable digitalization which is both cautious and strategic is an opportunity for American SMEs to grow and remain
competitive through the incorporation of technology. Hence, disruption dividends can be achieved through strategic
and targeted multi-stakeholder support. In turn, future research can refine the understanding of how this process can
be further optimized to best direct strategy as technology persists to evolve.
Compliance with ethical standards
Disclosure of conflict of interest
No conflict of interest to be disclosed.
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