The Adoption of Digital Technologies by Small and Medium-Sized Enterprises for Sustainability and Value Creation in Pakistan: The Application of a Two-Staged Hybrid SEM-ANN Approach

Abstract

Digital technologies have revolutionized the business field, offering significant opportunities for small and medium-sized enterprises (SMEs) to enhance sustainability and value creation. This study investigates the impact of digital technology adoption on economic and social value creation, as well as SME performance. Specifically, it examines how social media applications, big data analytics, IoT applications, blockchain applications, and AI-enabled applications influence economic and social value within SMEs. We employed a hybrid approach integrating Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) techniques using SmartPLs 4.0 Application; this research analyzes these relationships. For our analysis, data were collected from 305 SME managers operating in Upper Sindh, Pakistan, specifically from major cities like Sukkur, Larkana, Shikarpur, Jacobabad, and Khairpur. The findings reveal that social media applications, big data analytics, IoT applications, and blockchain applications significantly contribute to both economic and social value creation for SMEs. Conversely, AI-enabled applications show no significant impact on value creation. Importantly, economic and social value creation positively correlates with enhanced SME performance. This study enriches our understanding of how digital technologies influence SMEs in Pakistan, particularly in enhancing economic and social value creation. Through advanced methodologies and rigorous analysis, it bridges theory with practical applications in SMEs’ digital transformation.

1. Introduction

Small and medium-sized enterprises (SMEs) are pivotal to the global economy, comprising over ninety percent of businesses worldwide and playing essential roles in job creation, innovation, and economic growth [1]. Thus, SMEs are recognized as important agents of economic expansion and development since they generate jobs, spur innovation and new business ventures, and increase competition [1]. In Pakistan, SMEs significantly contribute to the economy, accounting [2,3] for over 30% of GDP, 25% of exports, and employing more than 80% of the workforce across various sectors [3,4], including retail, agriculture, manufacturing, wholesale, trade, and services [5,6,7,8]. As reported by The Tribune (2021), there are one million small and medium-sized enterprises (SMEs) functioning in the province of Sindh, which accounts for 30% of the country’s gross domestic product (GDP) [9]. Around 30% of small and medium-sized enterprises (SMEs) are currently operating in Upper Sindh [10]. The prosperity of SMEs is important for sustained economic development in both developed and emerging economies [11].

The rapid advancement of digital technologies has revolutionized business operations, providing SMEs with new opportunities to enhance sustainability and create economic and social value [10]. According to a study by Asgary, Ozdemir, and Özyürek (2020), small and medium-sized enterprises (SMEs) have had to adapt to the changing technological environment to remain competitive [12]. SMEs have adopted various strategies to stay ahead, including advanced digital technologies, automation, and innovation [13]. This adaptability has enabled SMEs to mitigate the impacts of global risks and remain resilient in the face of technological disruption [7].

Firms utilize digital technologies to optimize supply chains, modify products, expedite information processing, enhance expertise, and reduce energy consumption [14]. Hence, digital technologies not only influence profitability but also augment competitiveness across the society, the economy, and the environment [15]. Digitalization, which refers to the intricate combination of social and technical phenomena involved in adopting and utilizing digital technology, is gaining significant scholarly interest [16]. This is because it is fundamentally altering how companies manage crucial resources and develop their strategies [17]. Today, digital transformation involves integrating advanced technologies such as social media applications, big data analytics, the Internet of Things (IoT), blockchain, and AI into business processes [18,19,20]. These technologies empower SMEs to optimize supply chains, innovate products, accelerate information processing, enhance expertise, and reduce energy consumption, thereby improving profitability, competitiveness, and sustainability across social, economic, and environmental dimensions [13]. Embracing digital technologies is imperative for SMEs to navigate and thrive in today’s competitive business environment [21], despite challenges such as optimizing AI initiatives [21].

Today, social media is one the best sources for transformed communication and marketing strategies since the Internet’s 21st-century inception, fundamentally altering how businesses engage with audiences and promote products [22]. Traditional marketing channels like radio, television, and print ads have been largely supplanted by social media platforms offering cost-effective, direct customer engagement opportunities [21,22,23]. SMEs use social media to reach global audiences, build brand awareness, and engage directly with customers at minimal costs compared to traditional methods [24,25].

Moreover, Artificial Intelligence (AI) plays a pivotal role in enhancing SME performance by optimizing processes, increasing sales, reducing costs, and improving customer management [21]. Techniques such as deep learning, neural networks, machine learning, and fuzzy logic enable SMEs to develop adaptive solutions catering to diverse customer needs [26,27]. Despite challenges such as limited capital and regulatory compliance, AI applications offer SMEs opportunities to enhance efficiency and decision-making capabilities, crucial for maintaining competitiveness [28,29].

Big data analytics (BDA) empowers SMEs to analyze extensive datasets in real time, thereby providing insights for informed decision-making and market predictions [30,31]. BDA applications optimize production processes, reduce costs, and facilitate digital transformation within SMEs, enhancing operational efficiency and ensuring long-term sustainability [31]. Meanwhile, the Internet of Things (IoT) facilitates data-driven insights, reshaping SME operations and enhancing adaptability in dynamic business environments [32,33,34]. IoT applications enable SMEs to use data for actionable results crucial for long-term positioning and growth [33].

Furthermore, blockchain technology, initially associated with cryptocurrencies like Bitcoin, offers SMEs secure transaction methods and enhances supply chain operations [35,36]. Despite challenges such as technological expertise, blockchain adoption promises transformative benefits for SMEs seeking innovation and sustainable growth [37,38].

These digital technologies not only promote business sustainability but also facilitate the circular economy and sustainable development principles through enhanced operational efficiency and awareness of global issues like climate change [39]. The utilization of digital technology in business management practices is becoming increasingly prevalent among organizations as a means to effectively address diverse challenges encountered in the realm of innovation [37,38]. In a report published by McKinsey and Company (2019), it was found that Asian countries, including China, Bangladesh, Pakistan, India, and Malaysia, have experienced greater international usage and business opportunities compared to countries in Latin America or Europe [40]. This is due to digital innovation, digitization, and e-commerce marketing and trade. Pakistan has begun its digital endeavors, focusing on digitized Pakistan. Pakistan has implemented measures to promote digitality, which involve both the state and private sectors. These measures aim to enhance connectivity; improve digital infrastructure, finance literacy, and digital skills; and strengthen innovation, entrepreneurship, and small and medium-sized enterprises (SMEs) in order to achieve a digital and revisionist Pakistan [40].

Despite their potential benefits, understanding how specific digital technologies contribute to economic and social value creation for SMEs, particularly in emerging economies like Pakistan, remains underexplored. This scientific effort makes various contributions. This study examines the influence of several elements, including social, economic, and technological aspects, on the adoption of digital technology by small and medium-sized enterprises (SMEs) and their subsequent performance.

Research in this domain has predominantly utilized Structural Equation Modeling (SEM) to explore linear relationships between digital technology adoption and business outcomes. However, these approaches may not capture complex, non-linear interactions crucial for comprehensive analysis [41]. Addressing this gap, this study employs a hybrid approach integrating SEM with Artificial Neural Networks (ANNs) to explore both linear and non-linear relationships.

The primary objectives of this study are to investigate the impact of various digital technologies on the creation of economic and social value for SMEs and examine how this value creation influences overall SME performance. By addressing these objectives, the study aims to provide actionable insights for SME managers and policymakers to prioritize and support the adoption of value-creating digital technologies.

The research questions guiding this study include the following:

• RQ1: What is the impact of SMEs using various digital technologies on the creation of social and economic value?
• RQ2: How does the creation of social and economic value impact SME performance?

This study contributes theoretically by exploring how different digital technologies influence economic and social value creation for SMEs. By integrating SEM and ANN methodologies, it offers a nuanced perspective on both linear and non-linear relationships, enhancing empirical understanding in this area. The findings provide practical insights for SME managers, highlighting technologies to prioritize for investment, and for policymakers to develop supportive frameworks promoting sustainability and economic growth through digital technology adoption.

The subsequent sections of this study are structured as follows. Section 2 provides an overview of the existing literature on the adoption of digital technologies by SMEs for sustainability and value creation, which subsequently informs the formulation of the hypotheses. The research methodology is outlined in Section 3, while the findings are reported in Section 4. Section 5 serves as the final part of the essay, offering a comprehensive analysis and conclusions that encompass both theoretical and practical contributions, as well as the limitations of the research.

2. Theoretical Background

This study utilizes the Resource-Based View (RBV) [42] and Dynamic Capability View (DCV) [43] theories to examine how small and medium-sized enterprises (SMEs) that embrace digital technology enhance their performance by increasing their economic and social value [44]. The RBV hypothesis posits that the performance of a corporation is determined by its key resources, which can be classified as either tangible or intangible assets [42,45]. Resources possessing qualities of value, rarity, inimitability, and irreplaceability (VRIN) can confer a competitive advantage by generating value and enhancing the performance of a corporation [42,45]. The examination of how SMEs can effectively employ these resources to develop and sustain a competitive edge has become a focal point for scholars and practitioners [46,47]. SMEs exhibit variations in performance due to their unique sets of VRIN resources and capabilities, aligning with the principles of RBV theory [42].

The RBV framework suggests that digital technologies are a part of SMEs’ resource portfolios. However, on their own, they may not meet the VRIN criteria due to the relatively low barriers for other SMEs to obtain similar applications [42]. It is proposed that when SMEs concurrently employ various technologies, such as AI, big data analytics, IoT, and blockchain, their performance will surpass that of their counterparts in the same market [36]. This integration creates a more formidable and distinctive resource base, enhancing competitive advantage and overall performance [31,35,36].

The DCV theory complements the RBV theory by focusing on how firms adapt to rapidly changing environments. Dynamic capabilities refer to an organization’s capacity to sense, seize, and transform opportunities and external resources to achieve sustained competitiveness [43]. These capabilities are embedded in organizational processes, enabling firms to reconfigure their resources in response to environmental changes [48,49]. For SMEs, this means recognizing rapid market evolution and evolving customer demands and strategically utilizing digital technologies to enhance their internal and external capabilities [50].

The concept of dynamic capabilities includes the “microfoundations” of sensing, shaping/seizing, and transforming, which are critical for a firm’s competitive behavior [51]. Sensing involves identifying opportunities and threats in the business environment while shaping/seizing refers to the mobilization of resources to capture these opportunities. Transforming is about continuous renewal to maintain competitiveness [52]. These microfoundations are particularly relevant for SMEs in the context of public procurement, where firms must not only compete for contracts but also demonstrate the ability to deliver public value improvements [53].

The literature highlights the importance of SMEs’ ability to dynamically adapt their strategies to use digital technologies for value creation [54,55,56]. Technological orientation plays a pivotal role in transforming operational activities and aligning SMEs with evolving market demands, thus enhancing their value creation processes [57]. This strategic orientation enables SMEs to generate economic and social value, thereby contributing to their overall performance in a dynamic market environment [58].

The theoretical context of this study is supported by the RBV and DCV theories, emphasizing the importance of integrating and reconfiguring digital technologies to enhance SME performance. By leveraging these theories, this study provides a comprehensive framework for understanding how SMEs can achieve sustained competitive advantage and value creation in dynamic market environments. Each construct is explained below, respectively.

2.1. Social Media Applications (SMAs)

Social media applications (SMAs) play a pivotal role in enhancing SMEs’ business operations and brand-building efforts [38,59]. Kaplan and Haenlein (2010) define social media as “a collection of Internet-based applications that enable the exchange and creation of User Generated Content and are built on the technological and ideological foundations of Web 2.0” [60,61,62]. These platforms, such as Facebook, Instagram, and Twitter, promote collaboration, communication, and interaction, which are critical for SMEs to engage with their customers and the broader market [63,64]. The interactive nature of social media allows SMEs to conduct electronic commerce, leading to significant impacts on their economic and social value [65]. Thus, understanding the role of SMAs is crucial for SMEs aiming to enhance their market presence and stakeholder engagement [66,67].

SMAs provide SMEs with cost-effective tools for marketing, customer engagement, and brand promotion [66,67]. By leveraging social media, SMEs can increase their visibility, attract a wider audience, and foster customer loyalty, which directly contributes to economic value. Additionally, social media platforms enable SMEs to engage in social responsibility initiatives, enhance community relationships, and build a positive reputation, thereby increasing social value [24].

2.2. AI-Enabled Applications (AEAs)

AI-enabled applications (AEAs) are designed to significantly enhance SME performance by optimizing resource allocation, improving customer interactions, and providing valuable data-driven insights [39]. AI technologies such as machine learning, natural language processing, and predictive analytics enable SMEs to boost sales, reduce expenses, and develop sustainable products and services [68]. According to the Resource-Based View (RBV) theory, AI represents a technological advancement with complex task-solving capabilities akin to humans, impacting both economic and social value creation in organizations [69].

The integration of AI in business operations allows SMEs to achieve higher efficiency and effectiveness. AI-driven tools streamline processes, reduce operational costs, and enhance decision-making, which collectively drive economic value [70]. Furthermore, AI applications can support sustainable practices and social initiatives, improving the quality of life for communities and contributing to social value.

2.3. Big Data Analytics (BDA)

Big data analytics (BDA) is characterized by its variety, velocity, volume, and accessibility, transforming business operations by uncovering hidden patterns in data [71]. BDA enables SMEs to make informed decisions, enhance productivity, and foster innovation [72,73]. For SMEs, BDA provides real-time solutions to critical business challenges, enhancing performance through adaptability and responsiveness [74].

BDA empowers SMEs with data-driven insights that inform strategic decisions and operational improvements [75]. By leveraging big data, SMEs can optimize their marketing strategies, streamline supply chains, and innovate products, thereby driving economic value [75]. Additionally, BDA helps SMEs monitor and improve their environmental and social impact, promoting ethical business practices and contributing to social value.

2.4. IoT Applications (IoAs)

The Internet of Things (IoT) involves the interconnection of objects and data through the Internet, enabling real-time data transmission and analysis [76]. IoT technology is essential for monitoring assets, enhancing operational efficiencies, and promoting sustainability in manufacturing and logistics processes [77,78]. IoT applications facilitate continuous data collection and processing, allowing SMEs to make timely and informed decisions [79].

IoT applications enhance SMEs’ ability to monitor and optimize their operations, reducing costs and improving productivity, which drives economic value [80]. Moreover, IoT technologies support sustainable practices, such as energy efficiency and waste reduction, contributing to environmental conservation and social value [81].

2.5. Blockchain Applications (BCAs)

Blockchain technology is a distributed and decentralized ledger that ensures data immutability, cryptographic security, and full audibility [82]. Blockchain applications help SMEs address security and transparency concerns, reduce costs, and enhance productivity through smart contracts and tokenization [83,84]. By eliminating intermediaries and automating essential services, blockchain reduces transaction authentication costs and operational overheads [85].

Blockchain technology offers SMEs a secure and transparent way to conduct business, enhancing trust and reducing risks, which contributes to economic value [83,84]. Additionally, blockchain promotes ethical practices and transparency, fostering stakeholder confidence and supporting social initiatives, thereby enhancing social value [86,87].

2.6. Economic Value (ECV)

Economic value (ECV) refers to the value that an organization consistently seeks to obtain from its resources [88]. It is often quantified in currency units and represents the benefits generated by a product or service [89]. For SMEs, generating economic value involves maximizing profits, reducing costs, and improving efficiency. ECV is crucial for financial performance as it indicates the firm’s ability to create wealth and sustain growth [90].

ECV is essential for SMEs as it drives financial performance and sustainability [91]. By maximizing economic value, SMEs can invest in innovation, expand their market reach, and enhance their competitive advantage [92]. This contributes to long-term business success and resilience [93].

2.7. Social Value (SOV)

Social value (SOV) encompasses the multifaceted advantages that an organization can provide to its stakeholders, including economic, social, and environmental benefits that extend beyond mere profit generation [94]. SMEs that prioritize social value engage in activities that benefit local communities, promote environmental sustainability, and enhance social well-being [93].

SOV is significant for SMEs as it fosters community support, enhances reputation, and builds customer loyalty [64,86]. By creating social value, SMEs can differentiate themselves in the market, attract socially conscious consumers, and achieve sustainable growth. This holistic approach to value creation strengthens the overall performance and impact of SMEs [93].

2.8. SME Performance

SME performance is a comprehensive measure that includes financial success, operational efficiency, market competitiveness, and social impact. It reflects the organization’s ability to achieve its strategic objectives and sustain growth in a competitive environment [91]. Factors influencing SME performance include economic value, social value, innovation, customer satisfaction, and adaptability to market changes [93].

SME performance is important for evaluating the effectiveness of business strategies and the overall health of the organization [95]. High performance indicates successful management practices, strong market presence, and the ability to navigate challenges. By focusing on both economic and social value, SMEs can achieve balanced and sustainable performance, ensuring long-term success and stakeholder satisfaction [58].

3. Hypothesis Development

3.1. Social Media Applications (SMAs) and Economic Value and Social Value

Social media applications (SMAs) have revolutionized how businesses interact with their customers, thereby enhancing brand visibility, customer engagement, and market reach. SMEs utilize platforms to promote products and services, which can significantly increase sales and revenue. Research indicates that effective social media marketing strategies contribute to higher economic returns by improving customer acquisition and retention rates [38,58]. Social media marketing has become a cost-effective tool for SMEs to compete with larger firms, fostering economic growth and profitability [96,97].

In addition to economic benefits, social media applications enhance the social value of SMEs by fostering community engagement and corporate social responsibility (CSR) activities. Platforms like LinkedIn and Facebook enable SMEs to build and maintain relationships with stakeholders, including customers, suppliers, and local communities [97,98]. Social media facilitates transparent communication and feedback mechanisms, allowing SMEs to address social issues and contribute to community development [99]. By promoting socially responsible practices, SMEs can enhance their reputation and trust among stakeholders, leading to greater social value [100].

H1a. 

SMAs boost economic value for SMEs.

H1b. 

SMAs enhance social value for SMEs.

3.2. AI-Enabled Applications (AEAs) and Economic Value and Social Value

AI-enabled applications have revolutionized small and medium-sized enterprises (SMEs) by leveraging advanced computing power to implement neural network models, achieving high accuracy rates above 95% in production environments [8]. These applications excel in specific machine vision tasks within well-defined parameters, ensuring the stability and predictability crucial for industrial applications. The focus remains on enhancing production efficiency and meeting customer demands, driving economic value through optimized operations and quality assurance. Moreover, AI technologies enable SMEs to personalize customer interactions, analyze market trends, and improve decision-making processes, thereby enhancing social value by fostering stronger customer relationships and community engagement [9]. The integration of AI not only improves operational capabilities but also positions SMEs competitively in dynamic market landscapes, underscoring its pivotal role in digital transformation and value creation.

AI-enabled applications (AEAs) offer advanced analytics, automation, and personalization, which can significantly enhance the economic performance of SMEs [21]. AI technologies, such as machine learning and natural language processing, optimize business operations, reduce costs, and increase productivity [101,102]. For instance, AI-driven chatbots and recommendation systems improve customer service and sales efficiency, leading to higher revenues [103,104]. Studies have shown that SMEs adopting AI technologies experience substantial improvements in their economic outcomes, including profitability and market competitiveness [103,105].

AI-enabled applications also contribute to social value by improving the quality of life and well-being of communities. AI technologies can address social challenges such as healthcare accessibility, education, and environmental sustainability [106]. For SMEs, AI applications enhance social value by providing personalized services, promoting inclusivity, and supporting social initiatives [107]. By leveraging AI for social good, SMEs can build a positive societal impact, strengthening their social capital and stakeholder relationships [106].

H2a. 

AEAs increase economic value for SMEs.

H2b. 

AEAs boost social value for SMEs.

3.3. Big Data Analytics (BDA) and Economic Value and Social Value

Big data is a pivotal technology within Industry 4.0, encompassing the collection and analysis of vast datasets from various sources like production processes, customer interactions, and business operations [30,108]. This technology enhances production quality and efficiency by predicting machine failures through predictive maintenance and aligning production outputs with customer demands [109]. The concept of big data is defined by the three Vs—volume, velocity, and variety—which reflect its capability to handle massive amounts of data rapidly, including structured, semi-structured, and unstructured formats [110]. For small and medium-sized enterprises (SMEs), which often face challenges in adopting Industry 4.0 technologies due to resource constraints, big data offers significant opportunities [34,108]. It allows SMEs to leverage critical data effectively, thereby improving production processes and competitiveness within their respective industries [75].

Big data analytics (BDA) empowers SMEs with data-driven decision-making capabilities, leading to improved economic performance. BDA allows SMEs to analyze large volumes of data to identify market trends, customer preferences, and operational efficiencies [71,74]. Through the usage of big data, SMEs can optimize their marketing strategies, streamline supply chains, and enhance product development, resulting in increased sales and profitability [45]. The strategic use of BDA has been linked to significant economic gains for SMEs, including revenue growth and competitive advantage [74].

Beyond economic benefits, BDA enhances the social value of SMEs by promoting informed and ethical decision-making. BDA can be used to monitor and improve environmental sustainability practices, social responsibility initiatives, and community engagement [111]. For example, SMEs can use big data to track their carbon footprint and implement greener practices, contributing to environmental conservation [58]. Additionally, BDA helps SMEs address social issues by providing data-driven insights into societal needs and preferences, fostering a positive social impact [34].

H3a. 

BDA drives economic value for SMEs.

H3b. 

BDA improves social value for SMEs.

3.4. IoT Applications (IoAs) and Economic Value and Social Value

The Internet of Things (IoT) represents a cornerstone of Industry 4.0, enabling seamless connectivity and data exchange among devices across various sectors [112]. In the context of small and medium-sized enterprises (SMEs), the IoT facilitates real-time communication and decision-making capabilities, crucial for enhancing operational efficiency and responsiveness to customer demands [81]. By integrating the IoT with cloud computing solutions, SMEs can harness data from sensors and machines to optimize production processes and enable predictive maintenance, thereby driving economic value through cost savings and productivity enhancements [113]. Furthermore, the IoT empowers SMEs to offer personalized customer experiences and improve service delivery, contributing to enhanced social value by strengthening customer relationships and market competitiveness [114]. Despite its transformative potential, challenges such as data security and integration complexities need to be addressed to fully capitalize on the IoT’s benefits in SME environments.

Internet of Things (IoT) applications significantly enhance the economic value of SMEs by enabling real-time data collection and analysis, leading to improved operational efficiencies and cost savings [115,116]. IoT technologies allow SMEs to monitor and optimize their production processes, supply chains, and inventory management, resulting in increased productivity and reduced operational costs [116]. The adoption of IoT solutions has been associated with higher economic returns, as SMEs can make data-driven decisions and improve their market responsiveness [58].

For SMEs, IoT technologies facilitate better resource management, energy efficiency, and waste reduction, supporting sustainable development goals [113,116]. By adopting the IoT for social good, SMEs can enhance their social impact and contribute positively to community welfare [113].

H4a. 

IoAs elevate economic value for SMEs.

H4b. 

IoAs enhance social value for SMEs.

3.5. Blockchain Applications (BCAs) and Economic Value and Social Value

Blockchain applications (BCAs) offer decentralized and secure transaction mechanisms that enhance the economic value of SMEs. Blockchain technology ensures transparency, traceability, and security in business transactions, reducing fraud and operational costs [117,118].

Blockchain technology has emerged as a transformative force across various sectors, including small and medium-sized enterprises (SMEs), contributing to both economic and social value creation. Initially introduced with Blockchain 1.0, focusing on basic cryptocurrency transactions, its evolution through Blockchain 2.0 and beyond has expanded its applications to include property management and decentralized finance [118].

SMEs, which constitute a significant portion of national economies worldwide, face challenges such as limited access to banking services and inefficient processes. Blockchain technology offers solutions by enabling secure and transparent transactions without the need for intermediaries, thereby reducing costs and enhancing operational efficiency [82]. For instance, initiatives like the Blockchers project under the European Horizon 2020 program aim to integrate blockchain into traditional sectors, fostering innovation and competitiveness among SMEs across Europe [87].

Moreover, blockchain platforms like Ethereum and Hyperledger facilitate decentralized applications tailored to SME needs, such as supply chain management and trade finance [36]. These platforms streamline operations, improve transparency, and reduce bureaucracy, thereby empowering SMEs to compete more effectively in their respective industries [36,119].

Blockchain applications also promote social value by enhancing trust, transparency, and accountability in business operations [120]. Blockchain’s immutable ledger provides a reliable record of transactions, fostering stakeholder confidence and reducing corruption [121]. SMEs can use blockchain to implement fair trade practices, ensure product authenticity, and support social initiatives, enhancing their social responsibility and impact [114]. The adoption of blockchain technology contributes to building a more ethical and transparent business environment, benefiting society at large [122].

H5a. 

BCAs advance economic value for SMEs.

H5b. 

BCAs promote social value for SMEs.

3.6. Economic Value (ECV) and SME Performance

Economic value (ECV) refers to the monetary benefits that SMEs derive from their resources and activities. The primary goal of SMEs is to generate economic value by maximizing profits and minimizing costs [42]. ECV is a critical determinant of SME performance, as it directly impacts profitability, growth, and competitive advantage [6]. Research indicates that SMEs with higher economic value tend to perform better in terms of financial stability, market share, and overall success [123]. Therefore, enhancing ECV is essential for SMEs to achieve sustainable performance and growth.

H6. 

ECV contributes to SME performance.

3.7. Social Value (SOV) and SME Performance

Social value (SOV) refers to the broader societal benefits that SMEs provide through their operations, including economic, social, and environmental aspects [123,124]. SOV encompasses initiatives such as corporate social responsibility (CSR), community engagement, and sustainable practices, which contribute to the well-being of society [125].

The author of Ref. [94] argues that SMEs possess a unique capability to generate social value compared to larger enterprises. This is attributed to SMEs’ agility in introducing technological innovations, enhancing consumer choice, and promoting transparency and accountability in markets. Unlike large companies with centralized structures, SMEs are nimble and can efficiently improve product and service quality, thereby contributing significantly to social value creation alongside financial gains.

Research shows that SMEs that prioritize social value gain a competitive advantage by building stronger relationships with stakeholders, enhancing their reputation, and attracting loyal customers [113,114,123]. Therefore, integrating SOV into business strategies is crucial for SMEs to achieve long-term success and sustainability.

H7. 

SOV contributes to SME performance.

The conceptual model is depicted in Figure 1. It synthesizes the above relationship between these factors.

4. Research Methodology

4.1. Research Design

This study employs a quantitative research design to examine the impact of digital technologies on the economic and social value creation for SMEs in Pakistan [126]. A quantitative approach facilitates a detailed and analytical examination necessary for addressing the measures and internal coherence that underpin the adoption of digital technologies by SMEs [127]. By integrating Structural Equation Modeling (SEM) and Artificial Neural Networks (ANNs), the study aims to provide a comprehensive analysis of both linear and non-linear relationships [128].

4.2. Data Collection: Procedure and Sample

Data were collected from 305 SME managers operating in Upper Sindh, Pakistan, specifically from major cities like Sukkur, Larkana, Shikarpur, Jacobabad, and Khairpur. The respondents were either proprietors or managers of SMEs, given their comprehensive knowledge of their firms. A complete inventory of SMEs was compiled through visits to various Chambers of Commerce and leveraging social contacts and snowball sampling. Snowball sampling was particularly useful in overcoming challenges in locating suitable study participants [129].

The data collection process targeted 1100 SMEs across various sectors, including manufacturing, retailing, wholesaling, agriculture, livestock, poultry, and services. Out of these, 350 SMEs allowed in-person visits for data collection using a survey instrument. Data collection commenced in December 2023 and concluded in February 2024. The sample size exceeds the minimum recommended by Reinartz et al. (2009), who suggest a sample size of 100 for Structural Equation Modeling using partial least squares (PLS) [130]. A pilot study involving 30 SMEs was conducted to ensure the clarity and reliability of the survey instrument, resulting in a Cronbach’s alpha exceeding 0.7, indicating high reliability [131,132].

4.3. Measures

The questionnaire for this study was developed based on the existing literature, with modifications to suit the specific context of the current study. According to O’Gorman and MacIntosh (2015), the primary objective of a questionnaire is to communicate the study objectives systematically and motivate respondents to provide accurate responses [133].

A five-point Likert scale was used, where 1 represents “strongly disagree” and 5 represents “strongly agree”. This scale is commonly used in business management research [21,37,39,58,62,134,135]. The survey items were adapted from Vrontis et al. [58] and are detailed in Appendix A.

The survey was developed in English and translated into Sindhi and Urdu, considering that Sindhi is spoken by over 60% of the population in Sindh and Urdu by more than 18% [136]. The translation process involved subject and language experts to ensure semantic equivalence.

The content validity process included expert reviews, pilot testing, cognitive interviews, and translation verification. This rigorous process ensured that the questionnaire was culturally relevant, clear, and effective in capturing the constructs of interest, enabling high-quality data collection [137].

4.4. Statistical Analysis

The data analysis in this study was conducted using Structural Equation Modeling (SEM) to evaluate the proposed hypotheses [137]. SEM is a statistical technique used to examine complex relationships between variables [138,139,140,141]. A maximum likelihood approach within SEM was employed to assess both the structural and measurement models [88].

Confirmatory Factor Analysis (CFA) was used to assess convergent validity and the causal linkages between the revised items and variables within the measurement model [142,143]. CFA evaluates the degree to which observed variables align with the latent constructs intended to be measured, testing the goodness of fit of the measurement model [144,145].

Structural model: the relationships between exogenous (independent) and endogenous (dependent) variables were examined through the structural model following CFA [144,145]. The researchers used SmartPLs 4 software for the SEM analysis. [144,145]. It is variance-based, suitable for predictive applications, and does not require a normal distribution of data. Additionally, PLS-SEM supports nonparametric multigroup analysis for comparing groups [146].

5. Results

5.1. Demographic Characteristics

Of the 282 MSME respondents, 78.8% were men and the remainder were women (

Table 1. Demographic Information.

Demographic Characteristics	N (305)	(%)
Gender
Male	238	78.0
Female	67	22.0
Total	305	100
Age
20–30 years	81	26.6
31–40 years	102	33.4
41–50 years	53	17.4
51–60 years	55	18.0
61 years and above	14	4.60
Total	305	100
Education
Never attended school	46	15.1
Primary	34	11.1
Secondary	116	38.0
Tenth Grade	40	13.0
Twelfth Grade	33	10.8
Graduation	15	4.90
Graduation and higher	21	6.90
Total	305	100
Location of Firms
Sukkur	164	53.8
Larkana	95	31.1
Jacobabad	17	05.6
Khairpur	29	09.5
Total	305	100
Business Type
Manufacturing	60	19.7
Retail	91	29.8
Wholesale	32	10.5
Agriculture	39	12.8
Livestock	21	6.90
Poultry	24	7.90
Services	18	5.90
Other	20	6.60
Total	305	100

). The majority of respondents were between the ages of 31 and 40 (33.4%). Moreover, 38% of respondents had completed secondary education while 4.9% had completed further education. Only 15.1% of those who were surveyed had never completed any degree of education. Additionally, most respondents were employed in the retail industry (29.8%). The remainder worked in the wholesale (10.5%), manufacturing (19.7%), services (5.90%), livestock (6.90%), agricultural (12.8%), and poultry (7.90%) sectors. Lastly, 53.8% of firms were located in Sukkur and 31.1% were located in Larkana. Meanwhile, 9.5% were in Khairpur and 5.6% were in Jacobabad.

5.2. Measurement Model Evaluation

The loading factor (LF) of each item was calculated to determine content validity. “Average variance extracted (AVE)” validity, reliability, and internal consistency were assessed using “composite reliability (CR)” and “Cronbach’s alpha (α)” [37,132]. These constructions’ values were acceptable. The results are in

Table 2. Measurement Model Evaluation.

Constructs	Factor Loading	CR	AVE	A
AI-Enabled Application (AEA)	0.89	0.95	0.77	0.91
	0.91
	0.87
	0.86
Blockchain Application (BCA)	0.75	0.88	0.64	0.82
	0.78
	0.86
	0.82
Big Data Analysis (BDA)	0.86	0.92	0.74	0.88
	0.86
	0.85
	0.86
Economic Value (ECV)	0.81	0.90	0.63	0.86
	0.77
	0.77
	0.87
	0.77
IoT Application (IoA)	0.82	0.88	0.65	0.82
	0.78
	0.86
	0.76
Social Media Application (SMA)	0.82	0.88	0.65	0.82
	0.83
	0.83
	0.76
SME Performance (SMP)	0.77	0.87	0.69	0.77
	0.87
	0.84
Social Value (SOV)	0.77	0.88	0.59	0.82
	0.81
	0.83
	0.73
	0.68

Note: CR = composite reliability; AVE = average variance extracted; α = Cronbach’s Alpha.

.

The study employed “heterotrait–monotrait (HTMT)” criteria, as outlined by Henseler et al. (2015) and subsequently revised by Franke and Sarstedt (2019), in order to ascertain the discriminant validity [137,147]. Based on the findings, all HTMT values were below 0.85 [148], as indicated in

Table 3. Heterotrait–Monotrait Ratio (HTMT_.85).

Constructs	AEA	BCA	BDA	ECV	IoT	SMA	SMP	SOV
AEA
BCA	0.07
BDA	0.05	0.12
ECV	0.08	0.83	0.20
IoA	0.05	0.70	0.15	0.78
SMA	0.07	0.60	0.07	0.71	0.78
SMP	0.07	0.64	0.12	0.78	0.75	0.63
SOV	0.07	0.96	0.20	0.84	0.82	0.71	0.78

. Therefore, it may be inferred that the participants comprehended the uniqueness of the eight conceptions and that the need for distinguishing validity was met. In aggregate, the results of both validity tests indicate that the measuring items possess both validity and reliability standards.

According to Fornell and Lacker (1981), discriminant validity requires the average variance shared between each concept and its measures to surpass the variance shared between the construct and other constructs [149]. Hence, the measures presented in

Table 4. Evaluation of Discriminant Validity Using Fornell and Larcker Criteria.

Constructs	AEA	BCA	BDA	ECV	IoT	SMA	SMP	SOV
AEA	0.88
BCA	0.06	0.80
BDA	−0.03	0.10	0.86
ECV	0.02	0.70	0.17	0.80
IoA	0.02	0.58	0.13	0.66	0.81
SMA	−0.05	0.49	0.04	0.60	0.65	0.81
SMP	0.01	0.51	0.10	0.63	0.60	0.50	0.83
SOV	0.05	0.80	0.18	0.71	0.67	0.59	0.61	0.77

demonstrate sufficient discriminant validity due to the fact that the correlation coefficient for each construct (in both the column and row components) is lower than the average variance extracted (AVE) by the indicators used to assess that construct, as indicated on the diagonal.

5.3. Structural Model Evaluation

This study analyzes the data to investigate the relationship among the variables, following an examination of the reliability, validity, and formative structure of the measurement model [144,150]. The bootstrapping technique was utilized in this work, employing 5000 bootstraps [151] and 305 examples to obtain route values and their corresponding significance levels (Figure 2), as suggested in [152]. Hair et al. (2011) conducted an assessment of the structural model by utilizing various statistical measures, including the path coefficient, t-value, p-value, and coefficients of determination (R²) [142]. Cohen (1998) suggests that R2 values of 0.19, 0.33, and 0.60 are indicative of weak, moderate, and strong associations, respectively.

Table 5. Coefficient of determination of endogenous variables.

Variables	R2	Adjusted R2	Remarks
Economic Value (ECV)	0.62	0.61	Substantial
SME Performance (SMP)	0.45	0.44	Moderate
Social Value (SOV)	0.73	0.72	Substantial

displays the R² value [153,154,155].

Based on the standardized path coefficients presented in

Table 6. Summary of hypothesis testing.

Hypotheses	β	t-Value	Decision
H1a = SMAs → ECV	0.44	8.72	Supported
H1b = SMAs → SOV	0.49	8.50	Supported
H2a = AEAs → ECV	0.01	0.17	Unsupported
H2b = AEAs → SOV	0.02	0.65	Unsupported
H3a = BDA → ECV	0.22	4.23	Supported
H3b = BDA → SOV	0.16	3.07	Supported
H4a = IoAs → ECV	0.25	4.07	Supported
H4b = IoAs→ SOV	0.22	4.14	Supported
H5a = BCAs → ECV	0.09	2.23	Supported
H5b = BCAs → SOV	0.08	2.26	Supported
H6 = ECV → SMP	0.40	5.34	Supported
H7 = SOV → SMP	0.32	4.01	Supported

, the empirical findings reveal significant positive relationships. Specifically, social media applications demonstrate a notable association with the creation of both economic value (H1a: 0.44, p < 0.05) and social value (H1b: 0.59, p < 0.05). Similarly, big data analytics shows a positive impact on the generation of economic value (H3a: 0.22, p < 0.05) and social value (H3b: 0.16, p < 0.05). IoAs also exhibit positive effects on both economic value (H4a: 0.25, p < 0.05) and social value (H4b: 0.22, p < 0.05). Furthermore, blockchain applications are found to positively influence the creation of economic value (H5a: 0.09, p < 0.05) and social value (H5b: 0.08, p < 0.05). Additionally, there is evidence of a positive relationship between economic value and SME performance (H6: 0.40, p < 0.05), as well as between social value and SME performance (H7: 0.32, p < 0.05). However, AI-enabled applications demonstrate a negligible and negative impact on both economic value (H3a: 0.01, p > 0.05) and social value (H3b: 0.02, p > 0.05).

5.4. Artificial Neural Network Analysis

A multi-layer Artificial Neural Network (ANN) was employed, comprising input, hidden, and output layers and utilizing the statistical software SPSS 29. Each output neuron in the ANN architecture features two hidden layers to facilitate deeper learning [156,157]. To prevent overfitting, a ten-fold cross-validation process was implemented, and the root mean square of error (RMSE) was calculated. The training involved 90% of the samples, while the remaining samples were used for testing, following the methodology outlined by previous studies [158]. The sigmoid activation function was applied to both the hidden and output layers, with the number of hidden layers being determined automatically. A training-to-testing data ratio of 90:10 was used to assess the prediction precision [159]. RMSE values were utilized to evaluate the accuracy of the ANN model in both training and testing datasets.

Table 7. RMSE values.

Training			Testing
N	SSE	RMSE	N	SSE	RMSE	Total Samples
271	79.418	1.8472	34	07.932	0.4830	503
277	88.778	1.7663	28	11.026	0.6275	503
278	86.833	1.7892	27	10.170	0.6137	503
271	83.905	1.7971	34	8.602	0.5030	503
265	80.203	1.8177	40	11.350	0.5327	503
275	83.102	1.8191	30	12.765	0.6523	503
266	79.519	1.8289	39	12.562	0.5675	503
274	79.674	1.8544	31	17.014	0.7408	503
271	83.813	1.7981	34	07.280	0.4627	503
277	87.066	1.7836	28	04.042	0.3799	503
271	79.418	1.8472	34	07.932	0.4830	503
277	88.778	1.7663	28	11.026	0.6275	503
Mean	83.231	1.8102	Mean	10.274	0.5563
SD	3.3075	0.0268	SD	3.3865	0.1001

Note: SSE = sum square of errors, RMSE = root mean square of error, N = sample size.

displays low average RMSE values of 1.8102 and 0.5563 for the training and testing processes, respectively, indicating a good model fit. It is noteworthy that according to Hyndman and Koehler (2006), RMSE scores are always positive, with a value of 0 representing flawless accuracy, which is rarely achieved in real predictive practice [160].

5.5. Ranking of Predictors

To assess the predictive strength of each input neuron, a sensitivity analysis was performed (

Table 8. Sensitivity analysis.

Neural Network (NN)	BCA	BDA	IoA	SMA
NN (i)	0.15	0.56	1.00	0.52
NN (ii)	0.03	0.95	1.00	0.97
NN (iii)	0.25	0.44	1.00	0.39
NN (iv)	0.04	0.70	1.00	0.59
NN (v)	0.17	0.40	1.00	0.52
NN (vi)	0.10	0.95	1.00	0.83
NN (vii)	0.10	0.11	1.00	0.23
NN (viii)	0.22	0.64	1.00	0.56
NN (ix)	0.08	0.35	1.00	0.48
NN (x)	0.19	0.77	1.00	0.63
Average importance	0.58	0.52	0.72	0.36
Normalized importance (%)	19%	77%	100%	63%

Note: BCA = blockchain application, BDA = big data analytics, SMA = social media application, IoA = IoA.

) to determine the normalized importance of these neurons. This was accomplished by dividing their relative importance by the maximum importance and expressing the results as a percentage [161]. The analysis revealed that BCAs (19%), SMAs (63%), and BDA (77%) are identified as the subsequent most influential predictors after IoA (100%).

6. Discussion

This research explores the adoption of digital technologies by SMEs in Pakistan, focusing on their role in sustainability and value creation. The study examines how social media, big data analytics, the IoT, blockchain, and AI-enabled applications affect economic and social values. By understanding these factors, SMEs can enhance their operational performance and develop sustainable business models.

In the digital era, SMEs are increasingly turning to various digital technologies to drive growth and competitiveness. This study investigates the specific impact of these technologies on economic and social value creation and how these values, in turn, influence SME performance. The research was driven by two central questions:

What is the impact of SMEs using various digital technologies on the creation of social and economic value? How does the creation of social and economic value impact SME performance?

The findings from the quantitative analysis reveal that social media, big data analytics, the IoT, and blockchain applications significantly enhance both economic and social values for SMEs. However, AI-enabled applications did not demonstrate a substantial impact on either economic or social value. Additionally, the creation of economic and social values was found to significantly boost SME performance.

H1a: SMAs → ECV and H1b: SMAs → SOV

Social media applications (SMAs) have emerged as powerful tools for small and medium-sized enterprises (SMEs), influencing both economic and social dimensions of value creation.

Social media platforms enable SMEs to expand their market reach, engage with customers directly, and enhance brand visibility, thereby driving economic value. Agnihotri et al. (2022) emphasize that the effective use of social media can lead to increased sales and revenue growth for SMEs [162]. The research in [58,64] supports this, highlighting that SMEs support social media platforms effectively and can achieve significant competitive advantages through enhanced customer acquisition and market penetration.

In addition to economic benefits, SMAs foster community engagement, customer interaction, and brand loyalty, thereby enhancing social value for SMEs. Agnihotri et al. (2022) argue that platforms such as Facebook, Instagram, and Twitter enable SMEs to build relationships with customers, receive feedback, and establish a positive brand image within their communities [162]. This community engagement not only strengthens customer loyalty but also enhances the social impact of SMEs.

The integration of social media applications (SMAs) into SMEs’ business strategies proves instrumental in achieving dual benefits of economic and social value creation [63,163]. By adopting these platforms effectively, SMEs can enhance their competitiveness, expand their customer base, and strengthen community ties, thereby fostering sustainable growth and development.

H2a: AEAs → ECV and H2b: AEAs → SOV

Hypothesis H2a and H2b focused on AI-enabled applications (AEAs), which represent a growing area of interest for SMEs aiming to utilize advanced technologies for business growth. Despite the potential benefits of AI technologies, their impact on economic value creation for SMEs remains limited. Research suggests that while AI can enhance operational efficiency and decision-making processes in larger enterprises, its adoption among SMEs is often constrained by challenges such as high implementation costs, a lack of technical expertise, and concerns over data privacy [21,58,164]. These factors contribute to the lower adoption rates and, consequently, the limited impact of AI on economic value creation in SMEs.

Similarly, the adoption of AI-enabled applications (AEAs) among SMEs has shown limited direct impact on social value creation [54,127]. Unlike larger firms that can afford substantial investments in AI for customer engagement and service personalization, SMEs often struggle to integrate AI technologies effectively into their operations [58]. This limits their ability to enhance social value through improved customer interactions, community engagement, or social impact initiatives.

While AI-enabled applications (AEAs) hold promise for transforming business operations, their adoption and subsequent impact on economic and social value creation in SMEs remain constrained by various challenges.

H3a: BDA → ECV and H3b: BDA → SOV

Hypothesis H3a and H3b discuss big data analytics (BDA); this technology emerged as a significant tool for SMEs seeking to enhance their operations, understand customer behaviors, and innovate their products and services. This hypothesis positively influences both economic value creation (ECV) and social value creation (SOV) in SMEs,

Big data analytics (BDA) plays a significant role in driving economic value creation for SMEs. By analyzing vast amounts of data, SMEs can gain insights into consumer preferences, market trends, and operational efficiencies [165]. This capability enables SMEs to optimize their production processes, improve resource allocation, and innovate new products and services tailored to customer needs [166,167]. Studies have shown that the effective utilization of BDA leads to cost savings, revenue enhancement, and overall business growth [168,169].

In addition to economic benefits, big data analytics (BDA) contributes significantly to social value creation for SMEs. By understanding consumer behaviors and preferences more deeply, SMEs can offer personalized products and services that meet societal needs and expectations [170,171]. Moreover, BDA helps SMEs enhance customer satisfaction, build trust, and foster community engagement through targeted marketing campaigns and social initiatives [172,173]. This proactive approach not only strengthens an SME’s brand reputation but also enhances its social impact within the community.

Big data analytics (BDA) emerges as a pivotal tool for SMEs to enhance both economic and social value creation.

H4a: IoAs → ECV and H4b: IoAs → SOV

The adoption of Internet of Things applications (IoAs) presents significant opportunities for small and medium-sized enterprises (SMEs) to enhance their operational efficiency, innovate new products and services, and create both economic and social value. This study revealed that IoAs positively influence economic value creation (ECV) and social value creation (SOV) in SMEs, supported by empirical findings.

Internet of Things applications (IoAs) enable SMEs to improve their operational processes through real-time monitoring, predictive maintenance, and automation [174]. By connecting devices and systems, SMEs can gather valuable data insights that optimize resource utilization, reduce operational costs, and enhance productivity. Studies indicate that IoAs facilitate new business models and revenue streams for SMEs, thereby contributing to their economic growth and sustainability [81].

In addition to economic benefits, Internet of Things applications (IoAs) play a crucial role in creating social value for SMEs [58,175]. By adopting IoAs, SMEs can enhance customer experiences, ensure product quality, and promote sustainable practices [58]. IoT-enabled products and services can address societal challenges such as environmental sustainability and public health by offering innovative solutions and improving community well-being [176]. Furthermore, IoAs foster closer engagement with stakeholders and enhance corporate social responsibility efforts, thereby strengthening the SME’s reputation and trustworthiness in the marketplace.

Internet of Things applications (IoAs) emerge as a transformative technology for SMEs, enabling them to create significant economic and social value because of IoA capabilities; SMEs can drive operational efficiencies, innovate customer-centric solutions, and contribute positively to societal needs.

H5a: BCAs → ECV and H5b: BCAs → SOV

Blockchain applications (BCAs) have emerged as transformative technologies with the potential to significantly impact economic and social value creation for small and medium-sized enterprises (SMEs) [82]. The results of both hypothesis H5a and H5b highlight that BCAs significantly influence both the economic value creation (ECV) and social value creation (SOV) in SME contexts as supported by empirical evidence in the Section 5.

Blockchain technology offers SMEs several opportunities to enhance economic value creation [83,87]. By providing transparent and secure transaction records, BCAs enable SMEs to streamline supply chain operations, reduce transaction costs, and mitigate fraud risks [82,85]. These efficiencies contribute to improved operational performance and profitability. Moreover, blockchain’s decentralized nature facilitates direct peer-to-peer transactions, thereby eliminating intermediaries and reducing overhead costs for SMEs (Swan, 2015). Studies suggest that BCAs can foster innovation in financial services, logistics, and digital identity management, thus opening new avenues for revenue generation and market expansion [118].

Beyond economic benefits, blockchain applications also play a crucial role in creating social value for SMEs. BCAs enhance trust and accountability in business transactions by providing immutable records of data and transactions [85]. This transparency builds stronger relationships with stakeholders, including customers, suppliers, and investors, thereby enhancing an SME’s reputation and brand trustworthiness [177]. Moreover, blockchain technology supports sustainable practices by enabling traceability in supply chains, promoting fair trade practices, and ensuring ethical sourcing of materials [82,178]. These social impacts are increasingly valued by consumers and contribute to a positive societal image for SMEs.

Blockchain applications (BCAs) represent a promising avenue for SMEs to enhance both economic and social value creation. By leveraging blockchain technology, SMEs can achieve operational efficiencies, innovate new business models, and uphold ethical standards, thereby positioning themselves competitively in the marketplace. Future research should focus on addressing regulatory challenges, enhancing scalability, and exploring novel applications of blockchain in diverse SME sectors.

H6: ECV → SMP and H7: SOV → SMP

Hypotheses H6 and H7 have shown positive relationships between economic value creation (ECV) and social value creation (SOV) with SME performance (SMP), as hypothesized in the study. Both hypotheses significantly contribute to SMEs’ overall performance.

Economic value creation (ECV) plays a pivotal role in enhancing SME performance. When SMEs effectively create economic value through increased revenues, cost efficiencies, and profitability [58], they are better positioned to achieve sustained competitive advantage and financial stability. Research indicates that SMEs that prioritize economic value creation can reinvest profits into innovation, expansion, and talent development, thereby fostering long-term growth and resilience [179]. This positive relationship stresses the importance of strategic financial management and operational excellence in SMEs.

Similarly, social value creation (SOV) significantly impacts SME performance by enhancing reputation, stakeholder trust, and brand loyalty [58,180]. SMEs that engage in socially responsible practices, such as environmental stewardship, community engagement, and ethical sourcing, tend to attract socially conscious consumers and investors. This can lead to enhanced market positioning, reduced regulatory risks, and improved employee morale, all of which contribute to improved overall performance metrics [58].

The findings from this study highlight the dual importance of both economic and social value creation for SMEs’ performance. By strategically aligning their business practices with value creation initiatives, SMEs can not only enhance financial outcomes but also contribute positively to society and the environment. Future research should explore nuanced aspects of value creation dynamics in different industry contexts, address measurement challenges, and evaluate the long-term sustainability impacts of these practices.

6.1. Theoretical Implications

This study significantly advances the theoretical understanding of how digital technologies—such as social media, AI, big data analytics, the IoT, and blockchain—affect economic and social value creation within SMEs. By empirically examining these technologies’ impacts on value creation and SME success, the study enriches theoretical frameworks in the context of developing countries.

Unlike previous studies utilizing Structural Equation Modeling (SEM), which primarily identifies linear relationships, this research employs a deep-architecture Artificial Neural Network (ANN). This approach reveals both linear and non-linear correlations among variables, providing a more comprehensive understanding of how digital technologies influence SMEs. Integrating SEM with ANNs enhances the robustness of findings, validating the accuracy and depth of the relationships uncovered.

The application of SEM and ANN methodologies to investigate digital technology adoption among Pakistani SMEs fills a significant gap in the literature. By focusing on sustainability and value creation, this research not only contributes to SME management practices but also lays the groundwork for future studies exploring similar themes in other developing economies.

6.2. Practical Implications

Practically, this research offers valuable findings for managers and leaders of SMEs aiming to implement effective digital transformation strategies. Based on SEM-ANN findings, SMEs are encouraged to prioritize technologies such as the IoT, AI, cloud computing, and big data analytics, which have demonstrated positive impacts on sustainability and value generation.

SMEs can adopt digital technologies to enhance economic and social value creation, thereby building brand loyalty, increasing sales, and fostering community engagement. While AI-enabled applications currently show limited economic and societal benefits, ongoing advancements in AI present opportunities for future applications that may enhance operational efficiency and customer experiences.

It is crucial for SMEs to regularly assess how digital technologies align with sustainability goals and company success metrics. Setting Key Performance Indicators (KPIs) that support sustainability and value creation objectives can guide strategic decision-making and resource allocation within SMEs.

Collaborations with technological, academic, and other organizational partners can provide SMEs with the resources, skills, and networks necessary for the effective and innovative use of digital technologies. Such alliances enable SMEs to stay updated with technological advancements and regulatory requirements, thereby fostering sustainable business practices.

7. Conclusions

This study investigates the impact of digital technologies on economic and social value creation within small and medium-sized enterprises (SMEs), with a specific focus on social media, big data analytics, the IoT, blockchain, and AI-enabled applications. Conducted in the context of Pakistani SMEs, the research seeks to understand how these technologies contribute to sustainability and value creation. The motivation stems from the increasing importance of digital transformation for SMEs in emerging economies like Pakistan, where technological adoption can significantly influence business outcomes and competitiveness.

To achieve its objectives, the study employs a hybrid methodology integrating Structural Equation Modeling (SEM) and Artificial Neural Networks (ANNs). SEM is utilized to establish and validate theoretical relationships among variables related to digital technology adoption and economic/social value creation. Meanwhile, ANNs complement SEM by capturing both linear and non-linear correlations, providing deeper insights into how these technologies interact and impact SME performance. Quantitative data from SMEs in Pakistan form the basis of rigorous statistical analyses, ensuring a robust assessment of the relationships between digital technologies and economic/social values.

The findings highlight several significant insights into the role of digital technologies in SMEs:

The impact of digital technologies—social media, big data analytics, the IoT, and blockchain applications are shown to markedly enhance both economic and social value creation within SMEs. These technologies enable SMEs to improve customer engagement, operational efficiency, and innovation capabilities, thereby fostering sustainable growth.

Despite their potential, AI-enabled applications currently exhibit limited influence on economic and social value creation compared to other digital technologies. This suggests barriers to AI adoption in SMEs, such as high costs, technical complexity, and regulatory challenges, which need addressing for their broader implementation.

The integration of SEM and ANN methodologies validates the study’s findings, demonstrating robustness in identifying and understanding the complex relationships between digital technology adoption and SME outcomes. This comprehensive analytical approach underscores the necessity of using advanced methods to capture the full spectrum of technological impacts.

SMEs are encouraged to invest strategically in digital technologies like social media, big data analytics, the IoT, and blockchain for enhanced sustainability and value creation, boosting competitiveness and long-term viability. Despite current challenges, advancements in AI offer future opportunities for improving operational efficiency and customer engagement in SMEs. Policymakers should support initiatives that facilitate AI adoption and innovation, promoting economic growth and job creation within the SME sector through enabling policies.

7.1. Limitations and Future Research

This study, while contributing valuable insights, is subject to several limitations. Firstly, the effectiveness of the SEM-ANN model hinges on the quality and quantity of the data used, potentially limiting its applicability to diverse SMEs, industries, or geographic contexts. Secondly, the complexity of the hybrid SEM-ANN methodology may present challenges for practitioners without advanced statistical expertise, suggesting the need for future research to explore simpler analytical approaches. Thirdly, the reliance on cross-sectional data restricts the study’s ability to capture the dynamic aspects of technology adoption and its long-term implications for SMEs.

7.2. Future Research Directions

Future research endeavors can address these limitations through several avenues. Longitudinal studies could provide deeper insights into the evolving dynamics of technology adoption within SMEs over time. Exploring external factors such as regulatory environments, economic fluctuations, and industry-specific trends could enhance the understanding of the broader impacts of digital technology adoption on SME sustainability and competitiveness. Additionally, integrating fuzzy logic or system dynamics approaches may bolster the SEM-ANN model’s capacity to manage uncertainty and capture dynamic interactions effectively. Furthermore, investigating how digital technologies influence the economic, social, and environmental dimensions of sustainability will enrich our understanding of SMEs’ strategic use of technology for long-term growth and resilience.