The impact of digital transformation on big data analytics and firm’s sustainability performance in a post-pandemic era

Abstract

BACKGROUND:

COVID-19 caused an exogenous shock to global economies, businesses and people. However, digitalization is also helping many companies adapt and overcome the reality of COVID-19. The fact that people and companies are increasingly using technology in their daily lives to deal with this extraordinary situation demonstrates the acceleration of the digitalization process.

OBJECTIVE:

The aim of this research is to examine the mediating role of Big Data Analytics (BDA) in the relationship between digital transformation (DT) and economic, environmental and social sustainability performance.

METHODS:

For data analysis and hypothesis testing, partial least squares structural equation modeling (PLS-SEM) was used on 304 managers level employees in small and medium enterprises (SMEs) in Mozambique.

RESULTS:

The findings show that DT positively affects BDA in Mozambican SMEs. Furthermore, BDA positively impacts economic and environmental performance. In addition, BDA has a significant mediating role in the association between DT and economic and environmental performance. However, this relationship is insignificant regarding social performance.

CONCLUSIONS:

These findings have important implications for SMEs managers and policy makers, who can develop a coherent strategy to realize BDA opportunities, reduce costs and provide strategic value to improve firm sustainability performance in a post-pandemic world.

Keywords

Post-pandemic digital transformation big data analytics sustainability performance

Luisa Tomas Cumba was born in Maputo, Mozambique, 1992. She received the B.S in 2016 and M.S in 2019 degrees in Economics and International Trade from Jiamusi University, China and from University of Science and Technology Beijing. She worked as a primary school director assistant from 2016 to 2017.

From 2019 to the present, she is a PhD. research scholar at University of Science and Technology Beijing. She has written few papers related to areas of institutional management, sustainability, and big data analytics.

Miss. Luisa has published papers and few under review on reputable SCI and SSCI indexed journals such as; International journal of emerging markets.

Xiaoxia Huang is a professor in the School of Economics and Management, University of Science and Technology Beijing. She got her Bachelor degree of Engineering and Master degree of Management from University of Science and Technology Beijing, her Bachelor of Economics from University of International Business and Economics, and her Ph.D in Management Science and Engineering from Beijing Institute of Technology. Her major research areas include portfolio selection, risk analysis, investment optimization and innovation.

Moustafa Mohamed Nazief Haggag Kotb Kholaif was born in Alexandria, Egypt, 1987. He received the B.S. in 2009, and M.S. degrees in managerial accounting from accounting department, Faculty of commerce, Tanta university, in 2016. He worked as a teaching assistant from 2009 to 2019.

From 2019 to 2023, he has completed his PhD from the University of Science and Technology Beijing. Dr. Kholaif has several publications in the areas of green supply chain management, corporate social responsibility, and big data analytics.

Dr. Kholaif published and reviewed several manuscripts for renowned SCI and SSCI indexed journals such as; Sustainable energy technologies and assessments; Environmental science and pollution research; International journal of emerging markets.

1 Introduction

Digital transformation (DT) has become a business imperative. Digitalization is the application of digital technologies such as the Internet of Things, big data analytics and cloud computing to the production activities of companies with the aim of changing business processes and organizational management [1]. Today, firms are experiencing great pressure from stakeholders to integrate social and environmental concerns into their business decisions and strategies. Although digitalization has attracted considerable attention in research and practice [2, 3], the effects of enterprise digitalization on economic, environmental, and social sustainability performance remain unclear. For example, DT in Mozambican small business is being massively challenged as managers are faced with tough decisions pertinent to creating entirely new business models on the backbone of technology [4].

Furthermore, the effect of DT on economic, environmental, and social sustainability performance has been the subject of conflicting conclusions in the past. Some studies, have found a positive correlation between DT and economic and environmental sustainability performance [5, 6], others have suggested dissociation [7, 8]. Regarding inconclusive results, some researchers have questioned the traditional research approach [9, 10]. They argued that there is no precise method for establishing a causal link between DT and sustainable performance, and that the direct link between DT and sustainable performance is not 100 % reliable because it may be influenced by a number of other factors ignored by many studies, is far more complex than it appears and lacks a comprehensive mediating mechanism linking DT to outcomes [11]. Thus, the aim of this study is to investigate whether BDA can act as a mediator between DT and economic, environmental, and social sustainability performance of Mozambican SMEs after the pandemic.

Big data analytics (BDA) has become a key technology for doing business due to the constant increase in data volumes and varieties, and the distributed computing model processes big data quickly [12]. According to Chen [13], BDA is an integral part of effective digital transformation implementation. It can be argued that DT might not be fully implemented in organizations without the active support, collaboration and teamwork from organizational members. Although BDA is an important element contributing to positive organizational outcomes, its mediating role in the relationship between DT and economic, environmental, and social sustainability performance has not been sufficiently explored [14]. Much of the existing literature focused on investigation the mediating role of behavioral integration on the relationship between DT and organizational performance [15 –18], whereas there is not much literature on the impact of DT on economic, environmental and social sustainability performance. Authors could only find studies showing a link between digitalization and environmental performance [1, 5]. To advance research on BDA functions in DT and fill the above-mentioned research gaps, this study attempts to answer the following three research questions:

RQ1: What is the relationship between digitalization and BDA in the manufacturing industry in Mozambique?

RQ2: What is the relationship between BDA and firm economic, environmental and social sustainability performance?

RQ3: How does BDA mediate the relationship between DT and economic, environmental and social sustainability performance?

The main objective of this study is to examine the relationships between DT, BDA and economic, environmental and social sustainability performance after the pandemic. Additionally, examine how BDA influences the connection between constructs. Our work highlights the concerns and ambiguity of Mozambican SME managers in a post-pandemic world. Firstly, according to the Bertelsmann Stiftung’s Transformation Index (BTI) report [19], Mozambique’s digital transformation is accelerating, with 21% of the population using mobile networks to access the Internet. However, digital transformation poses a threat to end users, including the Mozambican government and the private sector. Due to the extensive storage of data on citizens who rely on public services, public institutions face serious consequences. It is for this reason that the Government of Mozambique has identified cybersecurity as a priority segment of the country’s digital transformation process [20]. Secondly, the practical importance of this study is to help managers use BDA management operations and achieve better economic, environmental and social performance, and to help Mozambican SMEs cope with economic difficulties, and environmental problems caused by the epidemic [21]. This can enable organizations to achieve their sustainable economic, environmental and social development goals and respond to the desires of their stakeholders [12].

The originality of this study lies in its exploration of the link between digital transformation (DT), big data analytics capabilities (BDA) and sustainable economic, environmental and social performance. In addition, the study examines the mediating effect of the adoption of new technologies, especially big data analytics (BDA) capabilities, on this relationship. Furthermore, the study’s focus on small and medium enterprises (SMEs) in Mozambique is also unique, as most previous studies in this area have focused on large companies in developed countries. By analyzing data from 304 SME managers, the study provides valuable insights into how these companies can overcome the challenges of the pandemic and improve their economic, environmental and social practices. In addition, the study results highlight the importance of using innovative technologies (BDA) to support post-pandemic sustainability efforts. The mediating effect of BDA on the relationship between DT and economic, environmental and social sustainability performance suggests that data analysis can help SMEs make better decisions about their supply chains. Overall, this study contributes significantly to the literature on sustainability in the context of the post-COVID-19 pandemic. It provides practical recommendations to SMEs in Mozambique and other developing countries to improve their economic, environmental and social performance and increase their resilience to future crises.

The motivation and significance of this study stems from linking research variables to real-world problems faced by business organizations in emerging markets after the pandemic, benefiting both managers and the public. First, according to Alfazema [19], SMEs, especially micro-enterprises, represent an important source of income for the large and growing population of Mozambique. Although SMEs make up the majority of enterprises, their impact on formal employment is small, accounting for only 1.3% of the total workforce in 2020 [20]. This is much less than the actual number of open positions. Therefore, Mozambique needs to strategically pursue new and future sectors such as the digital economy, life and health services and new materials, while accelerating the construction of new infrastructure projects such as internet and data centers to take advantage of the opportunities. Second, the COVID-19 pandemic has challenged businesses across various industries. Today, firms are experiencing great pressure from stakeholders to integrate social and environmental concerns into their business decisions and strategies [21]. Third, research on DT has primarily focused on business organizations with relatively little research on firms’ economic, environmental, and social sustainability performance in the context of African countries such as Mozambique. Moreover, the digital ecosystem in Africa has the ability to stimulate economic growth, offer new opportunities, advance gender and social equality, and generate new employment [22, 23]. Hence, it is crucial to investigate the impact of DT on BDA adoption towards economic, environmental, and social sustainability performance in Mozambican SMEs.

The present study makes several contributions to the literature. First, this study examines the relationship between DT and economic, environmental and social sustainability performance. Furthermore, although extensive research has been conducted on the impact of DT on organizational performance, little is known about its impact on BDA adoption. The study shifts the predominant focus of the DT literature on individual outcomes to the effect of DT on BDA practices. Second, the findings provide a more holistic management model that could help identify recipes that can effectively lead to improved business performance across economic, environmental and social sustainability. Third, to the authors’ knowledge, this study is among the few to propose a model in which BDA practices mediate the relationship between DT and economic, environmental, and social sustainability performance, thus explaining the mechanism through which DT can influence economic, environmental, and social sustainability performance.

Moreover, this study utilizes the resource-based view theory to explain the relationships between DT, BDA, and economic, environmental, and social sustainability performance. The resource-based view asserts that a firm’s ability to acquire, integrate, and deploy data-driven assets, as well as marketing expenditures and expertise, are crucial to its post-pandemic sustainability success [24]. As DT becomes increasingly complex and rapidly evolving, BDA is becoming a winning strategy for companies to gain a competitive advantage. BDA is defined as a holistic approach to managing, processing and analyzing the dimensions related to 5 V data (i.e. volume, variety, velocity, veracity and value) to create actionable ideas to deliver sustainable value, measure performance and create competitive advantages [25 –27]. Therefore, BDA will assist businesses in obtaining an inclusive comprehension of the opportunities and risks that the digital transformation operations may present [28]. This study enriches the literature by investigating the relationship between DT, BDA, and economic, environmental, and social sustainability performance.

The remainder of the paper is organized as follows: In Section 2, the theoretical background and literature review are presented. In Section 3, we discuss the methodology. The obtained results are presented in section 4. Discussion, theoretical and practical implications are covered in Section 5. Section 6 contains conclusions, limitations and policy recommendations for future research.

2 Theoretical background and literature review

In the following subsections, we will lay the theoretical foundations of this research. The authors then develop the study’s hypothesis based on theory.

2.1 Theoretical background

The resource-based view theory was developed by Penrose [32], who argued that firms have assets, some of which allow for competitive advantage, while others lead to long-term performance improvements [33]. This school of thought holds that an organization’s success stems from its ability to make the most of its resources, both those it can see and those it can’t (such as the personalities of its top executives) [25, 26]. Several studies have been conducted to understand the internal drive perspectives and sustainability-oriented practices and strategies of SMEs from a resource-based perspective (RBV) [27, 28]. According to the RBV theoretical perspective, a firm will acquire resources and capabilities that are valuable, uncommon, hard for rivals to imitate (imperfect imitation), hard to buy and sell (irreplaceable), and difficult to acquire over time [29]. A growing number of SMEs studies have adopted a resource-based perspective [30, 31]. This trend began with a focus on environmental aspects, i.e. from a natural resource-based perspective [32], but then extended to more general issues of SMEs [33, 34].

Previous studies have shown that RBV refers to firms’ ability to achieve competitive advantage through value creation strategies, not only from unique and heterogeneous resources, but also to the ability to integrate and exploit these resources as core organizational “capabilities” [12, 35]. Several organizational capabilities have been discussed in the literature, such as shared vision, employee involvement, stakeholder management, innovation, strategic initiative, capital management, superior learning, and integrating BDA adoption into strategic planning [9, 19]. Therefore, the participation of companies in BDA depends not only on sufficient financial, technical and management resources, but even more important for SMEs is the ability to mobilize and integrate these resources. From the perspective of RBV theory [34], this study examines the possible mediating role of BDA in the relationship between DT and sustainable performance. In this study, the work of Theodoraki and Caputo [45] was cited as the overarching hypothesis. Every organization is different and has its own assets and limitations, which are reflected in the resources available to it. The RBV of the company, which is based on the concept that even very direct competitors may have materially distinct strengths and vulnerabilities, explains the heterogeneous nature of the market. Thus, Mozambican SMEs will benefit from digital transformation through big data analytics to achieve sustainability performance.

2.2 Literature review

The impact of the COVID-19 pandemic has accelerated the adoption of online platforms and remote work systems. Whether it’s an office filled with cubits, a meeting room, or a video conference, digital transformation is a fact in today’s workplaces. Increasing dependence on technology, global market growth, and the importance of multinational corporations have increased the need for managers and employees to recognize and address the challenges posed by effective management operations. The following literature review confirms that digital transformation presents solutions that go beyond mere business processes, discusses specific and general solutions, and concludes that small business initiatives are need for today and tomorrow’s workforce.

Digital transformation has long been recognized as essential to digitally transforming business operations, helping individuals improve their results today, drive growth tomorrow, and remain competitive and agile in response to changing markets [46, 47]. Although digitalization is not a new phenomenon, the challenges and opportunities that arise from it are constantly changing. Before the emergence of COVID-19, the challenges of digital transformation were mainly focused on the fourth industrial revolution, associated with the concepts of Industry 4.0, Internet of Things (IoT) and Web 4.0 [1, 48]. The challenges concerned both the revolution of concepts and technologies and the speed of this digital transformation. In the COVID-19 era the challenges were exposed and it is essential to involve the entire organization and stakeholders in this process. Furthermore, the speed with which this change occurred was enormous. Organizations had to do this regardless of their previous positioning and experience with digital transformation processes. Inevitably, organizations are heading down the path of digital transformation. The key question, however, is whether they are prepared for this change. Jardak [6] highlights that companies, even those most advanced in digitally transforming their businesses and workflows, are not yet fully prepared to meet the challenges of digital transformation. Digitalization requires process restructuring, greater business agility, investments in more organic structures, greater standardization and automation to optimize responsiveness to customers. COVID-19 has brought difficult and uncertain times and accelerated the inevitable processes of digital transformation. At this point, it is important to think about the post-COVID-19 world and, above all, to explore how to take advantage of these challenges both within the company and the internal organization and transform them into new opportunities. Thus, this research examines how managers can exploit these challenges and convert them into new opportunities, both in the business and in the internal organization. In this sense, this study seeks to explore the opportunities that organizations will gain from the digitalization of their operations to evaluate the impact of big data analytics on economic, environmental and social sustainability performance. These components were chosen because of the significant impact that Rehman [29] and Lutfi [30] predict on business activities due to COVID-19.

3 Hypotheses development

3.1 The impact of DT on BDA

Previous studies have shown that restructuring processes, including making them more flexible, developing more organic structures, introducing standardization and industrializing as many tasks as possible, are essential to optimize a company’s responsiveness to consumers through digitalization [11, 29]. Thus, DT is data-driven and dependent on efficient data collection, cyber analytics, and application. In a similar vein, Lutfi et al. [12] proposed that BDA refers to a massive and comprehensive collection of data that requires the use of specialized storage technologies to extract, organize, and convert the information so that it can be broadly and quickly reviewed. For example, Jiatong [9] claims that in the post-pandemic future, there will be a dramatic rise in the quantity of data on the Internet, and that understanding and using this data would be essential for the survival of businesses. Likewise, Feroz [5] and Zhang [20] argue that companies that have experienced a wider range of DT are better equipped to address their most pressing challenges and strengthen their BDA capabilities. Therefore, we hypothesized that:

H1: There is a significant positive correlation between DT and BDA.

3.2 The impact of BDA on sustainability performance after the pandemic

Researchers like Bag et al. [46] and Cazeri et al. [36] indicated that the pandemic has forced nations to increase investment in organic structures, standardization and automation. For instance, Shah et al. [47] argued that majority of businesses have experienced substantial changes and implemented strategies using technology within a very short time frame. Thus, this situation leads researchers to address firms’ sustainability performance after the pandemic [22, 48]. We group the sustainability performance in three dimensions:

3.2.1 Economic sustainability performance

Economic sustainability performance refers to the cutting down on expenses including raw materials, power, trash removal, and fines for environmental catastrophes [53]. Moreover, Meiyou [35] argued that the BDA embodies methods to achieve sustainability with the purpose of creating a competitive advantage. Furthermore, Phuyal et al. [50] discovered that BDA has a noteworthy impact on operational concerns related to financial sustainability, where strategic decisions are crucial for the long-term growth and success of an organization. Ranjbari [48] suggested that high economic sustainability performance, which integrates BDA capabilities into internal firm decision-making processes, is essential for growth. Thus, we hypothesized that:

H2: There is a significant positive link between BDA and the economic performance of SMEs after the epidemic.

Furthermore, Potočan et al. [49] claimed that most firms agree that using BDA capabilities in managerial decision-making is essential for maintaining the long-term viability of businesses after the pandemic. For example, Chen et al. [51] argued that in recent years changes occurred in the ability to acquire and appropriately interpret massive amounts of information. These changes are related to the creation of increasingly complicated algorithms and the ongoing rise in computational storage capacity. BDA capabilities and techniques are essential for business survival in the post-epidemic era, and the volume of online data is expected to increase significantly [24]. According to Spurk [52], BDA can play a crucial role in resolving business issues related to the pandemic. Therefore, we hypothesized that:

H3: BDA mediate the association between DT and economic performance in SMEs after the pandemic.

3.2.2 Environmental sustainability performance

Environmental sustainability performance is the ability to lessen pollution, waste, hazardous material usage, and natural catastrophe occurrences [53]. Businesses use limited resources in the production of goods and services; These resources can cause pollution of the atmosphere, water and soil [58]. According to Gökalp et al. [54], BDA practices include any attempt to mitigate the negative impact of a company’s goods or services on the environment. Such attempts aim to improve sustainable environmental performance by minimizing solid/liquid waste, reducing hazardous materials, reducing environmental accidents and improving community well-being. Additionally, Khan et al. [55] argued that BDA contributes to better sustainable environmental performance by reducing companies’ production waste. Thus, we hypothesized that:

H4: There is a significant positive correlation between BDA and environmental performance in SMEs after the pandemic.

Additionally, Lucivero [53] examined the link between BDA and environmental sustainability performance after a pandemic. The findings highlighted the need to focus on (1) the terminology presently used in discussions of data initiative governance, (2) the inherent contradiction between existing data initiative and environmental rules, and (3) challenges of equitable distribution. They suggested that scientists set quality requirements for collecting data and utilization to guarantee that data are inclusive of all relevant groups. Furthermore, Mupaikwa [56] asserted that BDA has sufficient potential to affect environmental sustainability performance in poor nations that have been more affected by the negative consequences of the pandemic. According to Jardak [6], BDA’s main objective is to improve environmental sustainability performance by enhancing business performance and employee well-being after a disaster. Therefore, we hypothesized that:

H5: BDA mediate the association between DT and environmental performance in SMEs after the pandemic.

3.2.3 Social sustainability performance

Çankaya [57] defined social sustainability performance as the business actions in relation to the community in which it operates, including social programs, the welfare of all parties, and training for ecological activities for all employees. Furthermore, Macassa et al. [58] and Lin et al. [25] claimed that BDA strategy is crucial for improving social performance as it fosters and opens up new channels for effective communication among different societal stakeholders. Also, Latifian [21] showed how BDA contributed to resolving social performance issues and redefining the activities associated with the social framework dialogue in different sectors. Therefore, we hypothesized that:

H6: There is a significant positive correlation between BDA and social performance in SMEs after the pandemic.

According to Hamouche [59] and Cazeri et al. [36], businesses may benefit from BDA technology’s ability to collect crucial data, identify promising new business prospects, and streamline internal decision-making procedures to improve social performance. Lerman et al. [41] argue that the implementation of the business association, including its employees, sellers, associates, and clients, will be critical to its DT processes. Furthermore, Zhang [13] believed that the adoption of remote work and models that allow high levels of interaction, collaboration, and talent to overcome the distance between staff locations is one of the challenges faced. However, new financial opportunities have been created as a result of the changes that digital technologies have made to the way that businesses operate on a daily basis [6]. Thus, we hypothesized that:

H7: BDA mediate the association between DT and social sustainability performance in SMEs after the pandemic.

Therefore, Fig. 1 illustrates the relationship between DT (the independent variable), BDA (the mediator), and firms’ economic, environmental, and social sustainability performance (the dependent variable).

Fig. 1

Research design and hypothesis development.

4 Data and methodology

4.1 Population, sample and procedures

The main objective of this study is to investigate the mediating role of BDA in the relationship between DT and economic, environmental and social sustainability performance of Mozambican SMEs after the pandemic. The target population for this study consisted of managers level employees in manufacturing and service SMEs sectors in Mozambique. The study was carried out in Maputo the capital city of Mozambique and the boiling pot of major activities in the country. Previous studies have shown that Mozambique’s manufacturing and service sectors are vital to the country’s economy, providing long-term growth and new jobs and much-needed disposable income for the country’s rapidly growing population [19, 63]. However, despite government initiatives, there is still a lack of research activities in SMEs, for which affective technologies are inevitable to address the challenges of the Covid-19 pandemic. Thus, we intend to relate digital transformation and economic, environmental and social sustainability performance of SMEs in Mozambique. A survey questionnaire was used to collect data to test the hypothesized relationships, as shown in Fig. 1. A total of 500 questionnaire were distributed through electronic channels, such as email and messaging apps like WhatsApp and Facebook. A total number of 329 questionnaire were received back reflecting a response rate of 64 percent. After discarding 25 incomplete or invalid survey, 304 were retained for statistical analysis. Considering the complexity of the proposed research model, this sample size is fairly sufficient for use of Structural Equation Model (SEM) to analyze the complicated path model suggested by Kline [64]. This study applied a modified version of the Cochran formula developed by Nanjundeswaraswamy and Divakar [70]. The formula can be shown in Equation (1). $n = \frac{n_0}{[1 + {\frac{(n_0 - 1)}{N}}]}$ (1)

Where n is the sample size and N is the population size. Around 278 people should be surveyed to ensure a margin of error of 5%, a 95% confidence level, and a t-value of 1.96 [70]. The survey was sent to 500 managers. A total of 304 completed questionnaires were returned, resulting in a response rate of over 55%, which we used for further analysis in this study. Thus, the sample size of 304 in our research is deemed adequate.

The data was collected between May 26th to August 14th, 2022. Before conducting the survey, the questionnaire was pre-tested by 7 academics and 5 data analysts’ practitioners. They first tested the survey to ensure that all measurement scales were understood and clear. After final changes were made, the survey was sent via email and Social Media platforms. Respondents were managers level employees (top-level managers, middle-level managers and low-level managers) familiar with big data analytics concepts. Respondents were asked to use a five-point Likert scale, with 1 representing “strongly disagree” and 5 representing “strongly agree.” Researchers collected specific data from a variety of sources to circumvent and resolve issues related to the possibility of common method bias [65]. Specifically, Top managers, with a proportion of 8.56%, include presidents, vice presidents, and others on BDA adoption. Managers in medium management with a 19.77% proportion. Managers at lower levels, with 71.67% on DT after the pandemic. Table 1 provides a more detailed demographics of the sample.

Table 1

Demographic characteristics of the sample

	Description	Count	Percentage
Enterprise size	Small-sized	200	68.13%
	Medium-sized	104	31.87%
Gender	Man	168	61.47%
	Woman	136	38.53%
Industry	Manufacturing-sector	210	70.00%
	Service-sector	94	30.00%
Management-position	Lower-level	230	71.67%
	Medium-level	48	19.77%
	Higher-level	26	8.56%

4.2 Variables measurement and explanation

In this study, all the five measurement items were adopted from existing research findings [26, 68]. These items measure managers’ perceptions/attitudes to help their company improve its operations. However, minor changes were made to the wording to suit the context of the study. Furthermore, the survey questionnaire followed a five-point Likert scale, with 1 (strongly disagree) and 5 (strongly agree):

The independent variable, digital-transformation consists of three items developed by Yu [9]. For example, the extent to which the manager develops a digital transformation strategy as a framework that integrates the overall adaptation, prioritization and adoption of digital transformation within the company. The manager uses the framework to develop the organization’s digital vision and create new business models based on digital capabilities. The manager implements new strategic priorities, create new ways of working and develop new knowledge.

The mediating variable, BDA comprises of six items based on the work of Kholaif [14]. For example, how often do managers use the latest technology to explore data; how often they use the lessons learned from various data processing operations; managers can greatly benefit from using data visualization analytics tools to help them make sense of the massive data. Details contained centrally; use dashboards to monitor information that helps you make the necessary decisions; all the applications and parts related to the interface are connected and integrated into the supervisor chat platform.

The dependent variable, sustainability performance comprises of three sub-constructs. Firstly, the economic performance consists of three items from Çankaya [66]. For reliability purposes, the items assessed the extent to which the manager thinks the post-pandemic will increase sales and economic performance. The items asks whether respondents believe the DT will make it easier to acquire and process information, cheaper to spread disinformation, and more lucrative for enterprises. Secondly, environmental performance comprises of three items altered from Çankaya [66]. The items measured the range to which respondents believed that DT would enhance an organization’s environmental performance in a post pandemic. Thirdly, social performance consists of four items adjusted from Çankaya [66]. Responses were ranked based on participants’ confidence in improving DT through BDA in a post-epidemic. The items reflect the extent to which a respondent agrees with the statements about how DT will help a company improve its social investment projects (such as education, culture and sport), its relationships with stakeholders of the community (such as NGOs and community activists) and the well-being of all its members. Appendix A presents the questionnaire for this study.

5 Results

We applied partial least squares structural equation modeling (PLS-SEM), a causal-predictive method of SEM that places an emphasis on forecasting statistical models of designed structures [69]. According to Hair et al., [70], PLS-SEM is particularly suitable for early-stage theory development and testing and allows the investigation of constructs and relationships in complex structural models. This certainly applies to the relationship between DT and economic, environmental, and social sustainability performance, for which there is very limited research evidence. PLS-SEM does not require a large sample size, works effectively with complex models, and has no assumptions about data distribution [61]. In PLS-SEM, the guideline is that the sample size should be 10 times the number of arrows pointing to a construct [62].This makes PLS-SEM particularly suitable for the present research. The number of arrows pointing to the constructs in the present study is 13, while the sample size in the present study is 304, which is well above the required size. We followed recent guidelines and suggestions given in premier sources of PLS-SEM for data analysis and reporting the subsequent results [68, 69].

5.1 Common method bias assessment

We use inner VIFs to prevent biased methods to determine common method bias (CMB) [70]. After limiting the number of factors to 1, we examined the interpretation of the factor loadings. We performed a full collinearity test to generate a variance inflation factor (VIF) that fits well with the PLS-SEM data analysis technique proposed by Kock [71]. This test means that a VIF value above 3.3 indicates collinearity and therefore the presence of CMB. In our analysis, we found a VIF value of 1.000 (Table 3), which also confirms that CMB does not pose a significant threat to our study.

5.2 Measurement model assessment and factor analysis

Measurement model estimation is the first step in PLS-SEM analysis, which ensures that only constructs with good reliability and validity are used in structural path models. To this end, indicator reliability, internal consistency reliability, convergent validity, and discriminant validity were assessed for indicators of first- and second-order structures. Table 2 presents the measurement model estimation results, including outer loadings, composite reliability, average variance extracted (AVE), and the square root of the AVE value. These results confirm that the measurement model is suitable for structural analysis [68].

Table 2
Item loadings, reliability and convergent validity

Construct Items Loadings AVE CR InnerVIF

Digital transformation DT-1 0.815 0.698 0.874 1.000

DT-2 0.845

DT-3 0.847

Big-data-analytics BDA-1 0.799 0.582 0.893 1.000

BDA-2 0.768

BDA-3 0.735

BDA-4 0.738

BDA-5 0.72

BDA-6 0.811

Economic sustainability performance ECO-1 0.796 0.633 0.838 1.000

ECO-2 0.802

ECO-3 0.788

Environmental sustainability performance ENV-1 0.812 0.680 0.895 1.000

ENV-2 0.863

ENV-3 0.819

ENV-4 0.802

Social sustainability performance SOC-1 0.828 0.617 0.865 1.000

SOC-2 0.835

SOC-3 0.743

SOC-4 0.731

Construct	Items	Loadings	AVE	CR	InnerVIF
Digital transformation	DT-1	0.815	0.698	0.874	1.000
	DT-2	0.845
	DT-3	0.847
Big-data-analytics	BDA-1	0.799	0.582	0.893	1.000
	BDA-2	0.768
	BDA-3	0.735
	BDA-4	0.738
	BDA-5	0.72
	BDA-6	0.811
Economic sustainability performance	ECO-1	0.796	0.633	0.838	1.000
	ECO-2	0.802
	ECO-3	0.788
Environmental sustainability performance	ENV-1	0.812	0.680	0.895	1.000
	ENV-2	0.863
	ENV-3	0.819
	ENV-4	0.802
Social sustainability performance	SOC-1	0.828	0.617	0.865	1.000
	SOC-2	0.835
	SOC-3	0.743
	SOC-4	0.731

Notes: VIF = variance inflation factor, CR = composite reliability, AVE = average variance extracted. Source: Authors’ computation.

5.2.1 Indicator reliability

The reliability of the measurement model indicator is measured by examining item loadings. A measurement model is considered to have satisfactory indicator reliability when each item’s loading estimate is between 0.50 and 0.70 [72]. Based on the analysis, all items in the measurement model show loadings above 0.50, ranging from a lower limit of 0.72 to an upper limit of 0.863. All items have a significance level of 0.001. Table 3 shows the loadings for each item. Therefore, all items used in this study demonstrated satisfactory indicator reliability. Additionally, the authors consider the overall contribution of the formative indicator to the variable, shown by the outer-loadings of the indicators (which were above 0.60), as suggested by Sarstedt et al. [70] and Kholaif and Xiao [14], see Fig. 2. It is therefore assumed that the element is sufficiently reliable.

Fig. 2

Path coefficients and construct items outer-loadings.

5.2.2 Internal consistency reliability

A measurement model has satisfactory internal consistency if the composite reliability (CR) of each construct exceeds the threshold of 0.70. For instance, in exploratory research, reliability levels between 0.60 and 0.70 are regarded as “acceptable,” while values between 0.70 and 0.90 range from “adequate to good.” the composite reliability (CR) is measured; all indicators’ values are larger than 0.7, indicating inter- nal consistency [60]. As in Table 3, the“DT” indicator is very consistent, with a CR of 0.874. The “BDA” signal has a CR of 0.893, which means it is internally consistent. The CR for “economic sustainability performance” has 0.838. The environmental sustainability performance has 0.895. Lastly, the “social sustainability performance” CR equals to 0.86, which represents good levels of internal consistency. These results show that the items used to represent the construct have satisfactory internal consistency reliability.

Table 3
Discriminant validity (Fornell and Larcker)

Big data Analytics Digital transformation Economic performance Environmental performance Social performance

Big data analytics 0.763

Digital transformation 0.311 0.836

Economic performance 0.338 0.357 0.796

Environmental performance 0.341 0.347 0.415 0.825

Social performance –0.075 –0.06 0.014 0.018 0.786

	Big data Analytics	Digital transformation	Economic performance	Environmental performance	Social performance
Big data analytics	0.763
Digital transformation	0.311	0.836
Economic performance	0.338	0.357	0.796
Environmental performance	0.341	0.347	0.415	0.825
Social performance	–0.075	–0.06	0.014	0.018	0.786

Note: The diagonal is the square root of the AVE of the latent variables and indicates the highest in any column or row (Fornell and Larcker 1981). The square roots of the AVE are represented in bold and italics. Source: Authors’ computation.

5.2.3 Convergent validity

We evaluate the convergent validity of each construct measure by evaluating the average variance extracted (AVE) for each latent variable; the AVE is computed by squaring the loading of each indicator on a construct and computing the mean value. A value of 0.50 or higher indicates that the concept explains at least 50% of the variance among its elements [73, 74]. The researchers found that all the AVE values were acceptable and greater than 0.5, as shown in Table 2. The AVE for the DT construct has an AVE value equals 0.698; for the “BDA,” it equals 0.582; as for the “economic sustainability performance” it equals 0.633, while for “environmental sustainability performance” indicator, it has an AVE value equals to 0.626; 0.680, finally, “social sustainability performance” have value equals to 0.617.

5.2.4 Discriminant validity assessment

Discriminant validity is the degree to which measures of different concepts are distinct. The notion is that if two or more concepts are unique, then the valid measure of each should not be highly correlated [75]. According to Fornell and Larcker [73] criterion, discriminant validity is applied when the square root of the AVE of one construct is greater than its correlation with the other construct (Table 3). Therefore, it provides strong support for discriminant validity.

Table 4 shows the cross-loading indicator items. Cross-loadings are useful for assessing whether items belonging to a particular construct load on its parent construct relative to other constructs under study. The results showed that the factor loadings for all items were stronger on the underlying constructs to which they belonged than on the other constructs included in the study [76]. Thus, discriminant validity was achieved based on cross-loading assessment.

Table 4
Cross-loading indicator items

Digital transformation Big data analytics Economic performance Environmental performance Social performance

DT 1 0.815 0.276 0.286 0.233 –0.039

DT 2 0.845 0.263 0.274 0.305 –0.085

DT 3 0.847 0.244 0.333 0.326 –0.027

BDA 1 0.256 0.799 0.296 0.297 –0.068

BDA 2 0.265 0.768 0.267 0.233 –0.06

BDA 3 0.244 0.735 0.248 0.264 –0.041

BDA 4 0.32 0.738 0.229 0.279 –0.077

BDA 5 0.15 0.72 0.236 0.191 –0.011

BDA 6 0.155 0.811 0.265 0.275 –0.075

ECO 1 0.296 0.282 0.796 0.36 –0.032

ECO 2 0.224 0.248 0.802 0.374 0.034

ECO 3 0.32 0.272 0.788 0.267 0.036

ENV 1 0.28 0.279 0.259 0.812 0.048

ENV 2 0.345 0.277 0.411 0.863 –0.046

ENV 3 0.254 0.271 0.344 0.819 –0.007

ENV 4 0.259 0.298 0.35 0.802 0.07

SOC 1 –0.083 –0.067 0.013 –0.017 0.828

SOC 2 –0.051 –0.055 –0.021 –0.022 0.835

SOC 3 –0.016 –0.061 0.043 0.121 0.743

SOC 4 –0.008 –0.05 0.018 0.002 0.731

	Digital transformation	Big data analytics	Economic performance	Environmental performance	Social performance
DT 1	0.815	0.276	0.286	0.233	–0.039
DT 2	0.845	0.263	0.274	0.305	–0.085
DT 3	0.847	0.244	0.333	0.326	–0.027
BDA 1	0.256	0.799	0.296	0.297	–0.068
BDA 2	0.265	0.768	0.267	0.233	–0.06
BDA 3	0.244	0.735	0.248	0.264	–0.041
BDA 4	0.32	0.738	0.229	0.279	–0.077
BDA 5	0.15	0.72	0.236	0.191	–0.011
BDA 6	0.155	0.811	0.265	0.275	–0.075
ECO 1	0.296	0.282	0.796	0.36	–0.032
ECO 2	0.224	0.248	0.802	0.374	0.034
ECO 3	0.32	0.272	0.788	0.267	0.036
ENV 1	0.28	0.279	0.259	0.812	0.048
ENV 2	0.345	0.277	0.411	0.863	–0.046
ENV 3	0.254	0.271	0.344	0.819	–0.007
ENV 4	0.259	0.298	0.35	0.802	0.07
SOC 1	–0.083	–0.067	0.013	–0.017	0.828
SOC 2	–0.051	–0.055	–0.021	–0.022	0.835
SOC 3	–0.016	–0.061	0.043	0.121	0.743
SOC 4	–0.008	–0.05	0.018	0.002	0.731

Source: Authors’ computation.

Additionally, we also determined discriminant validity using the heterotrait-monotrait criterion (HTMT). Previous studies have debated the threshold for HTMT. For example, Kline [77] recommended a threshold of 0.85 or less, while Henseler [78] recommended a threshold of 0.90 or less. Thus, Table 5 shows an HTMT ratio below the desired threshold of 0.90.

Table 5

Discriminant validity (Heterotrait-monotrait (HTMT))

	Big data analytics	Digital transformation	Economic performance	Environmental performance	Social performance
Big data analytics	1
Digital transformation	0.372	1
Economic performance	0.429	0.469	1
Environmental performance	0.396	0.422	0.538	1
Social performance	0.09	0.073	0.066	0.099	1

For conceptually similar constructs: HTMT < 0.90. For conceptually different constructs: HTMT < 0.85 (Hair et al. 2019). Source: Authors’ computation.

Table 6

Fit indices for the one to five segments solutions

	Clusters
	1	2	3	4	5
AIC (Akaike’s information criterion)	3317.087	3170.515	3135.466	3108.674	3094.192
AIC3 (modified AIC with factor 3)	3328.087	3193.515	3170.466	3155.674	3153.192
AIC4 (modified AIC with factor 4)	3339.087	3216.515	3205.466	3202.674	3212.192
BIC (Bayesian information criteria)	3357.975	3256.006	3265.562	3283.374	3313.497
CAIC (consistent AIC)	3368.975	3279.006	3300.562	3330.374	3372.497
HQ (Hannan Quinn criterion)	3333.443	3204.713	3187.508	3178.558	3181.919
MDL5 (minimum description length with factor 5)	3609.524	3781.973	4065.946	4358.176	4662.715
LnL (LogLikelihood)	–1647.544	–1562.257	–1532.733	–1507.34	–1488.1
EN (Entropy statistic (normed))	n/a	0.784	0.802	0.79	0.744
NFI (Non-fuzzy index)	n/a	0.817	0.803	0.764	0.694
NEC (Normalized entropy criterion)	n/a	65.627	60.201	63.954	77.779

a. AIC: Akaike’s Information Criterion. b. AIC3: Modified AIC with Factor 3. c. AIC4: Modified AIC with Factor 4. d. BIC: Bayesian Information Criteria. e. CAIC: Consistent AIC. f. HQ: Hannan Quinn Criterion. g. MDL5: Minimum Description Length with Factor 5. h. LnL: Log Likelihood. i. EN: Entropy Statistic (Normed). j. NFI: Non-Fuzzy Index. k. NEC: Normalized Entropy Criterion.

5.2.5 Assessment of unobserved heterogeneity

This study performs the FIMIX-PLS (Finite Mix Partial Least Squares) process to evaluate unobserved heterogeneity for robustness evaluation [85]. First, we started the procedure assuming a one-segment solution and using the default settings for stopping criterion (10^-10 =1.0E-10), maximum number of iterations (5000), and number of repetitions (10). To determine the maximum number of segments to extract, we first calculated the minimum sample size required to estimate each segment [78]. With a maximum number of 6 arrowheads pointing to any construct in the SUM model construct indicators, and assuming a significance level of 5% and a minimum of 0.25, this study requires 30 observations to reliably estimate the model. The largest integer obtained by dividing the sample size (i.e., 304) by the minimum sample size (i.e., 30) gives a theoretical upper limit of 10. According to Sarstedt et al. [75] and Kholaif et al. [86], since the research model is complicated, this study can extract up to five segments. Consequently, this study reruns FIMIX-PLS on 2-5 segments and uses the same settings as before.

The fit indices for the 1- to 5-segment solutions are shown in Table 1. According to Sarstedt et al. [75] and Kholaif et al. [86], when both the AIC3 and CAIC recommend the same amount of segments, it is fair to presume that the optimal segment number is found. However, our findings reveal that AIC3 denotes a 5-segment solution, but CAIC denotes a 2-segment solution. Besides, Sarstedt et al. [75] stated that AIC4 and Bayesian information criteria (BIC) are crucial for determining the number of segments in FIMIX-PLS. Given the normed entropy statistic (EN) criterion, AIC4 point to a 4-segment solution, however, BIC criteria point to a 5-segment solution. Because indicators like AIC4 and BIC cannot reflect the separation degree of segments, they are not perfect solutions for determining the most appropriate number of segments in FIMIX-PLS. As a result, researchers should take entropy-based measurements like EN. In the case of this study, the EN value points to a 3-segment solution that equals 0.802, which exceeds the threshold value of 0.5, as suggested by Sarstedt et al. [75].

However, as shown in Table 7, a 3-segment solution does not meet each segment’s minimal sample size criteria. Moreover, the minimum description length with factor 5 (MDL5) points to a 1-segment solution. Thus, the analyses do not definitely point to a specific segmentation solution. The reasons lie in the following three aspects. First, AIC3 and CAIC point to different segment numbers. Second, AIC4 and BIC criteria, also, point to different segment numbers. Third, MDL5 points to a 1-segment solution unlike CAIC which points to a 2-segment solution. As a result, there is no evidence for the unobserved heterogeneity existence at a critical level, which supports the study of the complete data set.

Table 7
Relative segment sizes

Number of segments Segment 1 Segment 2 Segment 3 Segment 4 Segment 5

1 1

2 0.748 0.252

3 0.684 0.187 0.129

4 0.616 0.156 0.145 0.083

5 0.48 0.187 0.156 0.142 0.035

Number of segments	Segment 1	Segment 2	Segment 3	Segment 4	Segment 5
1	1
2	0.748	0.252
3	0.684	0.187	0.129
4	0.616	0.156	0.145	0.083
5	0.48	0.187	0.156	0.142	0.035

Note: The table shows the relative segment sizes for each solution in descending (ie, row-by-row) order. Due to sign switching, a given segment may have different signs for different decisions by McLachlan and Peel [85]. SmartPLS 3 software uses decreasing segment sizes when assigning segment numbers to final FIMIX-PLS segments.

5.3 Results and hypothesis testing

5.3.1 Direct relationships effect

Our study uses partial least squares structural equation modeling (PLS-SEM) and aims to provide causal explanations [60]. The bootstrap method estimates the distribution, shape, and bias of a population sampling distribution [79]. The results are summarized in Tables 8 and 9, and shown graphically in Fig. 3.

Table 8
Direct relationships of structural model assessment

Hypothesis β 97.5% CI LL 97.5% CIUL Std error |t-value|^∧ Decision

H1 DT ->BDA 0.321 0.211 0.428 0.055 5.705* Supported

H2 BDA ->Economic performance 0.258 0.153 0.374 0.057 4.444* Supported

H3 BDA ->Environmental performance 0.261 0.146 0.367 0.058 4.469*** Supported

H4 BDA ->Social performance –0.067 –0.192 0.093 0.077 0.81 Not supported

	Hypothesis	β	97.5% CI LL	97.5% CIUL	Std error	\|t-value\|^∧	Decision
H1	DT ->BDA	0.321	0.211	0.428	0.055	5.705***	Supported
H2	BDA ->Economic performance	0.258	0.153	0.374	0.057	4.444***	Supported
H3	BDA ->Environmental performance	0.261	0.146	0.367	0.058	4.469***	Supported
H4	BDA ->Social performance	–0.067	–0.192	0.093	0.077	0.81	Not supported

Note: ***p<0.001. Source: Authors’ computation.

Table 9

Mediation analysis of structural model assessment

Effects	Direct effect			Indirect effect			Total effect		Decision
	β	p-value	β	SD	T- value	p-value	β	p-value
H5 DT ->BDA ->Economic performance	0.282	0	0.083	0.023	3.449	0.001	0.364	0	Supported
H6 DT ->BDA ->Environmental performance	0.274	0	0.084	0.025	3.235	0.001	0.358	0	Supported
H7 DT ->BDA ->Social performance	-0.036	0.668	-0.022	0.026	0.75	0.454	-0.058	0.496	Not supported

Note(s): **p<0.01, *p<0.05. Source: Authors’ computation.

H1 evaluates whether DT has a significant impact on BDA. The findings show that DT significantly impacts BDA (β=0.321, p = 0.001). The value of T-statistic is 5.705, which is higher than the threshold value of 1.96, showing a significant positive correlation. Therefore, H1 was confirmed. H2 evaluates whether BDA has a significant effect economic sustainability performance. The results show that BDA significantly affects economic sustainability performance (β=0.258, p = 0.001). The T-statistic value equals to 4.444. Thus, H2 was supported. H3 evaluates whether BDA has a significant impact on environmental sustainability performance. The results revealed that BDA significantly impacts environmental sustainability performance (β=0.261, p = 0.001). The T-statistic value equals to 4.469. Thus, H3 is maintained. H4 evaluates whether BDA has a significant impact on social sustainability performance. However, the results revealed that BDA has an insignificant effect on social sustainability performance (β=-0.067). The value of the T-statistic is 0.81, lower than the threshold value. Hence, H4 was not supported.

Fig. 3

Results of hypothesis testing, direct and indirect effect results.

5.3.2 Mediating effect

The primary focus of the study was to examine the mediating effect of BDA in the relationship between DT and economic, environmental, and social sustainability performance. Table 7 shows the findings, which indicate:

H5 BDA mediates the relationship between DT and economic performance. The results revealed significant partial mediating role of BDA (β=0.083, t = 3.449, p = 0.001). The total effect of DT on economic was significant (β=0.364, t = 6.442, p = 0.001), with the inclusion of the mediator (BDA) the direct effect was still significant (β=0.282, t = 4.867, p = 0.001). Hence, BDA partially mediates the relationship between DT and economic performance.

H6 BDA mediates the relationship between DT and environmental performance. The results revealed significant partial mediating role of BDA (β=0.084, t = 3.235, p = 0.001). The total effect of DT on environmental performance was significant (β=0.358, t = 6.161, p = 0.001), with the inclusion of the mediator (BDA) the direct effect was still significant (β=0.274, t = 4.605, p = 0.001). Hence, BDA partially mediates the relationship between DT and environmental performance.

H7 BDA mediates the relationship between DT and social performance. The results revealed insignificant mediating role of BDA (β=-0.022, t = 0.75, p = 0.454). The total effect of DT on social performance was insignificant (β=-0.058, t = 0.682, p = 0.496), with the inclusion of the mediator (BDA) the direct effect was still insignificant (β=-0.036, t = 0.429, p = 0.668). Therefore, BDA did not mediate the relationship between DT and social performance.

6 Discussion

The purpose of this research was to test a model to understand whether BDA mediated the association between digital transformation and economic, environmental, and social sustainability performance (economic, environmental, and social), using a sample of 304 Mozambican SMEs managers level employees. In terms of hypothesis testing, H1 proposes that DT positively influences BDA. This hypothesis is supported by our data, consistent with the literature; that businesses can examine their analytics results to identify clues in disorganized data and indirectness across channels [20, 54]. Furthermore, H2 suggests that BDA has a positive impact on economic performance. These results support the existing literature which has shown the significant impact of BDA on improving the economic performance of companies [81, 82]. Since the main objective of the business is to achieve higher economic profit, the adoption of BDA practices and the development of management skills play a crucial role in achieving this fundamental objective [83]. Furthermore, H3 proposed that BDA has a significant effect on environmental performance. Thus supported by our data. In line with Bertelsmann [58] who argues that the post-pandemic pushes companies to promote the adoption of BDA capabilities in unpredictable contexts and that such adoption has a direct impact on economic and environmental outcomes. However, H4 showed that BDA has a negative impact on social performance, which contradicts existing research findings [84].

Furthermore, the inclusion of the mediator in the analysis also allowed us to identify under which conditions the DT could improve sustainable performance. Concerning hypothesis testing, H5 showed that BDA partially mediated the relationship between DT and economic performance. This shows that DT could play a crucial role in improving BDA adoption and lead to better economic performance. Furthermore, H6 indicated that BDA partially mediated the relationship between DT and environmental performance. Thus, small businesses aspiring to expand in the specific industry should therefore adopt and implement BDA concept.

However, H7 indicated that BDA did not mediate the association between DT and social performance. These results are in line with Kim [82] and Aga [18], who discovered that few SMEs in Mozambique had a BDA vision or collaborative model and that big data was frequently disregarded due to technical constraints. The following explanations account for the insignificant impact: First, Alfazema [17] argues that BDA activities have limited impact on working conditions in Mozambique because the small business environment is labor-intensive and the workforce is largely informal. Second, according to Bakar [83], the lack of awareness and knowledge of employees on the implementation of new digital technologies can hinder business operations and essential services. Third, Kholaif et al. [84] and Hamdan et al. [85] attribute this insignificant relationship to the gap between digitization and reality in the area, as a possible cause of friction in BDA adoption in developing countries. This research provides further empirical evidence identifying that BDA is associated with DT, and ultimately improved business economic and environmental performance.

6.1 Theoretical contributions

Digital technologies are changing the way economic and environmental sustainability issues are measured and managed. However, the academic literature lacks understanding of how organizations should adapt to these disruptions and what capabilities are needed to ensure that economic and environmental sustainability are integrated into digital transformation. Hence, this study addresses the need for more comprehensive studies to understand the impact of digital transformation on economic, environmental, and social sustainability performance though BDA. We contribute to the RBV theory by developing a framework that shows how digital transformation impacts economic, environmental, and social sustainability performance in Mozambique. Our results show that DT has a positive effect on economic and environmental performance through BDA. According to Liana Mangifera [47] and Yang [1], improving financial performance is important for small businesses to improve their digitalization capabilities and business development knowledge to meet customer needs and improve performance for the future sustainability of the company. It is about balancing economic growth and profit generation with the impact on the environment and people. Based on the RBV perspective, organizations that focus on DT initiatives that underpin economic, legal, ethical, and discretionary practices will help their employees become more effective [37]. This includes managers’ and employees’ perceived self-efficacy, as an organization’s strategic resources can provide the foundation for developing business capabilities that can lead to superior performance over time. Capabilities are needed to gather, manage and leverage resources to add value to customers and create an advantage over the competition [12]. As demonstrated, future academic research can explore the questions raised by our study on capabilities, organizational performance, digital transformation, and expand our understanding of economic and environmental sustainability. Our study will also help researchers in the field of ecological sustainability connect and collaborate on issues of common interest.

6.2 Practical implications

This study confirms the role of DT adopted by BDA in improving economic and environmental performance. First, organizational efforts to implement digitalization can improve managers’ BDA capabilities through their knowledge-based products and services, while improving economic and environmental performance. According to Zhang [91] and Wang [92], managers of small and medium-sized enterprises should invest significant time and effort in developing a unified strategy for evaluating sustainable economic and environmental performance after the epidemic. BDA will enable organizations to gain a deeper understanding of their customers’ needs and behavior, helping the organization reduce the time to market of its products. This will also allow the organization to refine its products, services and business processes based on the trends identified in the data. Satisfying customers’ needs should make it easier to build customer loyalty [92].

Second, the results of this study complement the work of Lucivero [57] and Lombardi [93], which suggest that managers should use BDA effectively. Lutfi [30] and Lin [12] found that BDA has a positive impact on the economic and environmental performance of the company and is beneficial for the application of DT in SMEs. Supported by Zhang [17] and Mokbel Al Koliby [95], companies should promote the use of innovative technologies because they are beneficial to stakeholders both inside and outside the organization.

Third, investing in BDA means accessing refined and processed data that improves decision-making [5, 10]. BDA implementations often provide users with a consolidated view of data from multiple data sources [30]. Employees have access to data at multiple levels of the organization, based on which decisions can be made. BDA also helps organizations become a learning organization. A learning organization involves the organization’s ability to use analyzed data to transform, adapt and improve the way they function [91].

7 Conclusion

This study examines the mediating effect of BDA on SMEs’ DT and sustainability performance in Mozambique in the post-pandemic world. The results show that DT has a significant impact on BDA. This follows recent findings from innovation research, which suggest that BDA adoption plays a key role in explaining digital transformation, especially in a post-pandemic world [30, 95]. Furthermore, our findings support the idea that big data analytics technology is a key tool in the era of digital transformation, as Lin [3] suggest. Second, we found that BDA has a significant impact on economic and environmental sustainability performance. These findings suggest that BDA could be a key factor in achieving economic and environmental performance. Furthermore, it is important for policymakers to redefine strategies to encourage BDA practices in Mozambique. In terms of long-term growth, strategies related to digital transformation, enabled by big data analysis, motivated by the exploration and evaluation of opportunities, are important. Otherwise, digital transformation may only solve short-term problems but have no effect on long-term economic and environmental performance. However, the impact of BDA on social performance was found insignificant. Moreover, the mediating role of BDA in the relationship between DT and economic and environmental performance was partially significant, while BDA did not mediate the relationship between DT and social performance. This could lead to theoretical and political implications regarding digitalization which, through BDA, influences sustainability performance, especially economic and environmental. In developing countries like Mozambique, the social context could be improved by promoting new technologies such as BDA. These advancements could be fueled by data-driven decision-making, which could lead to a deeper understanding of business operations and, therefore, better performance of organizations. Thus, it is possible to suggest additional elements with policy implications that could be plausible to achieve sustainable economic and environmental performance by promoting digital technologies as a tool for stakeholder engagement and sustainable reporting practices to improve organizational performance. Furthermore, some theoretical questions could be discussed regarding the importance of digitalization as a framework for understanding the determinants and impacts of BDA.

7.1 Policy recommendations

The findings of this study have important policy implications for policy makers and government officials responsible for regulating and supporting small and medium enterprises (SMEs) in Mozambique. The COVID-19 pandemic has highlighted the vulnerabilities and weaknesses of global digitalization and highlighted the importance of adopting sustainable and environmentally friendly business practices. Therefore, policymakers need to develop policies and strategies that encourage SMEs to embrace digital transformation and promote sustainable economic, environmental and social performance.

The study findings show that digital transformation implemented through the BDA’s capabilities highlights the need for government officials to provide SMEs with guidance, support and resources to help them meet the challenges of the pandemic. Furthermore, policymakers should consider offering financial incentives and subsidies to SMEs that invest in new technologies such as BDA capabilities. This technology can help SMEs improve the visibility, transparency and accountability of their supply chain, which can improve their economic and environmental performance.

Additionally, the results show that BDA has a positive impact on a firm’s sustainable economic and environmental performance highlights the importance of promoting sustainable business practices. Policymakers should consider developing regulations and incentives that encourage SMEs to adopt sustainable practices and integrate environmental and social aspects into their business models. Such measures can help create a more sustainable and resilient economy in Mozambique and promote long-term economic growth and stability.

In conclusion, policy makers and government officials should consider the findings of this study when developing policies and strategies to support SMEs in a post-pandemic era. By promoting BDA practices, encouraging the adoption of new technologies and promoting sustainable environmental performance, policymakers can help SMEs become more resilient and better prepared for future crises.

7.2 Limitations and future research

Some limitations regarding the sample size (especially for developing countries like Mozambique) and static analysis. Other model should be tested in other cultural contexts to increase its generalizability. Furthermore, we only examined managers’ opinions about digital transformation towards sustainability, not the facts. Further studies should use objective facts to measure variables, and readers should be cautious when generalizing results to the “real world.” Likewise, the empirical evidence provided in this article opens new avenues regarding the impact of other emerging technologies such as artificial intelligence and blockchain on digital transformation that enables improved economic, environmental and social performance. Finally, our lack of access to longitudinal data prevented the authors from analyzing effects over a longer period. So, all the information comes from one snapshot. This suggests that a long-term longitudinal study can be conducted to investigate the long-term post-pandemic effects on SMEs’ BDA practices and sustainability performance. This can help identify any lasting changes or adjustments to these practices due to the pandemic.

Footnotes

Acknowledgments

The author would like to express gratitude to my supervisor and the editor whose insightful criticism and suggestions significantly improved this paper.

The work was supported by Beijing Social Science Fund 23GLA006.

Authors contributions

CONCEPTION: Luisa Tomas Cumba.

METHODLOGY: Luisa Tomas Cumba.

DATA COLLECTION: Luisa Tomas Cumba.

INTERPRETATION OR ANALYSIS DATA: Luisa Tomas Cumba.

PREPARATION OF MANUSCRIPT: Luisa Tomas Cumba.

REVISION FOR IMPORTANT INTELECTUAL: Luisa Tomas Cumba and Moustafa Mohamed Nazief Haggag Kotb Kholaif.

SUPERVISION: Xiaoxia Huang.

Appendix A

Dear, sir, madam

Name (optional): . . . . . . . . . . . . . . . . . . . . . ..

The researcher aims to conduct the current study: The impact of Digital Transformation on big data analytics and sustainability performance in a post-pandemic period.

Taking into account your experience and professional knowledge in the field of work, the researcher asks you to fill out the attached questionnaire and express your good opinions about the following statements in this questionnaire, hoping to indicate in advance that you understand the content of each statement.

Section 1: Demographic Information

1- Enterprise size

Small []

Medium []

2- Gender

Male []

Female []

3- Position

Senior manager []

Middle manager []

Operation manager []

4- Type of industry

Manufacturing-sector []

Service-sector []

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The impact of digital transformation on big data analytics and firm’s sustainability performance in a post-pandemic era

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1 Introduction

2 Theoretical background and literature review

2.1 Theoretical background

2.2 Literature review

3 Hypotheses development

3.1 The impact of DT on BDA

3.2 The impact of BDA on sustainability performance after the pandemic

3.2.1 Economic sustainability performance

3.2.2 Environmental sustainability performance

3.2.3 Social sustainability performance

4.1 Population, sample and procedures

5 Results

5.1 Common method bias assessment

5.2 Measurement model assessment and factor analysis

5.2.4 Discriminant validity assessment

Table 7 Relative segment sizes Number of segments Segment 1 Segment 2 Segment 3 Segment 4 Segment 5 1 1 2 0.748 0.252 3 0.684 0.187 0.129 4 0.616 0.156 0.145 0.083 5 0.48 0.187 0.156 0.142 0.035

5.3.1 Direct relationships effect

6 Discussion

6.1 Theoretical contributions

6.2 Practical implications

7 Conclusion

7.1 Policy recommendations

7.2 Limitations and future research

Footnotes

Acknowledgments

Authors contributions

Appendix A

References

Table 7
Relative segment sizes

Number of segments Segment 1 Segment 2 Segment 3 Segment 4 Segment 5

1 1

2 0.748 0.252

3 0.684 0.187 0.129

4 0.616 0.156 0.145 0.083

5 0.48 0.187 0.156 0.142 0.035