Integrating Blockchain Technology in Business Models for Sustainable Innovation

Abstract

This research paper investigates the role of blockchain technology in sustainable business model innovation by proposing a comprehensive framework. The study addresses the following research questions: How can blockchain technology enhance business model components? What are the specific innovations enabled by blockchain technology? To answer these questions, a hybrid approach was employed. Initially, a thorough review of existing literature identified key components of business model innovation. Subsequently, a survey was conducted among technology experts to evaluate the applications of blockchain technology in these components. The analysis revealed five categories of innovation: macro-activities of the value network, micro-activities within the organization, governance, financial aspects, as well as sustainable innovation. Next, a novel conceptual model was developed to assess the impact of digital technologies on business model performance, and then it was evaluated using Structural Equation Modeling (SEM). Key findings indicate that blockchain technology significantly enhances data transparency, security, and efficiency capabilities, leading to improved innovation and increased sales volume. Practical implications include recommendations for organizations to prioritize investments in platform technologies, insight analysis, and sensor-based data collection to achieve sustainable business model innovation. The study underscores the importance of a holistic approach to integrating blockchain technology across all business model components to maximize its potential.

1. Introduction

In today’s era, digital technologies such as social media, mobile computing, data analysis, big data, augmented reality, virtual reality, wearable technologies, artificial intelligence (AI), Internet of Things (IoT), cognitive technologies, and blockchain are increasingly being utilized across various industries [1]. The business world is no exception to this trend, and digital technologies are transforming the fundamental dynamics of competition and success. In the digital economy, resources are not the primary determinant of a company’s success. Businesses that fail to leverage the potential of new technologies and innovations risk being quickly outcompeted. Winners and losers in the digital transformation landscape are determined by their ability to utilize these technologies effectively. Some studies indicate that technological innovations provide limited value without the appropriate business models. The design and implementation of innovative business models are crucial for realizing the full potential of technological advancements. The capabilities of digital technologies extend beyond products, business processes, sales channels, and supply chains, impacting the entire business model [2,3,4].

Despite the increasing amount of research on business model innovation, there is still a lack of comprehensive studies on the impact of blockchain technology on business models. Vaska et al. [5] noted the need to consider technological and business aspects simultaneously. A notable shortcoming in this field is the lack of collaboration between business and technology experts, resulting in a language barrier between the two domains. Therefore, bridging this gap and fostering joint ideation is imperative to integrate blockchain technology into business models effectively.

Blockchain technology is a decentralized digital ledger system that securely records transactions across multiple computers, ensuring transparency, immutability, and trust without the need for intermediaries [6,7]. It enables peer-to-peer transactions and the creation of smart contracts, revolutionizing various industries by enhancing security, efficiency, and traceability [8]. Blockchain facilitates the decentralization of markets and collaborative platforms, enabling the use of data computing power and algorithms for various components of AI [9]. It can expand the scope of AI applications and other developments [10]. AI involves the design of devices that can perform intelligent tasks [11], whereas blockchain technology is a decentralized computer network that transparently records and stores data in a secure and immutable ledger system [12]. Integrating AI and blockchain technology can enhance machine learning and grant AI access to financial products. Additionally, blockchain technology enables secure data storage and sharing and offers various benefits to organizations [12]. Blockchain advancements can break the agent connection and enhance information security by revolutionizing supply chains and streamlining processes. AI can improve workplace efficiency by automating routine processes and tasks and boosting productivity. Moreover, AI enables faster decision-making by utilizing the output of cognitive technology and personalizes customer experiences by analyzing vast amounts of data related to past purchases, credit ratings, and preferences [11].

Marketing corporations have increasingly adopted AI because of its velocity and efficiency. AI structures have the ability to engage with customers primarily based on statistics and records, imparting personalized conversation at the proper time without requiring intervention from marketing staff. Additionally, AI guarantees ideal productivity and lets companies correctly manipulate their online presence by using surfing very well and studying internet pages, social media channels, and different logo-related structures.

Sustainability, within the context of business, refers to the ability of businesses to function in a manner that guarantees lengthy-term environmental, social, and economic fitness. Therefore, a business model is conceptualized as a cognitive framework for employers creating, granting, and capturing value. The sustainable business model (SBM) integrates sustainability issues into the mainstream activities of the core business and ensures that the organization’s interests are not limited to financial value, among other environmental and social influences.

A sustainable blockchain-based enterprise model is a business version that leverages blockchain technology to cover sustainability efforts. It uses blockchain technology to enhance traceability, reduce fraud, enhance operational performance, build stakeholder trust, and help in sustainable innovation [13].

This research paper aims to fill this gap by examining the various applications of blockchain technology in validating transaction characteristics at a lower cost and providing a secure authentication mechanism. A sustainable business innovation strategy incorporating AI and blockchain technology is proposed to facilitate secure customer interactions and streamline processes.

To provide evidence, a qualitative analysis was conducted with four primary stakeholders from two different business sectors. The impact of AI and blockchain technology on value creation, business income, and value proposition was evaluated, highlighting the similarities and differences in the effects of digitalization. Furthermore, this study explores how blockchain technology can enhance organizational capabilities and skills by promoting interaction. By addressing these aspects, the study aims to provide a comprehensive understanding of how blockchain technology can drive sustainable business model innovation (SBMI) and offers practical recommendations for its effective implementation.

The rest of the paper is organized as follows. Section 2 provides the literature review. In Section 3 and Section 4, the proposed methodology and results of implementation are provided, respectively. The results are discussed in Section 5, and the paper is concluded in Section 6.

2. Literature Review

The concept of business model has been extensively researched but lacks a standardized definition. Literature on the subject shows varying approaches, such as resource-oriented, activity-oriented, knowledge-oriented, economic, strategy-oriented, and network-oriented approaches [14,15]. Zott et al. [16] identified four main themes in business model conceptualization: (1) a distinct unit of analysis separate from product, industry, and network; (2) a systemic description of organizational operations; (3) the impact of organizational actions; and (4) an explanation of value creation and capture. Furthermore, Clauss [17] outlined three practical applications of the business model based on prior research: classification of companies, measurement of organizational performance, and stimulation of innovation.

Mignon and Bankel [18] explored sustainable business models by analyzing recent empirical studies and various cases where companies have innovated their business models to achieve sustainability. Their research identified four primary sustainable business models, each realized through different business model innovation strategies. Böttcher et al. [19] investigated how startups achieve ecological and economic sustainability by adopting innovative business models incorporating digital technologies. They developed a taxonomy of digital sustainable business models based on an analysis of 31 startups, illustrating how digital technologies can be integral to a business’s value creation across ecological, economic, and technological aspects. Vehmas et al. [20] studied consumer perceptions of sustainable business models and their willingness to pay for sustainable products. The study found that while Finnish consumers value sustainability and seek information to guide their purchasing decisions, a significant obstacle is the lack of clear and accessible data. The research emphasized the need for transparency, effective communication of sustainability initiatives, and competitive pricing. Dwivedi et al. [21] analyzed the challenges faced by the manufacturing industry in adopting blockchain technology. Their research identified three primary obstacles: insufficient funding for Product Recovery Systems, a lack of governance and standards, and security issues related to blockchain implementation.

Moosavi et al. [22] reviewed the application of blockchain technology within supply chains, highlighting that there is significant potential for future research on real-world supply chain implementations of blockchain. Datta et al. [23] addressed the issue of blockchain bottlenecks by developing a mathematical model to optimize operational costs. Their study explored whether adopting blockchain technology could be a more practical solution for these bottlenecks than adding an assembly line to existing manufacturing infrastructure, ultimately aiming to enhance production efficiency. Adama et al. [24] conducted a systematic analysis to assess how digital transformation influences business model innovation, highlighting the imperative for organizations to continuously adapt and evolve in response to shifting market dynamics and evolving consumer preferences. Kılıç and Atilla [25] delved into practical examples of companies that have embraced Industry 4.0 technologies, thoroughly analyzing their specific practices and processes. Patra and Lenka [26] explored various dimensions of sustainability in business, identifying key terms and prioritizing essential practices. Their findings indicated that factors such as firm size, leadership, uncertainty, gender, geographic location, education, and tourism significantly impact the sustainable business practices of entrepreneurial firms.

Reviewing the research literature shows that sustainable business model innovation has been studied in aspects such as process orientation, model components, level of innovation occurrence, model design theme, and effective factors. However, the ability of blockchain technology to model innovation has been addressed less. In other words, comprehensive and sufficient studies regarding the impact of blockchain technology in the direction of business model innovation have not been carried out, so the present research was conducted to present the framework of SBMI based on the application of blockchain technology.

The researcher has adopted the activity system approach for several compelling reasons. Primarily, this approach stands as an established framework deeply rooted in value chain analysis, a resource-oriented perspective, strategic networks, and economic exchange costs. Its incorporation of external resources renders it a practical method for explicating competitive advantage. Moreover, it resonates with the mental models of both managers and entrepreneurs, concentrating on organizational activities and facilitating decision-making across various business model concerns. Additionally, the activity system approach fosters systematic and holistic thinking in business model design, eschewing isolated choices and advocating for a comprehensive understanding of organizational dynamics.

This research aims to provide a comprehensive framework for sustainable business model innovation through the application of blockchain technology. To achieve this, an extensive literature review is conducted, proposing a conceptual model that integrates business, technological, and SBMI perspectives, considering the role of blockchain technology.

Furthermore, this research seeks to contribute to the international body of knowledge by addressing gaps in current literature and offering novel insights. To ensure the novelty and originality of the research, the latest studies in the field, especially those within the past two years, are thoroughly reviewed. This approach ensures that the manuscript significantly contributes to advancing understanding in the intersection of blockchain technology and SBMI, thereby enriching the existing literature with fresh perspectives and insights.

3. Research Methodology

In recent years, researchers have shown an increased interest in business model innovation. Consequently, there is now a wealth of knowledge available on the subject. To accomplish this, the preferred approach is conducting a systematic literature review and synthesizing previous studies’ findings. This involves meta-synthesis, which is the process of combining data interpretation and key findings from selected studies. This study follows a six-step process based on Sandelowski and Barroso’s [27] model, which is illustrated in Figure 1. Moreover, the proposed conceptual model is presented in Figure 2.

This research begins by exploring the potential for blockchain technology to revolutionize business models. To achieve this, we designed a questionnaire for blockchain experts to gather their opinions on applying different types of technology within the components identified in the first phase of our research. Our Likert scale comprised seven points, ranging from “no use” to “absolutely necessary”, to elicit nuanced responses. Our sample consisted of blockchain experts with at least three years of relevant education and work experience, selected through non-random targeted sampling. The sample size was determined by factors such as access, expertise, and willingness to participate. We distributed the questionnaire via a Google Form to experts in digital technology and collected demographic information, which is presented in

Table 1. Demographic information of experts.

Specialization or Occupation	Gender	Age	Work Experience (years)
Research fellow	Female	36	7
Master of Industrial Engineering	Man	38	6
Ph.D. in Information Technology	Man	32	4
Faculty member—Information Technology Management	Man	38	8
Ph.D. in Information Technology	Female	36	5
Research fellow	Man	36	4
Faculty member—Information Technology Management	Man	37	11
Ph.D. in Information Technology	Man	35	10
Master of Information Technology	Man	32	8
Ph.D. in Information Technology	Female	36	7

.

The questionnaires were analyzed using the fuzzy analysis method, including three stages of fuzzification: fuzzy average and de-fuzzification. In the phase of fuzzification, the responses based on the experts’ Likert scale were converted into fuzzy numbers according to

Table 2. Conversion of Likert spectrum to fuzzy numbers.

Item	Linguistic Variable	Triangular Fuzzy Number
1	Useless	(1,0,0)
2	Very little use	(3,1,0)
3	Little use	(5,3,1)
4	Medium use	(7,5,3)
5	Many uses	(9,5,7)
6	Much use	(7,9,10)
7	Absolutely necessary	(10,10,9)

.

During the fuzzy averaging phase, the average of triangular fuzzy numbers was calculated for each question. De-fuzzification, which involves converting a fuzzy number into a regular number, was carried out using the Makowski method [28] presented in Equation (1), as employed in this research. The resulting number from the de-fuzzification stage indicates the degree to which each blockchain technology category is applied in business model innovation.

$$X=m+{\displaystyle \frac{u-l}{4}}$$

(1)

In this research, content validity is used to ensure that the questionnaire components accurately measure the researcher’s intended criteria. Subject matter experts were consulted to determine the content validity of the instrument. The questionnaire was based on dimensions of SBMI extracted from past studies using the meta-combination and classification method of blockchain technology and was previously used by researchers like Broccardo et al. [29], thereby establishing a high level of validity. Moreover, reliability refers to the reproducibility of providing consistent results under the same conditions using Cronbach’s alpha test.

Cronbach’s alpha is a measure of internal consistency, which indicates how closely related a set of items are as a group. It is a commonly used reliability coefficient for assessing the reliability of a scale or test. Cronbach’s alpha ranges from 0 to 1, with higher values indicating greater internal consistency. A value above 0.7 is generally considered acceptable for social science research, although this threshold can vary depending on the field of study.

The formula for Cronbach’s alpha is given by Equation (2):

$$\alpha ={\displaystyle \frac{N}{N-1}}(1-{\displaystyle \frac{\sum _{i=1}^N{\sigma }_i^2}{{\sigma }_T^2}})$$

(2)

where $N$ is the number of items (questions) on the test, ${\sigma }_i^2$ is the variance of the ith item, and ${\sigma }_T^2$is the variance of the total score formed by summing all items.

SEM Approach

Structural Equation Modeling is employed to test the hypothesized relationships among the constructs. SEM allows for examining complex relationships between observed and latent variables, providing a comprehensive understanding of the model’s structure and the underlying theoretical framework. The steps in SEM analysis are as follows:

• Model Specification:

The hypothesized model is specified based on theoretical foundations. It includes defining the relationships between exogenous (independent) and endogenous (dependent) constructs;

• Path Diagram:

A path diagram is constructed to represent the hypothesized relationships visually. The diagram includes the constructs as nodes and the hypothesized relationships as directed arrows. The path coefficients represent the strength and direction of the relationships between constructs;

• Model Estimation:

The model is estimated using maximum likelihood estimation (MLE). It involves calculating the path coefficients, standard errors, and fit indices to assess the model’s adequacy;

• Model Evaluation:

The model fit is evaluated using various fit indices, such as Chi-square (χ²), Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), and Root Mean Square Error of Approximation (RMSEA). Acceptable thresholds for these indices are typically χ²/df < 3, CFI > 0.90, TLI > 0.90, and RMSEA < 0.08;

• Interpretation of Path Coefficients:

The standardized path coefficients are interpreted to understand the strength and significance of the relationships between constructs. These coefficients range from −1 to 1, indicating the direction and magnitude of the effect.

4. Research Findings

The results of this study are divided into different phases, as shown in Figure 1. The following section will discuss the findings of each phase.

Firstly, the texts were systematically reviewed, with relevant articles identified through a literature search of the WOS and Scopus databases until 2022. The search terms included ‘SBMI’, ‘Evolution Business Model’, ‘Revolution Business Model’, ‘Transformation of Business Model’, and ‘Business Model Disruption’.

In the next step, 56 unrelated documents were removed from the 500 articles identified by checking the titles. Then, 155 more documents were removed after studying the abstracts for thematic relevance. The remaining articles were evaluated based on ten criteria, including research objectives, method logic, research design, sampling, data collection, reflectivity, ethical considerations, accuracy in analysis, clear expression of findings, and research value. Articles with an average score of less than 2.5 were removed, resulting in 42 articles for the next phase.

In the third step, the selected articles were analyzed and categorized into three groups: content, structure, and governance, with any unrelated codes being placed in a separate category. The fourth step involved analyzing and combining qualitative findings with codes of a similar nature grouped into themes. The codes were divided into four categories: content, structure, governance, and others, with the fourth category relating to financial aspects of organizational activities. The themes in the content category were divided into two sub-categories: intra-organizational activities and macro-activities of the value network. The findings are presented in

Table 3. Identified categories and themes.

Category	Explanation	Theme	Repetition
Macro-activities	New resources, resource allocation innovation, acquiring complementary assets, identifying and leveraging unique assets	Key resources and assets	19
	Key new capabilities, innovation in skills and competencies	Key competencies	11
	Attention to trends and drivers, innovation in strategy, and new directions	Strategic orientation	6
	Improving workflows, innovation in organizational structure, human resource management, training, development, and culture management	Internal systems alignment	9
Micro-activities	Innovation in products and services, software as a service, platform development, providing social and environmental advantages, identifying values that can be offered to customers	Discover the value	30
	Innovation in the manufacturing process, improvement of operational efficiency, management of input resources, new tools and equipment	Creating value	8
	New offering, offering customer value, offering vertical and horizontal goods and services, providing products, services and information in a combined form, product portfolio	Value proposition	30
	Marketing and sales innovation and distribution channel, customer behavior, direct and reverse logistics, customer journey management, customer experience management, communication with customers, and changing the quantity and quality of communication	Deliver value	80
	Research and development, innovation based on value feedback	Value development	4
	Data collection, processing and analysis, knowledge sharing, organizational learning, information access, information flow security, data structuring, real-time data	Data and knowledge management	11
Sustainable innovation	Cost-effectiveness through resource efficiency, revenue generation aligned with sustainable practices, investment in green technologies, enhanced competitiveness through sustainable innovation	Economic viability	16
	Reduction in carbon footprint and greenhouse gas emissions, conservation of natural resources, implementation of eco-friendly waste management solutions, promotion of biodiversity conservation efforts	Environmental impact	9
	Fair wages and equitable employment opportunities, community engagement initiatives, access to essential services, diversity and inclusion in organizational policies	Social equity	11
Innovation in governance	New partners, engaging stakeholders, suppliers, reverse logistics partners, identifying new players, key stakeholders, stakeholder needs assessment	Identification of beneficiaries	16
	Innovation in the role of ecosystem members, changing the position in the value network, rearranging the role of partners, innovation in ecosystem relationships, involving third parties, internal value chain, external value chain, value exchange between different stakeholders	Stakeholder role management	26
	Improving the effectiveness of the business network, simplifying relationships between partners, integrating the demand side, integrating the supply side, vertical, horizontal, and multidimensional value networks, reducing intermediaries, innovation, and improving communication between stakeholders	Improving the performance of the stakeholder network	10
	Motivating, building trust, strengthening collaboration and partnership between different stakeholders, and engaging customers	Collaborative relationships with stakeholders	7
	Change in governance mechanisms, innovation in control processes, decentralization of the ecosystem, increasing the organization’s autonomy	Stakeholder network control	4
Financial innovation	Fixed and variable costs, economic efficiency due to scope and scale, minimization and cost management	Cost structure	21
	Pricing strategy innovation, new revenue stream, profit formula, liquidity management, financial architecture innovation, revenue diversification	Revenue model	57

.

Table 3 presents the categories and themes identified from the analysis of the selected articles. These themes are grouped into four main categories: content, structure, governance, and finance. In Table 3, “repetition” indicates the frequency of each theme’s occurrence across the articles and indicates its importance in the context of SBMI. The categories identified in this study are consistent with previous research on SBMI, such as Trischler et al. [30] and Teece [31].

After performing the fuzzy analysis of the questionnaires, the application rate of each technology group was calculated for each of the nineteen SBMI components. The output of the second stage shows the technology groups that can be used for innovation in each business model component. For example, for the value creation component, the technologies of insight analysis, platform, connection, and role-oriented products are prioritized, respectively.

Table 4. Applicability of technologies for business model innovation.

SBMI Components	Platform	Connection	Inscription Axis	Axis Sensor	Insightful	Analytical Interaction	Added Interaction
Key resources and assets	6.5	6.6	5.4	7.6	7.7	5.5	6.2
Key competencies	5.8	6.4	5.4	8.5	9.8	4.4	5.3
Strategic orientation	6.3	6.7	6.2	7.9	9.7	4.3	5.5
Alignment of systems	6.4	6.	6.3	8.4	7.8	6.5	4.3
Discover the value	8.6	4.2	7.9	8.0	10.0	8.8	5.7
Creating value	9.1	7.9	7.6	4.6	9.4	4.2	6.5
Value proposition	7.9	4.3	4.8	6.3	9.8	7.3	6.3
Deliver value	8.4	4.0	6.5	5.6	8.3	4.0	7.7
Value development	8.6	2.9	4.8	5.5	8.2	6.2	4.8
Data and knowledge management	8.1	3.6	4.5	4.9	6.8	4.6	7.5
Economic viability	6.3	6.6	6.4	7.3	6.9	4.9	6.3
Environmental impact	5.2	3.6	5.1	6.2	7.8	6.4	5.7
Social equity	7.9	7.3	6.2	7.9	8.2	7.3	6.5
Identification of beneficiaries	7.1	7.9	6.4	6.5	8.4	6	5.7
Stakeholder role management	7.0	8.1	5.4	4.8	7.4	4.4	4.6
Improve stakeholder performance	8.8	6.2	6.1	7.9	8.8	5.3	5.7
Stakeholder network control	8.4	7.2	6.9	8.1	9.4	6.3	5.0
Collaborative relations of stakeholders	8.6	7.5	6.1	6.3	9.4	5.1	5.3
Cost structure	6.7	6.8	5.3	6.4	5	6.2	5.7
Revenue model	8.0	6.6	5.4	6.8	6.0	5.8	5.8
Number of choices per group	18	12	3	16	18	7	2

shows the average calculated for each technology.

Table 4 provides an overview of the applicability of various technologies for SBMI. This Table showcases the average calculated for each technology group across different components of SBMI. In this regard, technologies like platform, connection, and insightful analysis exhibit varying applicability for enhancing essential resources and assets. This analysis aids in prioritizing technology groups that can effectively drive innovation in different aspects of the business model, guiding organizations towards strategic technological investments for innovation and growth.

Statistical Analysis

This research employed SEM to examine hypothesized relationships between observed and latent variables. SEM’s strength lies in its ability to simultaneously analyze multiple regression equations while accounting for directly measured variables and underlying constructs. The two-stage model-building approach ensured proper assessment of measurement indicators followed by evaluation of the hypothesized structural relationships. Rigorous fit indices confirmed the model’s adequacy before interpreting the significance of the estimated coefficients and testing the research hypotheses.

In order to assess the hypotheses presented, a conceptual model must be created and analyzed using path analysis. The model was fitted using the maximum likelihood method, and the variance matrix of the data was incorporated as input. To ensure the normality of the data distribution, the skewness and elongation values of the variables were checked for one-variable normality. The findings of this assessment are displayed in

Table 5. Mean, standard deviation, and Cronbach’s alpha of the variables.

Factor	Average	Standard Deviation	Cronbach’s Alpha
Customer knowledge (F1)	3.79	0.68	0.788
Knowledge of the customer (F2)	3.85	0.63	0.811
Knowledge for the customer (F3)	3.46	0.69	0.836
Customized products for customers (F4)	3.56	0.71	0.799
Making better purchase decisions (F5)	3.39	0.73	0.789
Creating a better consumption experience (F6)	3.61	0.75	0.768
Customer success (F7)	3.58	0.62	0.801
Increase in sales volume (F8)	3.49	0.64	0.823
Creating value (F9)	3.35	0.68	0.832

and Figure 3.

According to the results in Table 5 and Figure 3, the reliability of the scales is confirmed at an acceptable level. Moreover, experts have used the validity of the research. In Figure 4 and Figure 5, the standard path and significance coefficients are shown.

The data in Figure 4 and Figure 5 confirm the significance of the measurement model’s correlation. It is worth noting that except for y9 and y14, all the parameters show a significant relationship (less than 0.01) with the relevant variables in the index. In

Table 6. Path coefficients and significance test results.

Hypothesis	Relationships of Symbols of Variables	p-Value	T-Value	Coefficients	Result
H1	F1->F4	<0.05	5.58	0.68	confirmation
H2	F2->F5	<0.05	4.73	0.72	confirmation
H3	F2->F6	<0.05	3.08	0.67	confirmation
H4	F3->F5	<0.05	2.95	0.55	confirmation
H5	F3->F6	<0.05	3.54	0.61	confirmation
H6	F4->F7	<0.05	3.25	0.58	confirmation
H7	F4->F8	<0.05	2.85	0.55	confirmation
H8	F5->F7	<0.05	2.77	0.64	confirmation
H9	F5->F8	<0.05	4.35	0.65	confirmation
H10	F6->F7	<0.05	3.33	0.86	confirmation
H11	F6->F8	<0.05	3.25	0.88	confirmation
H12	F7->F9	<0.05	3.13	0.83	confirmation
H13	F8->F9	<0.05	3.25	0.85	confirmation

, hypothesis testing based on T-statistics and path coefficients has been examined.

The results in Table 6 provide insights into the relationships between the variables in the conceptual model. The significant path coefficients and p-values indicate that the relationships between these variables are statistically significant and contribute to the overall understanding of SBMI.

5. Discussion

The main aim of this research was to introduce a sustainable business model innovation framework using blockchain technology. It was achieved through a two-stage process. In the beginning, an analysis of prior qualitative research was used to identify various SBMI components. This was then followed by integrating technological applications into SBMI and obtaining technology professionals’ opinions on this concept through questionnaires. Seven technologies that can be applied to innovation in each component were identified. The study outlined how blockchain technology can be exploited for innovation within each element in a given pattern.

Previous studies have examined similar methodologies. For example, Blaschke et al. [32] compared elements of digitalization to those found in a business landscape and assessed different ways digital value creation could occur. Snihur et al. [33] also analyzed how specific macro-level trends affected business models within the automotive industry, while Müller and Hundahl [34] conducted a systematic review of communication and information technologies-driven business model innovation development in 35 articles focusing on relationships between nine business landscape elements. Unlike previous works on similar lines, we specifically focus on integrating blockchain technology into SBMI.

Moreover, prior research on the business model used the activity system approach to define what it entails [30,31]. Our work resonates with these conclusions, especially regarding financial innovations as an essential class of SBMI. It is in line with Sjödin et al.’s argument [35] that value creation activities must be compatible with value capture; that is, the financial aspects of a model are crucial for innovation effectiveness. Such present models include crowdfunders, freemium models, collective purchasing, and joint ownership [36,37,38].

This study also highlights those businesses as part of a relational ecosystem containing suppliers, marketing partners, technology providers, and research organizations. Thus, the term ecosystem is critical since firms can outsource specific elements within their value chains to other participants based on their competencies. This method facilitates innovation and supports previous studies which found that collaboration among firms promotes business development.

Based on the analysis of questionnaires, three groups of blockchain technology (platform technologies, insight analysis technologies, and sensor-based data collection technologies) are most frequently applied for SBMI. Platform technologies act as a means to exchange information between various actors, with the possibility of communicating with all ecosystem members and thus increasing their added value. Insight analysis technologies facilitate knowledge generation and data-driven choices crucial for value creation in today’s digital age. Sensor-based data collection technologies which transfer information over a network without human involvement are needed to realize the concept of an intelligent world. These three digital technology clusters have the potential to enhance business value significantly.

However, we chose fewer parts since they thought these components were less valuable. Therefore, role-oriented product technology and extra interaction were ranked less critical. Thus, it is recommended that these groups require minimal concentration and investment.

6. Conclusions

This study contributes to understanding how blockchain technology can eventually help SBMI. Several key components of blockchain technologies have been identified, and their applicability groups them. Therefore, companies should focus on developing data analytics, insight analysis platforms, and sensor-based data collection technologies for further advanced business models.

6.1. Key Findings

This work has integrated blockchain technology into SBMI through a holistic approach encompassing monetary improvements, relationship building with the environment, and governance mechanisms. The key findings to be drawn from this research can then be expressed in the form of three critical components for an effective SBMI: technology of the platform, information gathering at the sensors, and analysis of insight.

6.2. Policy Implications

The findings of this study suggest several key policy implications for integrating blockchain technology into sustainable business models. Policymakers should develop supportive frameworks that incentivize financial innovation and foster ecosystem collaboration, such as through tax incentives, grants, or subsidies for blockchain-based sustainability initiatives. Governance mechanisms must be established to enhance transparency, data security, and trust, addressing risks associated with blockchain’s decentralized nature. In addition, investment in the development of critical technologies, such as platforms, insight analysis, and sensor-based data collection, is essential, along with training and public–private partnerships, to accelerate innovation and adoption.

6.3. Study Limitations

An important limitation of this research is the lack of collaboration among commercial enterprises, resulting in a language barrier between the two domain names. To address this, fostering joint ideation and collaboration between those professionals is very important. However, it must be recounted that the cutting-edge approach did not overcome this problem because the outcomes depended on the views and critiques of only 10 professionals, often from the information technology zone. The average ratings in Table 6, which hover around 4 with a well-known deviation of less than 1, propose that these opinions are impartial and may not offer the intensity of perception required to bridge the space successfully. Thus, destiny research should encompass a more significant and extensive organization of specialists from enterprise and generation fields to ensure a more balanced and comprehensive analysis.

6.4. Recommendations for Practitioners

• Strengthen data infrastructure: Organizations should invest in robust real-time data collection platforms to support data-driven decision-making;
• Adopt a holistic approach: Avoid focusing solely on a few components of SBMI; instead, continuously monitor and invest in promising areas that align with environmental trends and technological advancements;
• Engage in ecosystems: Develop roles within suitable ecosystems to gain experience and drive innovation, balancing cooperative and competitive relationships periodically to maintain a dynamic portfolio;
• Develop governance mechanisms: Foster trust and commitment within ecosystems by establishing fair and transparent governance rules to regulate complex relationships;
• Create a road map: Develop a strategic road map for digital transformation that aligns with the organization’s goals and removes obstacles to proper design.

By addressing these recommendations and furthering research in these areas, organizations can better navigate the complexities of SBMI using blockchain technology.

6.5. Future Research Directions

Future research should delve deeper into the sector-specific challenges and opportunities in implementing the proposed SBMI framework, with particular attention to private, public, and non-profit organizations. Additionally, longitudinal studies are needed to capture the evolving nature of business model innovation, focusing on critical phases of transformation over time. Researchers should also investigate how cultural dynamics, human resource practices, and industry-specific characteristics influence the success of SBMI and digital transformation efforts. Specifically, examining the role of leadership, organizational culture, and regulatory environments in different sectors could offer valuable insights into optimizing SBMI initiatives. Finally, the exploration of emerging technologies beyond blockchain, such as AI and IoT, and their integration into sustainable business models could further enhance the understanding of the digital transformation’s impact on sustainability.