Ecosystem-driven business opportunity identification method and web-based tool with a case study of the electric vehicle home charging energy ecosystem in Denmark

Understanding the local needs and challenges is critical for technology adoption in the energy sector. However, it is still a big challenge for most ecosystem stakeholders. Furthermore, technology adoption theories have mainly focused on the technology itself, and the business ecosystem perspective has been neglected. Therefore, this paper proposes an ecosystem-driven business opportunity identification method, a systematic approach for ecosystem stakeholders to conduct business opportunity analysis and evaluation based on the CSTEP ecosystem analysis and evaluation method. This method includes four correlated steps: Step 1: Identify the five CSTEP dimensions of the business ecosystem; Step 2: Identify potential changes in the business ecosystem; Step 3: Identify future ecosystem trends and timeline; Step 4: Select business opportunities; and Step 5: Potential solution identification. A web-based tool called opportunity identifier is developed for implementing the proposed method. A case study of the electric vehicle (EV) home charging energy ecosystem in Denmark is applied and demonstrates the application of the proposed method and the implementation of the developed web-based tool. Three value propositions are identified in the case study: (1) EV users can have optimal EV charging cost and optimal CO2 emission consumption with the intelligent EV charging algorithms that consider electricity prices, tariffs, and CO2 emission; (2) DSOs can avoid grid overloads and postpone the grid upgrade by applying intelligent EV charging algorithms; (3) Independent aggregators can aggregate EVs and participate in the ancillary service market or provide Vehicle-to-Grid services by using intelligent EV charging algorithms. Moreover, three feasible decentralized EV charging strategies (Real Time Pricing, Time-of-Use Pricing, and Timed charging) are identified as the potential solutions targeting the first value proposition.


Introduction
Undoubtedly, understanding the local needs and challenges is the first and most crucial stage for the success of any implementation, especially in the energy sector. Business opportunities come from needs and challenges in the existing markets and trends towards the transition to future markets. Companies need to capture opportunities and predict when the market will have the needs. However, although companies realize the importance of the above matters and try to improve the situation, it is still a big challenge for most companies.
In recent years, ecosystem thinking has been popularly used for investigating complex systems from a business perspective. The use of 'ecosystem' in business has started since the term 'business ecosystem' was introduced in 1993 (Moore 1993) to describe how the economic community works. Without ecosystem thinking, companies mainly focus on developing customer insight, building core competencies, and beating the competition. In the business ecosystem domain, the evolution/ co-evolution perspective is rarely discussed, although there are discussions in, e.g., system thinking (Rubenstein-Montano et al. 2001); furthermore, there is no systematic approach for investigating unmet needs and megatrends in a given business ecosystem.
Technology and innovation adoption has been well discussed in the literature, and several popular technology adoption models are proposed, e.g., Rogers' adoption curve (Rogers 2003). The technology adoption theories try to understand the adoption behaviors toward new technologies, especially behaviors and constructs during the decision process. However, technology adoption theories have mainly focused on the technology itself, the adoption process and influential factors for decision making, and the business ecosystem perspective has been neglected.
Some theories in strategy management, such as ETPS (Economic, technical, political, and social;Aguilar 1967), STEP (Social, technical, economic, political;Brown and Weiner 1984), and STEPE (Social, technical, economic, political, and ecological;Davenport and Prusak 1997), intend to investigate the impact factors in business and strategies. However, the main focus is personal or organizational. Therefore, this paper proposes a method for identifying business opportunities based on the theories of business ecosystem modelling (Ma 2019), ecosystem architecture design , and CSTEP-the five business ecosystems dimensions (Ma 2022). Furthermore, the proposed method is implemented as a web-based tool (called 'business opportunity identifier') to be applied in research and teaching.
A case study of the electric vehicle (EV) home charging energy ecosystem in Denmark is chosen to demonstrate the application of the method with a complex ecosystem impacted by all the five CSTEP business ecosystem dimensions. The electric vehicle home charging energy ecosystem is chosen because there potentials for EVs to provide energy flexibility due to their larger energy consumption compared to other home appliances (Ma et al. 2018a;Howard et al. 2020) and the potential flexibility due to intelligent EV charging algorithms (Billanes et al. 2017(Billanes et al. , 2018. However, EV home charging usually involve multiple stakeholders from both energy and EV ecosystems which potentially causes high uncertainty (Ma et al. 2015(Ma et al. , 2017a.

Business ecosystem theory
The term of ecology was introduced by Haeckel in 1866 as the science of relations between organisms and the surrounding outer world (Haeckel 1866). Accordingly, based on ecology and observations of how biological organisms function, the ecosystem considers nature, society and business as integrated from a system's perspective (Capra and Luisi 2014). In general, an ecosystem is a system with thousands of organisms that live in a constant relationship with their environment, the members benefit from each other's participation through symbiotic relationships, and relationships also develop among them (Maracine and Scarlat 2008).
Business ecosystems are analogous to biological ecosystems. In 1993, Moore (1993) uses biological metaphors and introduces the business ecosystem concept. Moore describes how the economic community works and highlights the interaction between companies and their business environment. Moore (1996) divides the business ecosystem into four stages for analysis and management (the definition is shown in Table 1). These four stages represent the business ecosystem life cycle.
Following Moore's definition, (Iansiti and Levien 2002) describes the business ecosystem as a large number of loosely interconnected participants who depend on each other for mutual effectiveness and survival. Iansiti and Levien (2004) introduces a framework for studying and understanding innovation and operations management in business ecosystems. They define specific indicators of ecosystem structure and develop specific operational implications for different types of ecosystem roles and corresponding strategies as dominator, keystone, and niche firms (the definitions are shown in Table 2; Levien 2004).  Moore (1996)

Stage Definition
Pioneering (Vision) When the basic paradigm of the ecosystem is being worked out Expansion (with the goal of market domination) When the community broadens its scope and consumes resources of all types Authority (and the inevitable challenges to authority When the community architecture becomes stable and competition for leadership and profits within the ecosystem gets brutal Renewal (or death) When continuing innovation must take place for the community to survive or die Keystone specie Is simply a species that governs the most important ecosystem health through specific behaviours or features that have effects that propagate through the entire system, often without being a significant portion of the ecosystem itself. Removal of biological keystones can have dramatic cascading effects through the entire ecosystem Levien (2004) Dominator Integrates vertically or horizontally to own and manage a large part of its network directly and seizes a greater part of the value Iansiti and Levien (2004) Niche players Develop specialized capabilities to add value to a business ecosystem. Niche species individually do not have broad-reaching impacts on other species in the ecosystem, but collectively they constitute the bulk of the ecosystem both in terms of total mass as well as a variety Levien (2004) Among many definitions in the literature, there are three key phases in the business ecosystem defined as the community of interdependent organizations, business environment (opportunity space), platform and co-evolution (the definitions are shown in Table 3; Rong and Shi 2015).
Other ecosystem analogies used regularly in academic research and business practice have been discussed as the customer ecosystem that focuses on the customer views of the business ecosystem, e.g., (Ma et al. 2017b;;Manning et al. 2002), the organizational ecosystem that emphasizes the aspect of human organizational structures (Mars et al. 2012), and product ecosystem that denotes "the consideration of multiple related products in a coherent process, compared with the conventional viewpoint of static, isolated products" (Zhou et al. 2011).
Although a large amount of literature has discussed and analyzed the business ecosystem structure, no systematic approach has been proposed. Therefore, (Ma 2019) proposes a framework for business ecosystem modeling based on the combined theories from system engineering, ecology, and business ecosystem. This framework includes three parts of business ecosystem architecture development: factor analysis, ecosystem simulation, and reconfiguration. Based on the work by Ma (2019), a methodology for business ecosystem architecture design with the business ecosystem ontology is introduced by Ma et al. (2021). Several business ecosystem architecture terms are defined in Ma et al. (2021). This methodology has been popularly applied in the energy field. For instance, (Ma et al. 2019a) applies the method to investigate microgrid solutions for reliable power supply in India's power system, and (Hack et al. 2021) investigate the digitalization potentials in the electricity ecosystem in Germany and Denmark. Table 3 Three key phases in the business ecosystem (Rong and Shi 2015)

Phase Definition
Community of interdependent organization It means the relationship among network partners is dependent on one another and share in a common fate Business environment It can be treated as an opportunity space where interdependent organizations share their ideas and visions for future development. It means that organizations in a business ecosystem should expand their views beyond the supply-chain partners of their core business. The business environment includes other non-direct business partners who shape the industry greatly and the business environment Co-evolution It means that interdependent organizations will co-evolve with one another in the dynamic business environment. Co-evolution highlights the importance of key firms' interactions with their business environment as well as with core business partners

Business ecosystem dimensions
In the framework for studying and understanding the management of innovation and operations in business ecosystems proposed by Iansiti and Levien (2004), the indicators of the ecosystem structure 'health' is defined with three dimensions: • Robustness: a business ecosystem's capability of facing and surviving perturbations and disruptions. • Productivity: how effectively does the ecosystem convert raw materials into living organisms. • Niche creation: the ecosystem's capacity to create new valuable niches. It refers to the capacity to increase meaningful diversity over time by creating new valuable functions.
The measures for the three dimensions are also proposed by Iansiti and Levien (2002) as shown in Table 5.
However, the three dimensions proposed by Iansiti and Levien (2002) only focus on the business aspect of a business ecosystem and do not cover all aspects. For instance, in an energy business ecosystem, the climate is an important dimension that impacts the energy production (e.g., wind energy or solar power), and all Digital business ecosystem 'Constructed when the adoption of internet-based technologies for business is on such a level that business services and the software components are supported by a pervasive software environment, which shows an evolutionary and self-organizing behaviour' Peltoniemi and Vuori (2004) IT/ Technology ecosystem The network of organizations that drives the delivery of information technology products and services Iansiti and Richards (2006), Adomavicius et al. (2006) Platform  (2014), Tanev et al. (2010) Digital ecosystem A network of digital communities consisting of interconnected, interrelated and interdependent digital species, including stakeholders, institutions and digital devices situated in a digital environment, that interact as a functional unit and are linked together through actions, information and transaction flows Iyawa et al. (2016) Innovation ecosystem The complex relationships that are formed between actors or entities whose functional goal is to enable technology development and innovation Oh et al. (2016) segments in the energy supply chain, e.g., the lighting, heating, or cooling at the consumption side. Therefore, (Ma 2022) proposes five critical business ecosystem dimensions called CSTEP for systematically understanding a targeted business ecosystem (as shown in Table 6). Furthermore, each dimension consists of several sub-dimension and macro and micro levels (as shown in Table 7). Various energy ecosystem cases have applied the CSTEP, e.g., microgrids (Ma et al. 2018b) and distribution tariffs . Table 5 The measures of robustness, productivity, and niche creation proposed by Iansiti and Levien (2002) Dimension

Robustness
Survival rates Ecosystem participants enjoy high survival rates, either over time, or relative to other, comparable ecosystems Persistence of ecosystem structure Changes in the relationships among ecosystem members are contained; overall the structure of the ecosystem is unaffected by external shocks. Most connections between firms or between technologies remain Predictability Change in ecosystem structure is not only contained, it is predictably localized. The locus of change to ecosystem structure will differ for different shocks, but a predictable "core" will generally remain unaffected Limited obsolescence There is no dramatic abandonment of "obsolete" capacity in response to a perturbation. Most of the installed base or investment in technology or components finds continued use after dramatic changes in the ecosystems environment Continuity of use experience and use cases The experience of consumers of an ecosystem's products will gradually evolve in response to the introduction of new technologies rather than being radically transformed. Existing capabilities and tools will be leveraged to perform new operations enabled by new technologies Productivity Total factor productivity Leveraging techniques used in traditional economic productivity analysis, ecosystems may be compared by the productivity of their participants in converting factors of production into useful work Productivity improvement over time Do the members of the ecosystem and those who use its products show increases in productivity measures over time? Are they able to produce the same products or complete the same tasks at progressively lower cost?
Delivery of innovations Does the ecosystem effectively deliver new technologies, processes, or ideas to its members? Does it lower the costs of employing these novelties, as compared with adopting them directly, and propagate access to them widely throughout the ecosystem in ways that improve the classical productivity of ecosystem members?
Niche creation Variety The number of new options, technological building blocks, categories, products, and/ or businesses being created within the ecosystem in a given period of time

Value creation
The overall value of new options created

Technology adoption theories and models
The innovation adoption theory is firstly introduced by Rogers in 1960, in his publication called "Diffusion of Innovation Theory" (Rogers 1962). This theory's essential elements are the S-shaped (logistic function) shown in Fig. 1 and the adoption rate curve shown in Fig. 2. Additional technology adoption theories and models have been addressed for many years. The theory tries to describe the adoption behavior toward new technology. Understanding and knowing such behavior can help develop business models aiming to achieve a fast and/or high adoption. In total, 30 technology adoption theories are identified from the literature (Gangwar et al. 2014;Taherdoost 2018;Sharma and Mishra 2014;Lai 2017;Oliveira and Martins 2011;Maryam Salahshour et al. 2018;Molinillo and Japutra 2017;Qayyum and Ali 2012) and shown in Table 8. Among the 30 theories, the main focuses of the most popular discussed technology adoption theories are summarized as shown in Table 9 based on the discussion in Taherdoost (2018) (Sharma and Mishra 2014;Lai 2017).
Furthermore, many constructs in the technology adoption theories have been identified and discussed in the literature, as shown in Table 10. The application of these Policies and regulation Policies the activities of the government, members of law-making organizations, or people who try to influence the way a country is governed;

Regulation
An official rule or the act of controlling something constructs in the technology adoption decision processes can be divided into "before adoption, " "adoption decision, " and "after the decision, " as shown in Table 11. Technology adoption has been applied in the energy domain with several focuses. For instance, Ma et al. (2018c) identifies influential factors for Industrial consumers to adopt smart grid concept. Ma et al. (2019b) conducts a survey to investigate demand response control preferences, stakeholder engagement, and cross-national differences for retail stores' demand response adoption. Furthermore, technology evaluation and adoption of energy related solutions has been conducted with modeling and simulations, and applied for both energy efficiency (Christensen et al. 2020a, 2019) energy flexibility Christensen et al. 2020b), and CO2 emission reduction (Christensen et al. 2020c).

Methodology
To identify business opportunities in a business ecosystem, it is essential to clarify two terms unmet needs and megatrends that trigger the potential changes in a business ecosystem: • Unmet needs usually indicate needs, demands, or challenges that have not yet been met or solved in the current business ecosystem. The unmet needs are usually related to climate (climate changes) or economic challenges in the energy ecosystems, e.g., electricity supply for the inhabited islands in Indonesia. • Megatrends usually indicate how a business ecosystem evolves and how the future of the targeted business ecosystem will look. Megatrends are usually due to political goals (e.g., climate neutrality in 2050 in Denmark), advanced technologies (e.g., digitalization), or society's willingness in industrial ecosystems. Megatrends can help to understand what future ecosystems look like. There might be several or many megatrends in an ecosystem. The application of CSTEP can facilitate the evaluation of these future trends and the selection of the most potential ones for development.  (Rogers 2003) Four steps in the ecosystem-driven business opportunity identification method are designed for the investigation of business opportunities in the targeted business ecosystem, and each step includes several sub-steps (as shown in Fig. 3): Step 1: Identify the CSTEP dimensions of the current business ecosystem.
Step 2: Identify potential changes in the business ecosystem.
Step 3: Identify future ecosystem trends and timeline.
Step 4: Select business opportunities.
Step 5: Potential solution identification. Diffusion of Innovation Theory Rogers (1960) Rogers (1962) Inter-organizational relationship theory Clark (1965) Clark (1965) Flow theory Csikszentmihalyi (1975) Play and Rewards (1975) Theory of Reasoned Action Fishbein and Ajzen (1975) Ajzen and Fishbein (1975) Expectation confirmation theory Oliver (1977) Oliver (1977) Theory of Interpersonal Behavior Triandis (1977) Triandis (1977) Social identity theory Tajfel (1978) Tajfel (1978) Institutional theory DiMaggio and Powell (1983) DiMaggio and Powell (1983) Theory of Planned Behaviour Ajzen (1985, 1991) Ajzen (1985, 1991 The Social Cognitive Theory Bandura (1986) Bandura 1986 Perceived value model Zeithaml (1988;) Zeithaml (1988 Social capital theory Coleman (1988) Coleman 1988) Technical/Technology Adoption/ acceptance Model Fred D Davis et. al. (1986D Davis et. al. ( , 1989D Davis et. al. ( , 1996 Davis (1989;Davis and Venkatesh 1996;, 1986) Technology-organization-environment framework  Igbaria et al. (1994) Task technology fit model Goodhue and Thompson (1995) Goodhue and Thompson (1995) Decomposed Theory of Planned Behaviour Taylor and Todd (1995) Taylor and Todd (1995) Trust model Kipnis (1996) Kipnis (1996 Extended Technology Adoption Model 2 Venkatesh and Davis (2000) Venkatesh and Davis (2000) Uses and Gratification Theory Ruggiero (2000) Ruggiero (2000) Unified Step one: Identify CSTEP dimensions for the current business ecosystem To identify related CSTEP dimensions in a business ecosystem, firstly, it is necessary to investigate CSTEP dimensions to the related actors and objects in the defined business ecosystem, as shown in Table 12. The relevant value chain segments, actors and objects can be identified and listed during the business ecosystem architecture development introduced by Rogers (2003). However, not all actors and objects are relevant to the evolution of the ecosystem. For instance, in the EV home charging energy ecosystem (presented in the case study section), there is an actor called electricity supplier. The electricity supplier buys electricity from the electricity markets and is obliged to supply all household customers with electricity with a payment. However, this is not relevant to the evolution of the EV home charging energy ecosystem. Therefore, to reduce the analysis workload, this step should focus on the critical actors and objects relevant to the ecosystem's evolution.
Based on the result of Table 12, the current business ecosystem condition can be further described in detail (as shown in Table 13). Meanwhile, it is important to analyze the ecosystem conditions with references. The investigation of the regulations at the Venkatesh and Davis (2000) Unified Theory of Acceptance and Use of Technology Four key constructs affecting the acceptance and use of technology Venkatesh (2003) Venkatesh et al. (2003 Table 10 Definition of the identified constructs in the technology adoption theories

Construct Definition Author and year
Affect Towards Use "Feelings of joy, elation, or pleasure, or depression, disgust, displeasure, or hate associated by an individual with a particular act. " (Thompson, 1991) Intrinsic motivation "if performing an activity leads to a feeling of pleasure and results in satisfaction for the individual, such behaviour can be classified as intrinsic motivation. " (Davis, 1992) Affect "Positive contribution is made by the factor "affect" which is the extent to which an individual likes his job. " (Bandura, 1986) Anxiety "Negative contribution to desired behaviour is made by the factor "anxiety" which is the anxious reaction of the person while performing a job such as trying to use a computer with which the person is not very familiar. " (Bandura, 1986) Facilitating conditions "Facilitating conditions are defined as the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system. " (Venkatesh 2003) Result demonstrability "tangibility of the results of using the innovation. " (Venkatesh and Davis, 2000) Long-term consequences "Outcomes that have a pay-off in the future. " Thompson, (1991) Subjective norm Person's perception that most people who are important to him think he should or should not perform the behaviour in question Venkatesh and Davis (2000) Image "the degree to which use of an innovation is perceived to enhance one's... status in one's social system" Moore and Benbasat (1991) Social influence Social influence is defined as the degree to which an individual perceives that important others believe he or she should use the new system Venkatesh and Davis (2000) Social factors "Individual's internalization of the reference group's subjective culture, and specific interpersonal agreements that the individual has made with others, in specific social situations. " Thompson et. al. (1991) Perceived usefulness The degree to which a person believes that using a particular system would enhance his or her job performance Fred D Davis et. al. (1989) Perceived ease of use The degree to which a person believes that using a particular system would be free of effort Fred D Davis et. al. (1989) Job relevance Defined as an individual's perception regarding the degree to which the target system is applicable to his or her job. Regarded as cognitive judgment that exerts a direct effect on perceived usefulness, distinct from social influence processes Venkatesh and Davis (2000) Output quality Output quality measures perception of how well the system performs the job related tasks Davis et al. (1992) Performance expectancy Performance expectancy is defined as the degree to which an individual believes that using the system will help him or her to attain gains in job performance Venkatesh (2003) Effort expectancy Effort expectancy is defined as the degree of ease associated with the use of the system Venkatesh (2003) Attitudes "Sum of beliefs about a particular behaviour weighted by evaluations of these beliefs" Ajzen (1991) Perceived behavioural control "people's perception of the ease or difficulty of performing the behaviour of interest" Ajzen (1991) Job-fit "The extent to which an individual believes that using a technology can enhance the performance of his or her job. " Thompson et. al. (1991) P-dimension can help to understand the current ecosystem condition, and the policies will later be used for understanding the future business ecosystem. The main difference between Tables 12 and 13 is: Table 12 is from the individual ecosystem elements' perspective, and Table 13 is from the relevance of the ecosystem perspective.
Step two: Identify potential changes in the business ecosystem To identify potential changes in the business ecosystem, step two is divided into two sub-steps: 1. Identify political or business statements critical to the business ecosystem 2. Portray future ecosystem condition • Sub-step 1: Identity political or business statements critical to the business ecosystem Although some policies related to the identified actors and objects are investigated in Step one, the policies related to the future ecosystem conditions are not completed. Therefore, it is necessary to investigate and identify political or business statements critical to the business ecosystem.
The transformation of a business ecosystem is usually strongly influenced by the ecosystem dominators, e.g., governmental authorities or leading companies. For instance, energy-related business ecosystems are driven by political agendas, such as 70% CO 2 reduction in 2030 and climate neutrality in 2050 in Denmark; Hightech related business ecosystems, are usually driven by leading giant companies. For instance, in the social media business ecosystem, the announcement of Facebook to be in the metaverse business indicates a social media ecosystem trend.
Although the initiatives created by leading giant companies, such as Google glasses, can provide inspiration or highly possibly become megatrends in the related business ecosystems, the future (e.g., when and in what way) is unclear because megatrends are usually formed with strong collective effort. Therefore, investigating unmet needs or megatrends in a given business ecosystem is rec-

Construct Definition Author and year
Complexity "The degree to which an innovation is perceived as relatively difficult to understand and use. " Thompson et. al. (1991) Extrinsic motivation the perception that users want to perform an activity "because it is perceived to be instrumental in achieving valued outcomes that are distinct from the activity itself, such as improved job performance, pay, or promotions". Examples of extrinsic motivation are perceived usefulness, perceived ease of use, and subjective norm Davis et al. (1992) Self-efficacy "the judgments of how well one can execute courses of action required to deal with prospective situations. " Venkatesh (2003) ommended to focus on the political goals. The ecosystem boundary can indicate related political or business statements, since the boundary is defined by the supply chains, market or systems in a certain geographical/cultural boundary. • Behavior The Social Cognitive Theory • Self-efficacy (the judgments of how well one can execute courses of action required to deal with prospective situations) • Affect (positive contribution) • Anxiety (negative contribution) • Expectations of outcome (personal as well as performance-related gains)

• Behavior
There are two approaches to identifying relevant policies: domain expert recommendations for those familiar with the related areas; Policy or trend investigation for each actor/object and their roles in the ecosystem. Especially, the information in governmental white papers and reports provides a detailed description of the focus areas, and is often supported by numbers and data. • Substep 2: Portray future ecosystem conditions The critical political or business statements usually provide a direction where the current business ecosystem might evolve (the future business ecosystem). Therefore, the potential changes can be identified by the gap analysis. To do so, it is necessary to ask the following questions about each current ecosystem condition with each identified critical political or business statements: Sub-step 2 strongly requires expert input, and at some dimensions, there might not be any significant difference between current and future ecosystems. Therefore, it doesn't need to be included. The summarized guideline and result of Step two: Identify potential changes in the business ecosystem is shown in Table 14.
Based on Table 14, the future ecosystem conditions can be identified at each CSTEP dimension. In most cases, the policy and regulation dimension will be blank, since governmental authorities make decisions. Meanwhile, there might be overlaps among the identified future ecosystem conditions across the CSTEP dimensions. Therefore, it is necessary to conduct merging and reorganizing, and present the future ecosystem conditions precisely and comprehensively.   Step three: Identify future business ecosystem trends and timeline Although the future ecosystem conditions are identified at Step two. The realization timeline is not clear. The realization timeline relates to when (in short-, medium-, or long terms) and what (which part of the ecosystem) will change. A CSTEP fivedimensional three-scale evaluation method for ecosystem trends (shown in Table 15) is introduced to answer this question. This evaluation method might have different weights among the five CSTEP dimensions. The presentation of the weight differences can be qualitative (indirect and descriptive) or quantitative (direct and quantified). In different cases, it might apply different prioritization based on the purpose of the evaluation, e.g., research gap identification. At which scale of the required technology is ready to realize this trend? E Economic feasibility 1: Long-term return-on-investment, or no financial significant benefit but large investment/cost 2: Medium-term return-oninvestment 3: Short-term return-on-investment, or significant financial benefit and low investment At which scale of financial benefits that this trend will provide to the core stakeholders? P Political and regulatory feasibility 1: Policy agenda is under discussion, and the related regulations remain unclear 2: Political agenda is there, and the regulation will be ready after a certain period 3: The regulations are ready or will be ready in a short-term period At which scale of regulations and policy agendas are ready to support the realization of the trend?
As Table 15 shows, the higher score at a dimension, the higher likelihood that a trend would happen. It is based on the principle that the evolution of a business ecosystem is always towards the direction that can benefit the ecosystem the most.
To identify future ecosystem trends and timeline, this step is divided into 3 substeps: 1. Modify the evaluation criteria from the CSTEP five-dimensional three-scale evaluation (shown in Table 12) if necessary. 2. Evaluate the future ecosystem conditions, and score how likely the future ecosystem conditions will happen in the near future based on the CSTEP three-scale ecosystem trend evaluation method with scores (shown in Table 16).

Rank the future ecosystem conditions based on the total scores
Step four: Select business opportunities The ranking of the total scores from Step three represents the realization timeline of the identified trends. The ecosystem roadmap and the transition stages can be identified based on this ranking. According to Ma et al. (2021): • Ecosystem roadmap: is a critical path with sequenced ecosystem transition stages for achieving the planned/future ecosystem. • Transition stage: One Minimum Variable Ecosystem (MVE) or expanded/shifted ecosystem is designed at one transition stage. The sequence of the transition stages can be either horizontal (boundary scale) dependent on the boundary coverage or vertical (time scale) dependent on the realization terms (short, medium, and long terms).
Therefore, each of the top-ranked ecosystem trends will be at one transition stage. Based on the ranking, the sequence of the transition stages can be identified. Sometimes, there are sub-transition stages at one transition stage because the ecosystem trends can happen simultaneously or the ecosystem trend happens with certain conditions. Therefore, it is important to ensure the sequence of the (sub)transition stages Table 16 Evaluation of ecosystem potential change

Future ecosystem condition C S T E P The total score Explanation
From according to the realization conditions. However, there might be different results due to different stakeholders' focuses. It is necessary to match identified policies with the identified trends. It not only can clarify the goals of the identified trends, but also can confirm whether the identified trends are the megatrends or unmet needs in the targeted ecosystem. The related policies can be stated as shown in Table 17.
To portray the future ecosystem, it is necessary to map the transition stages to the identified relevant actors and objects with value chain segments of the ecosystem (at Step one) as shown in Table 18. Therefore, the future ecosystem can be described according to the summary in Table 18. Furthermore, the value proposition for each actor can be proposed as shown in Table 19.

Step five: Potential solution identification
With the identified value propositions, potential solutions can be investigated, evaluated and identified. To do so, two sub-steps should be conducted:

• State-of-the-art (SoA) solution investigation • SoA solution evaluation • State-of-the-art (SoA) solution investigation
The SoA investigation includes market research and literature (sometimes, patent search is also conducted to avoid any infringement issue). The purpose of the market research is to investigate whether there are any existing products in the targeted ecosystem that provide similar value. If yes, this value proposition is not considered for further because there is no opportunity for the ecosystem stakeholders unless the existing product can not fully fulfil the value proposition.
The literature research aims to investigate whether there is any solution that (1) can provide the identified value; (2) has not been implemented in the current ecosystem, and (3) uses the most modern or advanced techniques or methods. Transition stage number • The sequence of the transition stage is based on total scores of ecosystem trends identified from Table 3.6 (Evaluation of ecosystem potential change). The higher the total score is, the earlier the transition stage is • The ecosystem trends that have the same total score are allocated at the same transition stage • The sequence of the sub-transition stages is based on the individual CSTEP score. The result might be different due to different stakeholders' focuses. However, recommended priority among the CSTEP dimension is: T, E, S, P, C Allocate the relevant policies identified at

• SoA solution evaluation
Step 2 for each (sub)transition stage Table 18 The future ecosystem description *Transition stages are from Table 17 Identified transition stages and related policies ** Actor/object is from the column of Relevant business ecosystem elements

Future ecosystem
From column-Value chain segment in Table 8 Investigation of related CSTEP dimensions for business ecosystem elements From column-Relevant business ecosystem elements in Table 8 Investigation of related CSTEP dimensions for business ecosystem elements For each transition stage* and actor/object**, ask the following question: • Whether this transition stage make anything change to this actor or object?
•How this actor/object will be changed due to this transition stage? Describe the change result of the actor/object Not all the investigated SoA solutions are feasible to be applied in the targeted ecosystem. Therefore, a feasibility assessment needs to be conducted and identify the most feasible solutions that potentially can be applied in the targeted ecosystem. One method can be applied with modification is the feasibility assessment method applied ).

Software architecture
The software architecture of the CSTEP tool aims to capture and describe the fundamental building blocks and what they consist of. The tool is built on a classic client/ server approach, which relies on considering the separation of concern on the client-side and server-side. The architecture consists of the three following tiers:

• Frontend • Backend • Database
On the client-side, the frontend, acting as a presentation tier, provides the user interface and allows for sending requests to the server side. The communication for these requests are established through the API (Application Programming Interface) exposed by the server-side, consisting of the backend and the database. The backend act as a business tier, responsible for handling the incoming requests from the user and replying with a response. All data required for the functionality to function is stored in the database, acting as a data tier. Together, these components constitute the foundation for a web application offering the functionality required by the proposed method.
With the basics in place, a more detailed description of the architecture, what components the tiers hold and how they associate is now introduced. Figure 4 depicts the three tiers, including their respective components. The structure and content of each tier are highly affected by the different technologies applied in the project. The goal is to include technologies that help ensure the ability to provide the required functionalities in terms of following the procedure of the proposed method and promote core software qualities appropriate for the application supporting these functionalities. The focus was to create a lightweight, easily maintainable and flexible application for the users.

Table 19
The proposed value propositions Actor/object is from the column of Relevant business ecosystem elements in Table 14 The future ecosystem description

Market segment Related technology Identified value proposition
List each actor* in the relevant business ecosystem elements List object* in the same value chain segment and the same transition stage as the listed actor The value proposition formula is: The identified object will have the ability of (the object's future ecosystem condition) for (the actor) to have (the actor's future ecosystem condition) Note: If an object is at a transition stage with no actor in the same value chain segment, it should consider actors in other value chain segments at the same transition stage

Frontend
The nature of Vue.js and Nuxt.js highly impacts the architecture of the frontend. These frameworks allow developers to build user interfaces on a component-based programming model that allow for easy structuring and encourages flexibility. Together, these frameworks offer features that ease and improve the development experience through easy routing, modularity and reusability, virtual DOM rendering, reactive data binding and more. Communication from the frontend to the backend is established through a tool called Axios, which is an HTTP client for JavaScript, providing the ability to make HTTP requests from the application running in the user's browser.

Backend
The backend and API are built using Node.js as it provides a great runtime environment for backend services, where fast and easy development in JavaScript, simple file structure, support for many open-source libraries and performance are in focus. The architecture of Node.js ensures asynchronous handling of requests from the user, allowing more efficient processing and the ability to serve multiple clients on one thread without having to create a thread for every request. This makes Node.js suitable for this project as the potentially many concurrent users and the nature of the features in the application result in I/O-intensive activity. Regarding the API, the backend uses a tool called Express to expose the endpoints accessible from the frontend. Each endpoint calls a method from a controller related to the object related to the requested functionality. These methods that set the boundary and actions of an event are defined in the controllers' folder. The exposed endpoints are specified in the app.js file, which also holds information on how the connection to the database is established. This connection is made possible through the Mongoose library. This library is applied in the backend and not only allows the backend to manipulate data in the database but also to help define data models or schemes for the documents stored in the database.

Database
For storing data about the users and the projects they create in the application, Mon-goDB is used. MongoDB is a document-based NoSQL database offering flexibility and scalability. Data on users and projects are stored in separate collections, analogous to tables in relational databases, that each holds a set of individual documents, one for

Case study
An example of the EV home charging energy ecosystem is used to explain the implementation of the proposed method. The ecosystem map generator investigates the business ecosystem architecture of the case (ecosystemmapgenerator.sdu.dk). Meanwhile, the critical actors and objects relevant to this case study are exported to the tool-CSTEP business opportunity identifier, as shown in Table 20.

CSTEP dimension identification for the targeted business ecosystem
According to Table 12 (Investigation of related CSTEP dimensions for business ecosystem elements) in the methodology section, the CSTEP dimensions related to each actor and object can be added as shown in Table 21. Furthermore, the current ecosystem conditions can be summarized and presented based on CSTEP.

Potential change identification in the business ecosystem
Related political or business statements can be defined based on the boundary of the EV home charging energy ecosystem:  • Investing in the development and deployment of green digital solutions with significant energy and material efficiency that achieve a net positive impact in a wide range of sectors • Developing methods and tools to measure the net impact of green digital technologies on the environment and climate by joining forces with NGOs and relevant expert organizations • Co-creating, with representatives of other sectors, recommendations and guidelines for the green digital transformation of these sectors that benefits the environment, society, and economy

Future ecosystem conditions
Based on Step 2 and Table 14 Identification of future ecosystem conditions, the potential future ecosystem conditions can be identified as shown in Table 22.

Future business ecosystem trend identification
Based on Step three: Identify future business ecosystem trends in the methodology section, the results of ecosystem potential change evaluation for this case study can be shown in Table 23.

Business opportunity selection
Based on Table 23 (Results of ecosystem potential change evaluation), four transition stages are defined as shown in Table 24: The transition stages represent the realization potentials. The future ecosystem description and the proposed value propositions for each transition stage can be described as shown in Table 25.

Potential solution identification
The value proposition related to the Transition stage 1 (1.1 and 1.2) and 2 (2.1 and 2.2) is considered for the investigation of the potential solutions. Based on the market research, the EV charging algorithms in the Danish market are either the traditional charging that EV users charge EVs immediately when they arrive home or electricity price signal based charging. However, none of these two consider the dynamic distribution tariffs or CO2 emission, and the second charging strategy needs to be manually configured.
Therefore, a literature review is conducted to investigate State-of-the-art (SoA) EV charging strategies. According to Christensen et al. 2020d, the EV charging strategies can be categorized as centralized and decentralized, and the decentralized charging strategies are usually used by the EV users. Furthermore, based on evaluation with the modified feasibility assessment method , Real Time Pricing (Nimalsiri et al. 2019), Time-of-Use Pricing (Chunlin et al. 2017), and Timed charging (Huachun et al. 2012) are the most feasible decentralized EV charging strategies in Demark.

Discussion
The case study of the EV home charging energy ecosystem shows that the proposed methodology can facilitate the business opportunity identification process. However, although there are only four steps in the method, it is difficult to follow the steps in practice due to the complex logic behind each step and across steps. Therefore, the All EV users will adopt an hourly electricity price scheme Table 23 Results of ecosystem potential change evaluation

Future ecosystem condition C S T E P Total
Increase in the number of EVs 3 3 3 3 3 15 All EV users will adopt an hourly electricity price scheme 3 3 3 3 3 15 Dynamic distribution tariffs that comply with regulations will be designed and implemented 2 3 3 3 3 14 Intelligent EV charging strategies that can optimize EV users' bill and reduce CO2 reduction 3 2 3 3 3 14 DSOs will adopt Intelligent algorithms to enable energy flexibility strategy for sector coupling between EVs and the distribution grid 2 2 3 3 1 11 Independent aggregators are allowed to aggregate EVs for participation in the ancillary service market or Vehicle-to-Grid services 2 1 3 2 2 10 Table 24 The identified transition stages of the EV home charging energy ecosystem with future ecosystem conditions

Future ecosystem condition Transition stage
Increase in the number of EVs 1.1 All EV users will adopt an hourly electricity price scheme 1.2 Intelligent EV charging strategies that can optimize EV users' bill and reduce CO2 reduction 2.1 Dynamic distribution tariffs that comply with regulations will be designed and implemented 2.2 DSOs will adopt Intelligent algorithms to enable energy flexibility strategy for sector coupling between EVs and the distribution grid 3 Independent aggregators are allowed to aggregate EVs for participation in the ancillary service market or Vehicle-to-Grid services 4 Independent aggregators (e.g., EV charging box providers) can aggregate EVs and participate in the ancillary service market or provide Vehicle-to-Grid services by using intelligent EV charging algorithms web-based tool-CSTEP business opportunity identifier (https:// oppor tunit yiden tifier. sdu. dk/) solves this challenge. For instance, the first two steps (CSTEP dimension identification for the targeted business ecosystem and potential change identification in the business ecosystem) can be presented on one webpage (a screenshot is shown in Fig. 5). Furthermore, the calculation error increases when evaluating the ecosystem's potential changes including many evaluation subjects. The tool can automatically calculate and rank the total score, making the process much easier (as shown in Fig. 6). Moreover, the analysis result can be downloaded as an Excel file for further work.
Furthermore, the tool allows a collaborative environment that multiple users can share and edit the same project. In this way, relevant stakeholders can be involved to ensure a clear interpretation shared among the stakeholders, and stakeholders' opinions/feedback, e.g., on the derived value propositions, can be captured during the whole process.

Conclusion
This paper proposes an ecosystem-driven business opportunity identification method. This method includes four correlated steps, and the proposed method is implemented as a web-based tool. A case study of the EV home charging energy ecosystem is applied and demonstrates the application of the proposed method and the implementation of the developed web-based tool. The results show that the potential changes can be identified, and the future business ecosystem conditions can be portrayed. Furthermore, the business opportunities can be selected, and correlated value chain segments can be placed at the actor and object level. For instance, three value propositions are identified in the case study: (1) EV users can have optimal EV charging cost and optimal CO2 emission consumption with the intelligent EV charging algorithms that consider electricity prices, tariffs, and CO2 emission; (2) DSOs can avoid grid overloads and postpone the grid upgrade by applying intelligent EV charging algorithms; (3) Independent aggregators can aggregate EVs and participate in the ancillary service market or provide Vehicle-to-Grid services by using intelligent EV charging algorithms. Moreover, three feasible decentralized EV charging strategies (Real Time Pricing, Time-of-Use Pricing, and Timed charging) are identified as the potential solutions targeting the first value proposition. This result also illustrates the importance of digitalization in the energy transition, especially for energy efficiency, energy flexibility, and CO2 emission reduction. Moreover, the web-based tool-CSTEP business opportunity identifier proves the ability to facilitate and ease the whole analysis process.
The proposed ecosystem-driven business opportunity identification method addresses gaps and contributes to three research domains: business ecosystem, technology adoption, and strategy management. The proposed method is a systematic approach that allows ecosystem stakeholders to conduct collaborative business opportunity analysis and evaluation. Furthermore, the application of the CSTEP dimensions and ecosystem architecture design ensures all aspects and elements related to the targeted ecosystem can be covered and investigated. Meanwhile, the user-friendly web-based tool, business opportunity identifier, can facilitate teaching in class for students to quickly understand the needs and value of the technical solutions in the energy sector.
The web-based tool, business opportunity identifier, will be available via opportunityidentifier.sdu.dk. The tool is developed and passed the initial verification and validation testing. Later this year, the tool will be further tested in the course of "Ecosystem driven technology development and adoption" for the Master programs of energy system and technology and welfare technology.