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JOItmCJournal of Open Innovation: Technology, Market, and Complexity
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23 March 2022

An Energy Culture Maturity Conceptual Framework on Adopting Energy-Efficient Technology Innovations in Buildings

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Department of Building Economics, Faculty of Architecture, University of Moratuwa, Moratuwa 10400, Sri Lanka
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School of Architecture and Built Environment, Faculty of Science Engineering and Built Environment, Deakin University, Geelong 3220, Australia
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Author to whom correspondence should be addressed.
J. Open Innov. Technol. Mark. Complex.2022, 8(2), 60;https://doi.org/10.3390/joitmc8020060
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Abstract

The building sector is identified as the leading global energy consumer. Adopting energy-efficient technology innovations has been recognised as the most promising approach to reducing energy consumption in buildings. However, such technology adoption is considerably lacking due to traditional techno-economic thinking, which lacks human focus. Energy culture has been identified as a research domain that successfully overcomes the traditional techno-economic focus of technology diffusion. However, available energy culture studies on adopting energy-efficient technology innovations in buildings are limited to exploring specific energy cultures rather than investigating holistic energy culture maturity, which guides incremental diffusion of energy-efficient technology innovations. Conversely, culture maturity has been studied in other cultural research domains such as safety cultures. Therefore, this study aims to develop an energy culture maturity conceptual framework that provides a holistic view of energy culture maturity for adopting energy-efficient technology innovations in buildings. The research method this study implemented was a scoping literature review method, using Web of Science, Scopus, and Engineering Village research databases. The findings of the study include the development of factor categorisation with 14 main factors and 11 subfactors and the development of three energy culture maturity stages, as well as the development of the energy culture maturity conceptual framework as the principal outcome. The proposed conceptual framework significantly contributes to energy culture research as the pioneering framework on energy culture maturity. The framework should be further tested and applied to find its utility.

1. Introduction

Global energy consumption has been a major contributor to greenhouse gas (GHG) emissions [1,2]. Further, global energy consumption is projected to increase by 30 to 50% in the next twenty-five years [3,4]. The building sector is the global leader in energy use [5,6,7,8,9] and is responsible for more than one-third of the world’s energy consumption [10,11], Yang et al. cited in [12,13]. Therefore, energy efficiency improvements in buildings can significantly reduce global energy consumption and related GHG emissions [14,15,16,17,18,19]. Open eco-innovations can increase the energy efficiency in buildings [19,20,21,22,23] throughout their lifecycle [24,25]. According to Ruby [26], Schubert and Stadelmann [27] and Trotta [28], increased adoption of energy-efficient technology innovations in buildings is the most appropriate strategy to reduce energy consumption in buildings. Further, energy consumption reduction through energy efficient innovations provides benefits such as reducing production cost, increasing compliance with regulations and facilitating sustainable development [29]. Therefore, significant diffusion of the widely available energy-efficient technology innovations in buildings has become essential [30,31].
However, despite the availability of various cost-effective energy-efficient technology innovations, their adoption is not at a satisfactory level [32,33,34,35,36,37]. As a result, potential contribution of buildings to reduce global energy use has not been adequately met. Furthermore, the United Nations [38] reported a significant gap between the targeted and actual rates of global energy efficiency improvements to achieve Sustainable Development Goal (SDG) 7. According to SDG 7, the target is to maintain a rate of energy efficiency improvements of at least 2.7% annually until 2030. However, the actual rate of improvement was as low as 1.3% in 2018. Traditional techno-economic thinking, without considering the human dimension, is one of the major obstacles that prevents the adoption of energy-efficiency technology innovations [39,40]. Promisingly, many authors have proposed to deploy an energy culture research approach that considers the human dimension to overcome the traditional techno-economic focus of technology diffusion [27,39,40,41,42,43,44,45,46,47].
There are several energy culture studies focused on the diffusion of energy-efficient technology innovations in buildings and similar environments, such as home heating technologies in New Zealand [42], heat pump driers in New Zealand’s timber industry [48], hot water systems in Australia [49], Light Emitting Diodes (LED) lighting in the United States Navy [45], and eco-innovations in business organisations in New Zealand [50]. However, one of the major limitations of these energy culture studies is that they only examine the details of the existing energy cultures, such as drivers and barriers for the diffusion of energy-efficient technologies in buildings, without examining the holistic view of energy culture maturity. At the same time, the holistic culture maturity has already been well-established in other culture research domains, such as safety culture and guides for cultural excellence [51,52,53,54,55]. In addition, existing energy management maturity studies are also limited to areas such as energy management in organisations [56,57,58,59,60] and ISO 50001 energy management system (EnMS) [61,62,63], without focusing on energy culture maturity. Therefore, none of the available energy research studies on energy-efficient technology diffusion in buildings has focused on holistic energy culture maturity, which provides the roadmap for energy culture excellence. Therefore, this study aims to take the innovative step to develop the first energy culture maturity conceptual framework focusing on the diffusion of energy-efficient technology innovations in buildings. The proposed energy culture maturity conceptual framework contributes to expanding the energy culture research domain by adding a novel branch of energy culture maturity research. The framework can be used to assess the status of the energy culture with respect to energy-efficient technology diffusion in buildings and contribute towards the achievement of energy cultural excellence.
The structure of this article can be explained in a few steps. First, a background relevant literature review on energy culture and energy culture maturity is presented. Then, the materials and methods of the study are given, explaining the research methodology employed. The following section, results, explains the findings, including the development of the conceptual framework. Finally, the conclusion section provides the implications and the further research directions of the study.

2. Background Literature

2.1. Energy Culture

Ishak [64] identified the model developed by Lutzenhiser [41] as the first attempt to unveil energy culture as a concept. Stephenson et al. [42] also concurred that the same work was an innovative study. However, Stephenson [46] and Stephenson et al. [42] recognised a significant limitation of the development made by Lutzenhiser [41]; it is not a developed theoretical model since it only describes the energy culture concept. Considering the further advancements of the energy culture research area, Ishak [64] highlighted the development by Stephenson et al. [42] as the first framework that comprehensively explains the energy culture, illustrated in Figure 1.
Figure 1. Energy cultures framework (Adapted from Stephenson et al. [43]).
According to Stephenson et al. [42], the framework was comprehensively developed based on systems and behavioural theories. Therefore, the energy cultures approach can resolve considerable gaps in the existing energy behaviour literature since it provides a broader cultural identity, which is lacking in the traditional techno-economic scholarship. Jürisoo et al. [65] also agreed on the inadequacy of existing energy-related models to explain real-life energy behaviours and the potential of energy cultures framework to be utilised in the multidimensional and interrelated examination of complex and multifactor energy behaviour. Furthermore, the energy cultures framework has proven its ability to examine energy behaviours in different contexts, from domestic buildings to firms and industries [66].
Stephenson et al. [42] identified the three core components of the energy culture as norms, material culture and practices. Norms are defined as the shared beliefs regarding the behaviour of persons in a given context; material culture refers to the energy-consuming physical elements, such as machinery; and finally, practices represent usual activities that consume energy. Hence, energy culture can be broadly defined as the interactive energy behaviour between a given subject’s norms, practices, and material culture [43]. The energy behaviour of different subjects, such as individuals, households, businesses, or countries, can be studied by examining the interaction between the three components [67,68]. According to Ford et al. [69], energy cultures are distinctive in different settings, such as domestic and industrial; therefore, they impact the occupants differently. Norms impact energy technology choices (material cultures) and energy-consuming activities (practices). Material culture shapes energy technology utilisation, practices and norms. Furthermore, practices control the use of energy technologies (material cultures) and partly influences attitudes, values, and belief systems (norms) [42]. Occupants’ energy behaviour in buildings is a combined effect of these three principal components that create a self-reinforcing system [64]. In addition, energy culture is shaped by external influences, which are typically beyond the control of internal actors [70]. External influences, illustrated outside the dotted line of Figure 1, generally include policies, regulations, energy prices, subsidies, information and promotion campaigns and broader social norms [43].

2.2. Energy Culture Maturity Models

According to Schein and Schein [71], organisational culture matures through stages such as founding and early growth, midlife, and maturity, which also applies to energy culture. Therefore, energy culture maturity can be identified as the maturation journey of an energy culture. Hence, the energy culture maturity shows different levels in the evolution of the norms, practices, and material culture. It is essential to note how knowledge on energy culture maturity has emerged in developing an energy culture maturity framework. The Capability Maturity Model (CMM) developed by Paulk et al. [72] was the first recognised maturity model and has been widely used in the software industry [63] and was later propagated in various fields such as engineering and construction, manufacturing, work management, healthcare, eco-design, and mining [62]. The CMM has been adapted to develop culture maturity models and energy maturity models. According to Paulk et al. [72], the capability maturity model guides the establishment of cultural excellence within an organisation. Therefore, the culture enhancement ability of CMM confirms the adaptability for various culture maturity studies. Accordingly, there are various applications of CMM already available, such as the safety culture maturity model [52,53,54,55,73,74,75,76,77,78], the health safety environment culture maturity model [79], the organisation culture maturity model [80] and the food safety culture maturity model [81]. Overall, the literature on culture maturity models confirms the suitability of maturity models for energy culture maturity studies. Furthermore, the existing literature on culture maturity also confirms the unavailability of energy culture maturity studies. Apart from the culture maturity models, there are energy maturity models developed based on the CMM in the literature. However, the energy maturity models are in their infancy [63] due to a lack of research and practical advice on implementation [57]. Table 1 shows a review of the available energy maturity models.
Table 1. Review on energy maturity models.
The studies on energy maturity models were limited to Ngai et al. [60], Antunes et al. [63], Introna et al. [59], Jovanović and Filipović [62], Finnerty et al. [56], Finnerty et al. [46], Prashar [47], and Jin [50], as given in Table 1. The available energy maturity models given in Table 1 were focused on areas such as energy management in organisations [56,57,58,59,60] and the ISO 50001 energy management system (EnMS) [61,62,63]. Only Finnerty et al. [56] and Prashar [58] have focused on adopting energy-efficient technologies among the available studies. Furthermore, literature on energy maturity models confirms that none of the existing models has focused on energy culture maturity.
Overall, the suitability of the maturity models to examine energy culture maturity can be confirmed through the existing applications in culture maturity and energy maturity, as discussed above. Therefore, there is potential to develop an energy culture maturity framework based on these developments. Thus, the energy maturity model developed by Finnerty et al. [56], shown in Figure 2, was adapted to be the foundation for developing the energy culture maturity framework in this study.
Figure 2. Energy maturity model (Adapted from Finnerty et al. [56]).
The reason for adopting the Finnerty et al. [56] model was its strong focus on adopting energy-efficient technologies compared to the Prashar [58] model, which focused less on adopting energy-efficient technologies. There are five maturity levels: none or minimal, emerging, developing, advancing, and leading in the adapted model. The five different levels of maturity represent the status of energy efficiency. Level 1 (none or minimal) represents the lowest level of energy maturity. Then, the maturity gradually increases to reach the highest level (leading) at level 5. The levels between 1 and 5 represent the roadmap for energy-efficiency enhancement [56].

3. Materials and Methods

3.1. Implementation of Scoping Literature Review Procedure

Integrating best practices within the literature has been identified as a possible way to develop maturity models [63]. Therefore, this paper executed a scoping literature review method that is suitable for identifying key characteristics and factors [82,83] to develop an energy culture maturity conceptual framework using relevant energy culture studies. The procedure proposed by O’Brien and Guckin [84] was used to carry out the scoping literature review. The procedure consists of 10 steps based on the established Cochrane collaboration checklist. Ten steps of the procedure successfully cover the three elements of a research methodology: study design, conducting, and data analysis. Accordingly, the study design is explained in step 1. Then, step 2 to step 9 explains the process of conducting the study. Finally, the data analysis was carried out as in step 10. The steps of the procedure and outcomes of each step are depicted in Figure 3.
Figure 3. Adapted procedure for scoping review (Source: Author developed based on O’Brien and Guckin [84]).
This inclusion and exclusion criteria of this research are defined on the research question, as outlined in the first step. The inclusion and exclusion criteria and respective justifications, which guided the relevant articles selection process, are provided in Table 2. In the second step, appropriate keywords were developed with the support of a liaison librarian before searching the electronic databases.
Table 2. Inclusion and exclusion criteria.
Then, the scoping review literature search was conducted using Scopus, Web of Science, and Engineering Village electronic databases. The suitability of these databases for energy research was recognised by Torreglosa et al. [86] and Princeton University Library [87] for Scopus and Web of Science, and by Bue Library [88] for the Engineering Village database. Table 3 depicts the scoping review searching summary details of the literature review.
Table 3. Scoping review searching summary.
As the third step outlines, the search results from all the databases were imported to EndnoteX9, the bibliographic software used. Before removing the duplicate articles using EndnoteX9, in the fourth step, relevant search details were recorded to facilitate future replications of this research. In the fifth step, 14 of the 43 articles found were identified as duplicates and subsequently removed from the results in the bibliographic software. Then, in the sixth step, the remaining 29 articles underwent a rigorous sorting process to identify the relevant and irrelevant articles, utilising the inclusion and exclusion criteria. The sorting process consisted of three steps: reading titles of articles, reviewing abstracts, and browsing through the full article. The articles that did not meet the inclusion criteria were recorded as irrelevant in each step. Only five articles met the inclusion criteria to qualify as relevant articles based on the sorting process. The five articles were: Walton et al. [50], Dew et al. [45], Gill et al. [49], Bell et al. [48] and Stephenson et al. [42]. The seventh step included additional searching using the reference lists of the five studies screened in the previous step to identify other articles relevant to this study. However, no other relevant articles were found from the reference list of the five studies. Then, the eighth step included examining the five articles to identify the level of relevancy to the research question and were ascribed with a star rating. In addition to the relevancy examination by categorising articles, a word cloud analysis was also performed using NVivo 12 software for the five articles, to further understand the results’ relevancy to this study. Implementation of the inclusion and exclusion criteria in the above steps was validated with the support of another researcher, which was the ninth step of the protocol. The tenth and final step of the protocol detailed the findings. This involved an in-depth investigation of the selected articles to understand the factors of energy culture affecting the adoption of energy-efficient technologies, the overall maturity stage of the respective energy cultures, and identification of the maturity descriptors. Ultimately, the findings were integrated, and the energy culture maturity conceptual framework was developed.

3.2. Validation Procedure for the Framework

The validation procedure comes after the development of the energy culture maturity conceptual framework, in the tenth step of the scoping literature review procedure, as detailed in Section 3.1. Then, the framework should be validated from a panel of energy experts from a particular industry. The need for an industry-specific validation is based on the recommendations of Stephenson, et al. [43] and Lutzenhizer [41], whose research studies are the leading literature in the energy culture research domain. Both studies stressed the need to specialise energy culture studies to a particular industry due to the existence of distinctive industry-specific energy cultures. Accordingly, the framework presented in this study should be validated for the focused industry with the support of experts before carrying out the energy culture maturity assessment.

4. Results

This section explains the findings of the study, where the preliminary results of word cloud analysis are presented first. The development of the energy culture maturity conceptual framework and related components follows, which is the outcome of the in-depth analysis of the five articles. Table 4 summarises the selected five articles from the scoping review, including contexts, energy-efficient technologies, and the types of organisations.
Table 4. Summary of the selected studies.
Considering the identification of the above seven contexts from CA to CG, Walton et al. [50] and Stephenson et al. [42] consisted of two different contexts within each study, while all the other studies are limited to single contexts. Accordingly, CA and CB belong to Walton et al. [50], representing two different contexts of energy cultures relating to the eco-innovation linked to the adoption of energy-efficient technologies in New Zealand business organisations. CC refers to a study adopting LED lighting technologies in the ships of the United States Navy [45]. CD is the study conducted on the diffusion of hot water systems in Australian domestic buildings [49]. CE presents the case of adopting energy-efficient heat pump dryers in the timber industry in New Zealand [48]. Lastly, Stephenson et al. [42] provide two other contexts, CF and CG, regarding the adoption of heating technologies in New Zealand homes before and after an energy culture change programme.

4.1. Word Cloud Analysis of Scoping Review Results

Word cloud analysis is a visual representation tool that reviews the frequency of words recurring in an article. The size of the words in a word cloud represents the frequency with which the words appear in an article [89]. As a result, word cloud analysis has been identified as a tool that analyses the relevance of an article for a given context. Figure 4 depicts the word cloud analysis developed using NVivo 12 software for the five articles selected from the scoping review.
Figure 4. Word cloud analysis (Source: Outcome of the NVivo 12 word cloud analysis).
Firstly, “energy” and “cultures” are prominent words in the word cloud. This demonstrates the focus on energy culture in the screened studies. Further, “using” and “technology” are identified as two prominent words, which reveals the focus of the selected studies on “adoption of technologies”. According to McNaught and Lam [90], word clouds enable users to gain a preliminary understanding of the nature of the data on hand. Furthermore, the most frequent words in this word cloud analysis show the relationship between the scoping review results and the research aim. Therefore, the word cloud analysis results have further confirmed the suitability of the selected articles from the scoping review for this study. Moreover, the word cloud analysis strengthened the eighth step of the scoping review to examine the relevancy of the results.

4.2. Energy Culture Maturity Conceptual Framework

This section presents the development of the energy culture maturity conceptual framework based on the findings of five studies representing three countries, as given in Table 4. Even though there might be cultural differences among different countries [91,92,93,94,95], there are similarities under certain conditions. In particular, there are previous studies on the cultural similarities between the five Anglosphere countries, namely, the United States, Canada, the United Kingdom, Australia and New Zealand [96,97,98]. This study’s five articles represent three Anglosphere countries: the United States, Australia, and New Zealand. Therefore, the suitability of the selected studies to develop the conceptual framework can be justified due to the cultural similarities among the selected Anglosphere countries. Development of the conceptual framework was carried out in several steps. First, the conceptual framework components were developed, including factors and factor categorisation, energy culture maturity stages, and energy culture maturity descriptors. Then, the conceptual framework was developed as the study’s outcome by integrating the factor categorisation, energy culture maturity stages, and energy culture maturity descriptors.

4.2.1. Factors and Factor Categorisation of Energy Culture

This section explains the development of the factors and factor categorisation. First, the selected studies were analysed in depth to understand the factors of the energy culture affecting the diffusion of energy-efficient technologies in buildings. Khan [99] defined the factors of energy culture that promote adopting energy-efficient technologies as the “drivers” and, conversely, the factors that limit adopting energy-efficient technologies as the “barriers”. The selected five studies given in Table 4 have already defined drivers and barriers of energy culture for each study. This study directly identified drivers and barriers considering the classifications of the drivers and barriers in the selected five studies. Accordingly, Walton et al. [50] consisted with four barriers under context A and 11 drivers under context B. Dew et al. [45], which is identified as context C, contained seven barriers. Five drivers were given in Gill et al. [49], which is named as context D. Bell et al. [48], as context E, owned 11 barriers. Finally, Stephenson et al. [42] comprised of three barriers under context F and four drivers under context G. The identified drivers and barriers are presented in Table 5. The drivers and barriers were assigned with codes for easy reference. For example, for “CAD1”, CA represents the relevant context, “D” denotes the driver, and 1 represents the respective driver number. Instead of “D”, “B” is used as the coding letter when coding barriers. Altogether, 45 factors were identified from the studies as the factors of energy culture affecting the adoption of energy-efficient technologies. The factors have been distributed between drivers and barriers as 20 and 25, respectively.
Table 5. Factors and factor categorisation.
Further, considering the origin of the factors, the 45 factors can be categorised under the three components of the energy culture, namely, norms, practices, and material culture. Out of the 15 factors of Walton et al. [50], three barriers and 10 drivers were presented under norms. Further, one barrier and one driver were identified under practices. Considering the Dew et al. [45] study, six barriers were presented under norms and one barrier was presented under practices out of the total of seven factors. Furthermore, Gill et al. [49] allocated all five factors under the norms as drivers. Bell et al. [48], out of the 11 factors, had nine barriers under norms, one barrier under practices and one barrier under material culture. Finally, Stephenson et al. [42] presented two barriers under norms, three drivers under norms, one barrier under material culture and one driver under material culture out of the total of seven factors. Accordingly, altogether, 38, 4 and 3, factors were originated from norms, practices, and material culture, respectively. Each study has shown that the norms are the principal originator of factors, rather than practices or material culture, when adopting energy-efficient technologies.
Then, the factor categorisation was developed as main factors and subfactors using the identified 45 factors. The purpose of developing factor categorisation is to identify the common factors under which to group the similar drivers or barriers. Subsequently, factor categorisation was used to develop the final framework of this study. The factor categorisation creates a proper structure for the factors in the final framework without repetitions. As the first step, all 45 factors were categorised under main factors. Then, if a further subdivision of the drivers or barriers identified under the main factors was meaningful, the subfactors were developed under the main factors. Overall, the factor categorisation as main factors and subfactors was carried out considering the similarities of the identified 45 factors. Furthermore, codes were assigned for the main factors and subfactors. For example, MF1 represents the first main factor, and SF1 represents the first subfactor. Table 5 outlines the development of the factors and factor categorisation.
As per the factor categorisation, 14 main factors and 11 subfactors were derived by analysing all drivers and barriers. Under norms, the main factors are attention to economic benefits, readiness for energy-efficient technologies, prioritisation for energy-efficient technologies, energy policies and strategies, knowledge of energy-efficient technologies, green values of owners, focus on green marketplaces, and the commitments of top management to presence of internal politics and industry norms acceptance. Further, changing business practices for energy-efficient technologies and acceptance of industry practices are the main factors under practices. Then, industry material culture acceptance and availability of energy-efficient technologies are identified as the main factors under material culture.
In addition, the subfactors were identified under the main factors. The subfactors identified under attention to economic benefits were investment return analysis and operational cost-saving focus. Readiness for energy-efficient technologies consists of three subfactors: whole organisation’s readiness, willingness to adopt energy-efficient technologies, and actively seeking energy-efficient technologies. The level of acceptance of energy-efficient technologies and priority in the procurement criteria are the two subfactors under the prioritisation for energy-efficient technologies. In addition, supportive key performance indicators and supportive energy policies and strategies were identified as subfactors under energy policies and strategies. Finally, knowledge of the benefits and the suitability of new energy-efficient technologies were identified as subfactors under knowledge of energy-efficient technologies.

4.2.2. Energy Culture Maturity Stages

As previously mentioned, this study adapted Finnerty et al.’s [56] energy maturity model with five maturity levels. However, it was challenging to identify the energy culture maturity for five maturity levels due to limited literature. Therefore, this study had to modify the Finnerty et al. [56] model with five maturity levels to be operationalised while keeping the original structure. Accordingly, a framework with three energy culture maturity stages was developed based on the original maturity model. The development of the three stages is illustrated in Figure 5. The three-staged maturity design covers the five maturity levels of the Finnerty et al. [56] model, where Stage 1 represents Level 1, Stage 2 represents Level 2, 3 and 4, and Stage 3 represents Level 5.
Figure 5. Development of maturity stages (Source: Author developed based on Finnerty et al. [56]).
Then, the energy cultures of seven contexts given in Table 4 were grouped into the three energy culture maturity stages, considering the energy culture’s overall support for adopting energy-efficient technologies. Energy culture’s overall support ranged from the energy cultures that strongly prevent adopting energy-efficient technologies to the energy cultures that exceedingly support adopting energy-efficient technologies in the seven contexts. Accordingly, CA, CC, CE, and CF contexts could be identified as the energy cultures that strongly prevent adopting energy-efficient technologies due to significant resistance from the energy culture. Therefore, those contexts were categorised for Stage 1. Then, CB could be identified as an energy culture that remarkably supports adopting energy-efficient technologies due to intense drivers in energy culture to adopt energy-efficient technologies. CB energy culture demonstrates a world-class example of energy culture excellence. Therefore, CB, categorised as Stage 3, was the topmost stage of the energy culture maturity. As the remaining contexts, CD and CG represented some drive for adopting energy-efficient technologies. However, there is room for further improvement of the energy culture to reach the energy culture excellence of Stage 3. Therefore, CD and CG were placed under the Stage 2 energy culture maturity, between Stage 1 and Stage 3. Figure 6 depicts the grouping of seven contexts of the selected five studies under three energy culture maturity stages.
Figure 6. Grouping contexts under energy culture maturity stages (Source: Author developed).

4.2.3. Energy Culture Maturity Stage Descriptors

The third element of the energy culture maturity conceptual framework requires establishing different maturity stage descriptors for each maturity stage against the previously developed factor categorisation in Table 5. The maturity descriptors are the descriptions of energy culture in the different maturity stages against the available factor categories. Antunes et al. [63] proposed developing maturity descriptors using best practices found in the literature. This study also incorporated a similar approach to developing energy culture maturity descriptors for the three stages. The drivers and the barriers identified for the seven contexts in Table 5 best describe the energy culture features in given contexts. For instance, drivers in CB presents the best practices of excellent energy cultures, whereas barriers in CC show the factors of an energy culture that has obstructed adopting energy-efficient technologies. Hence, this study uses the drivers and barriers from seven contexts to develop the energy culture maturity descriptors. Accordingly, maturity descriptors were developed for all three stages, including some projections when direct drivers or barriers were not clearly available. Accordingly, each subfactor in the factor categorisation owns maturity descriptors for all three maturity stages. When there is no subfactor, the main factor directly relates to the maturity descriptor.

4.2.4. Development of the Energy Culture Maturity Conceptual Framework

Ultimately, the development of the energy culture maturity framework integrates previous findings under factor categorisation, energy culture maturity stages and energy culture maturity descriptors. Accordingly, Figure 7 and Table 6 jointly present the developed energy culture maturity conceptual framework by integrating previous findings. Figure 7 depicts the integration of the three-staged energy culture maturity. Each stage represents the interaction between norms (N), practices (P), and material culture (M) as the three components of energy culture. This relationship is illustrated using two-way arrows that connect each of the components. The framework’s maturity moves from Stage 1, the lowest energy culture maturity, to Stage 3, the topmost energy culture maturity. The green arrows illustrate the possible trajectory of energy culture maturity towards energy culture excellence. The three stages of energy culture maturity are defined below, considering the overall support of energy culture for adopting energy-efficient technologies.
Figure 7. Energy culture maturity conceptual framework (Source: Author developed based on energy cultures framework [43] and Finnerty et al. [56]).
Table 6. Factors and maturity stage descriptors of the framework.
  • Stage 1 (S1): Energy cultures that obstruct the adoption of energy-efficient technologies.
  • Stage 2 (S2): Energy cultures that support the adoption of energy-efficient technologies to some extent. There is still room for improvement in terms of reaching energy culture excellence.
  • Stage 3 (S3): Energy cultures that support at the best level for adopting energy-efficient technologies.
Table 6 outlines a detailed integration of the energy culture maturity conceptual framework in the form of a matrix table consisting of factor categorisations presented in rows and energy culture maturity stages outlined in columns. The factor categorisation’s main factors or subfactors link with the respective maturity descriptors under three stages. Furthermore, the three maturity stages vertically distribute the maturity descriptors of each stage. Thus, the maturity descriptors are shared by the factor categorisations and the three maturity stages to form the energy culture maturity conceptual framework.
The conceptual framework elaborates norms, practices, and material culture. The norms section of the framework consists of 10 main factors, such as the green values of owners, top management commitments, energy policies and strategies, a focus on a green marketplace, knowledge of energy-efficient technologies, readiness for energy-efficient technologies, attention to economic benefits, prioritisation of energy-efficient technologies, presence of internal politics, and industry norms acceptance. Moreover, the practices section comprises two main factors: changing business practices for energy-efficient technologies and industry practices acceptance. Lastly, the material culture section includes the availability of energy-efficient technologies and industry material cultures acceptance as the two main factors. In addition, all 11 subfactors belong to the norms section. The subfactors include investment return analysis, operational cost savings focus, whole organisation readiness for energy-efficient technologies, willingness for energy-efficient technologies, actively seeking energy-efficient technologies, levels of acceptance for energy-efficient technologies, energy priorities in procurement criteria, supportive key performance indicators, supportive energy policies and strategies, knowledge on benefits of energy-efficient technologies, and the suitability of new energy-efficient technologies. Furthermore, the energy culture maturity descriptors of a given main factor or subfactor explain its maturity in the three stages. Overall, the conceptual framework outlines the structure to assess the energy culture maturity in three stages against the factors and subfactors.

5. Conclusions

As evidenced by the existing literature, an energy culture maturity framework is absent. Therefore, this study contributed to developing the first energy culture maturity conceptual framework on adopting energy-efficient technology innovations. This pioneering framework has significantly contributed to the scientific literature by adding a three-staged energy culture maturity conceptual framework to the energy culture research area, which can be identified as a novel contribution.
To date, the existing energy culture scholarship has not focused on holistic energy culture maturity and has not provided an energy culture roadmap for adopting energy-efficient technology innovations in buildings. This framework can be used to understand the current maturity status and provide a roadmap for reaching energy culture excellence, which continually improves the adoption of energy-efficient technology innovations in buildings. Furthermore, the proposed framework also provides policy implications. The framework can be used by the energy policy-related regulatory bodies to assess the energy culture maturity of different industries and organisations within industries. As a result of such a maturity assessment at the organisational or industrial level, benchmarking and baselining ability are acquired by the regulatory bodies as well as the organisations. Accordingly, the energy regulatory bodies can use the framework as a roadmap to guide organisations to achieve higher maturity levels, towards energy cultural excellence. Hence, the overall result increases the diffusion of energy-efficient technology innovations that support the demand-side management-related policy deployments.
Development of this framework was solely based on a scoping literature review and was limited to three maturity stages due to the limited literature, compared to the widely available maturity models with five maturity levels. As a result of this limitation, the framework’s ability to provide the energy culture maturity assessment results will be limited to three stages. However, this limitation may not be a major obstacle to using the proposed framework since there are other scientific maturity models in the literature that are also limited to three levels. Further, the energy culture articles considered when developing the framework were limited to three Anglosphere countries. Hence, further empirical studies should be conducted based on the different countries and organisations to add additional scholarly value to this research area. Currently, an ongoing study is exploring the energy culture maturity of the textile and apparel industry in a developing country. Therefore, it would be fruitful to further research by empirically applying this novel framework to establish the energy culture maturity research approach.

Author Contributions

Conceptualization, D.S., G.K., U.K., M.N.M. and L.D.S.; methodology, D.S., G.K., U.K. and M.N.M.; software, D.S.; formal analysis, D.S., G.K., U.K. and M.N.M.; writing—original draft preparation, D.S.; writing—review and editing, D.S., G.K., U.K. and M.N.M.; visualization, D.S. and G.K.; supervision, G.K., U.K., M.N.M. and L.D.S.; project administration, G.K., U.K., M.N.M. and L.D.S. All authors have read and agreed to the published version of the manuscript.

Funding

This article is based on the ongoing PhD study of the corresponding author. The first year of the PhD scholarship is partially funded by the World Bank-funded Accelerating Higher Education Expansion and Development programme in Sri Lanka (reference no: AHEAD/PhD/R1-PART-2/ENG-TECH/097) and Deakin University, Australia. The APC was not funded by any of the above funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors would like to acknowledge the support from Cristina Santa-Isabel by proofreading the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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