Next Article in Journal
Circular Business Model Value Dimension Canvas: Tool Redesign for Innovation and Validation through an Australian Case Study
Previous Article in Journal
Sustainable Assessment Tools for Higher Education Institutions: Developing Two-Hierarchy Tools for China
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 15
Issue 15
10.3390/su151511552
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Energy Audit as an Instrument to Tackle Internal Barriers to Energy Efficiency: Lessons from Moroccan Industrial Firms

by
Mehdi Bensouda
1,*,
Mimoun Benali
1,
Ghada Moufdi
2,
Taoufik El Bouzekri El Idrissi
1 and
Abdelhamid El Bouhadi
1
1
Laboratory of Research and Studies in Management, Entrepreneurship and Finance (LAREMEF), National School of Commerce and Management of Fez, Sidi Mohamed Ben Abdellah University, Fez 30050, Morocco
2
Studies and Research in Management of Organizations and Territories (ERMOT), Faculty of Legal, Economics and Social Sciences, Sidi Mohamed Ben Abdellah University, Fez 30050, Morocco
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(15), 11552; https://doi.org/10.3390/su151511552
Submission received: 27 May 2023 / Revised: 9 July 2023 / Accepted: 13 July 2023 / Published: 26 July 2023
Browse Figures
Versions Notes

Abstract

:
Due to climate change, firms are encouraged to introduce various measures to enhance both their competitiveness and sustainability, particularly energy efficiency measures (EEMs). Energy efficiency is particularly important in energy-intensive sectors such as the industrial sector. However, EEMs within industrial firms are hindered by several internal barriers such as competing interests within firms, lack of information regarding energy efficiency opportunities, and low technical competence. In this regard, energy audits aim to improve energy efficiency in facilities and to tackle internal barriers to energy efficiency. We developed a model that seeks to investigate the significance of energy audits in the implementation of EEMs and to reduce the intensity of internal barriers to energy efficiency. Our research model was empirically tested via data collected from a survey conducted with 193 industrial firms in the Kingdom of Morocco. Results show that competing interests, lack of information, and low technical competence hinder the adoption of EEMs within industrial firms, which aligns with findings from previous studies. In addition, our findings indicate that energy audits enhance EEMs and mitigate the negative effect of lack of information and low technical competence on the adoption of EEMs, which is consistent with findings from prior studies. However, our results demonstrate that energy audits do not attenuate the negative effect of competing interests on EEMs; this contrasts with findings from several previous studies. Therefore, our study builds upon prior research and contributes new insights regarding the importance of energy audits in tackling internal barriers to energy efficiency.

1. Introduction

Climate change is an urgent and significant issue of the modern era [1]. This multifaceted challenge impinges upon a wide range of areas, including politics, the business world, and personal choices [1]. In this regard, energy efficiency is paramount to decreasing firms’ climate footprints while enhancing their competitiveness [2]. Energy efficiency refers to the process of reducing energy consumption to its lowest possible level without compromising quality of production, profitability, and quality of life [3,4].
A substantial body of the existing literature points to energy efficiency multiple benefits at the firm level [5,6,7,8]. However, energy efficiency potential is still untapped [2], which means that there is a difference between energy efficiency potential and the current energy efficiency level [1].
Factors hindering the adoption of EEMs contribute to the energy efficiency gap; these factors are referred to as energy efficiency barriers [9].
The empirical literature has found that several barriers hinder energy efficiency within firms, including financial factors [10], organizational factors [11], and behavioral factors [9,12]. Energy efficiency barriers could be classified into internal and external barriers to energy efficiency [13]. External barriers to energy efficiency are associated with institutional settings, while internal barriers are linked to internal capabilities and behaviors [14].
In the current study, we focused on the effect of internal barriers to energy efficiency on the implementation of EEMs, namely the competing interests within firms, the lack of information regarding energy efficiency, and the low technical competence within firms.
Competing interests could impede the adoption of EEMs within industrial firms by attaching a high importance to expanding production capacity to the detriment of energy issues that could also be beneficial [11].
The existing literature assumes that competing interests—also referred to as diverging priorities—are one of the main barriers to energy efficiency [15]. However, for some authors, competing interests are not a weighty barrier to energy efficiency [16]. We believe that competing priorities within firms’ departments have a negative effect on the adoption of EEMs within firms.
Furthermore, prior research states that the lack of information concerning energy efficiency opportunities leads to non-optimal decisions regarding energy issues or even the non-implementation of EEMs [13,17]. In this regard, the lack of information should be negatively related to the adoption of EEMs within firms.
Low technical competence within industrial firms is noticeable when it comes to sensing energy efficiency opportunities and integrating energy efficiency measures into the existing internal processes [13]. In this regard, previous studies have shown that low technical competence and expertise constitute significant barriers to energy efficiency [18,19]. From this point of view, lack of competence should be negatively related to the adoption of EEMs within firms.
Our research focused on examining the effect of competing interests, lack of information, and low technical competence on the implementation of EEMs within the Moroccan industrial sector. The Moroccan industrial sector was chosen for the following reasons:
First, the industrial sector is well known for being an energy-intensive sector, a sector that could play a central role in decreasing global energy consumption and in closing the energy efficiency gap [20]. Second, in the Moroccan context, the industrial sector consumes one quarter of the overall energy production [2]. It is therefore relevant to explore the effect of internal barriers to energy efficiency within the Moroccan industrial sector in order to determine the most appropriate means to reduce their intensity.
Furthermore, our research explored the role of energy audits in tackling internal barriers to energy efficiency.
An energy audit is defined as an analysis of energy use within a firm’s facilities in order to assess energy efficiency opportunities and to identify avenues for energy savings [21]. Energy audits are powerful vehicles for encouraging firms to undertake energy efficiency investments [22]. Energy audits are important within the industrial sector for the following reasons:
To begin with, energy audits lead to cost savings within firms via lowering cost of maintenance, tracking energy savings, and enhancing equipment age [23]. Furthermore, energy audits promote greater comforts within the workplace. Moreover, energy audits help firms to comply with current or potential regulations [23]. After a literature review on EEMs, we found that several studies have considered energy audits to be excellent instruments to overcome internal barriers to energy efficiency, including information barriers [22], competing interests, and competence related barriers [24]. Thus, we believe that energy audits are positively related to the adoption of EEMs within industrial firms.
Overall, this study explores the following factors: first, the negative direct effect of competing interests, lack of information, and low technical competence on the adoption of EEMs within the Moroccan industrial sector; second, the positive direct effect of energy audits on the adoption of EEMs within the Moroccan industrial sector; finally, the indirect moderating effect of energy audits on mitigating the negative effect of internal barriers to energy efficiency on the adoption of EEMs within the Moroccan industrial sector.
To the best of our knowledge, no empirical study in Morocco has put the emphasis on the role of energy audits on mitigating the negative effect of competing interests, lack of information, and low technical competence on the adoption of EEMs within Moroccan industrial firms [25], which constitutes the originality of our study.
In the second section, we present our research model, the theorical background, and our hypotheses. In the third section, we explain the method of the study. Then, in the fourth section, we present our results. Subsequently, in the fifth section, we discuss the findings of the study. Finally, in the sixth section, we elaborate on our study’s policy implications.

2. Theorical Background and Research Hypotheses

We built a model that examined the effect of competing interests, lack of information, and low technical competence on EEMs’ adoption. We also explored the effect of energy audits on mitigating the negative effect of the aforementioned internal barriers on EEMs (See Figure 1).
From Figure 1, competing interests, lack of information, and low technical competence are considered as potential barriers to EEMs. Moreover, energy audits could potentially enhance EEMs within firms and could dampen the negative effect of the aforementioned internal barriers on EEMs.

2.1. Internal Barriers to Energy Efficiency

The existing literature posits the existence of an untapped energy efficiency potential [22]; this potential is referred to as the energy efficiency gap [26]. The energy efficiency gap is the difference between the theoretical energy efficiency potential and the current energy efficiency level [27].
The energy efficiency gap is attributed to the existence of energy efficiency barriers [28].
Several studies have explored and categorized energy efficiency barriers across different categories [29]. Energy efficiency barriers could be related to neoclassical economics, behavioral economics, and psychology [29]. Energy efficiency barriers could also be related to consumers, governments, and financial institutions [30]. In addition, energy efficiency barriers could be institutional, organizational, or behavioral [31].
Energy efficiency barriers could be internal (within firms) and external (with respect to firms) [13]. Internal barriers to energy efficiency are associated with internal competence and individual behaviors, while external barriers are linked to institutional and regulatory framework [14]. In this paper, the emphasis was placed on internal barriers.
Internal barriers arise not only from the lack of managerial competence and awareness and the lack of management commitment to energy efficiency issues but also from irrational behaviors and individual preferences within firms [14,32].
Several energy efficiency barriers arise from firms, such as competing interests within firms and other priorities [33], the lack of information regarding energy efficiency technologies [34], and low technical competence within firms [35].

2.1.1. Competing Interests

Competing priorities or interests or conflicts of interests are undoubtedly considerable internal barriers to energy efficiency within the industrial sector [24,36]. The existence of competing priorities hinders EEMs by impeding the adoption of long-term perspectives and by limiting firms’ abilities to achieve their energy efficiency potential [15,36].
In this regard, industrial firms tend to place a higher priority on production activities [11]. Industrial firms consider the main value stream to be production, which explains the importance they attach to expanding their production capacity and increasing their market share [11]. By emphasizing production activities, industrial firms’ management tends to neglect EEMs [37].
Numerous papers have addressed how the existence of competing interests within industrial firms could impede EEMs [11,38,39]; therefore, Hypothesis 1 is as follows:
H1. 
The existence of competing interests has a negative effect on the adoption of EEMs.

2.1.2. Lack of Information

The lack of information regarding energy efficiency technologies is a prominent barrier to energy efficiency [40].
The lack of information could be explained by the existence of transaction costs related to the collection and analysis of information on products and suppliers [32]. Furthermore, the lack of information could be a result of individual inattention or the existence of constraints when assessing available information such as time constraints [14].
The adoption of EEMs is often hindered by a firm’s lack of information [41]. As a best-case scenario, the lack of information results in decision making based solely on the most noticeable features, which ultimately leads to non-optimal decisions [13,17].
Several studies have examined the negative effect of poor information regarding the benefits and costs, technologies, and suppliers of energy efficiency on implementing EEMs [42,43]. Thus, Hypothesis 2 is as follows:
H2. 
The lack of information regarding energy efficiency has a negative effect on the adoption of EEMs.

2.1.3. Low Technical Competence

Technical competence and expertise are paramount to implementing EEMs [40]. In this regard, low technical competence within a firm delays the adoption of EEMs [44] and could lead to the non-implementation of EEMs [45].
Barriers related to internal competence are materialized by inefficient diagnoses related to a firm’s energy needs, inefficiencies in sensing energy efficiency opportunities, difficulties regarding the integration of EEMs into the existing internal processes, and the difficulty of attracting external competent human resources [13].
These barriers point to a management lack of awareness regarding energy efficiency opportunities [46] and to inadequate management capacity [11,13].
Various studies have highlighted the impact of low technical competence within firms on impeding EEMs [13,18,19,39,47]. Thus, we propose the following hypothesis:
H3. 
The low technical competence regarding energy efficiency has a negative effect on the adoption of EEMs.

2.2. Energy Audits

Energy audits are processes by which an auditor evaluates how facilities use different forms of energy, examines opportunities, and determines different routes for energy savings and areas for energy efficiency [21]. Energy audits do not result in energy savings [48] but rather constitute a first step toward unlocking the untapped energy efficiency potential [49]. Thus, energy audits are great tools for encouraging firms to invest in energy efficiency projects [22].
Energy audits within companies could be financed by energy services companies (ESCOs) [50]. Energy services companies (ESCOs) are specialized firms that provide energy-efficient solutions and services to help clients reduce energy consumption and improve energy performance [50]. ESCOs have the following specificities: they facilitate or arrange financing for energy audits based on the achieved energy savings and they receive remuneration based on the achieved energy savings [50]. Therefore, the ESCO market is a valuable tool for promoting energy efficiency [51]. Therefore, energy auditors play a central role in the energy audit’s process [52]. The certification of energy auditors is regulated by well-recognized international standards [52].
The promotion of energy audits enhances energy efficiency and is vital to address the energy efficiency gap within firms [53] (Fleiter et al., 2012), not only for small and medium enterprises (SMEs) but also for large companies [54,55] (Redmond and Walker, 2016; Lisauskas et al., 2022).
In this regard, the energy efficiency directive in the UE requires large companies within the most energy intensive sectors to undergo mandatory energy audits [56] (Nabitz and Hirzel, 2019). Mandatory energy audits are important to enhance energy performances of industrial firms, particularly the ones within the most energy intensive sectors [57] (Herce et al., 2022). The adoption of mandatory energy audits is also crucial for large companies within the industrial sector [58] (Locmelis et al., 2020).
Even if few studies have found that energy audits might deter audit recipients to invest in energy efficiency when the perceived breakeven point is reached only after a long period of time [59,60], a vast body of the existing literature has shown that energy audits are critical to improve industrial firms’ energy management [22,61,62,63].
We believe that energy audits promote EEMs within industrial firms. Therefore, we have the following hypothesis:
H4. 
Energy audits have a positive effect on the adoption of EEMs.
Energy audits reduce the intensity of energy efficiency barriers [35,49]. Energy audits could overcome competing interests and the low priority given to energy issues within firms by increasing employees’ involvement [24]. Energy audits enable employees’ early commitment through the multiple cross-functional meetings [24]. Thus, we believe that energy audits reduce the negative effect of firms’ competing priorities regarding the adoption of EEMs.
H5. 
Energy audits dampen the negative effect of competing interests on the adoption of EEMs.
Conducting energy audits is considered an important tool for decreasing the intensity of energy efficiency barriers, including the lack of information regarding energy efficiency opportunities [22,49]. Therefore, we have the following hypothesis:
H6. 
Energy audits dampen the negative effect of the lack of information on the adoption of EEMs.
Energy audits could tackle internal barriers to energy efficiency, including low technical competence within firms [24]. By pinpointing the various inefficiencies, installing energy management software, introducing energy KPIs, defining employees’ roles, and setting training programs, energy audits are most likely to overcome internal barriers related to competence [24]. We believe that energy audits mitigate the negative effect of low technical competence within firms regarding EEM.
H7. 
Energy audits dampen the negative effect of low technical competence on the adoption of EEMs.
Figure 2 presents our research model and visualizes our seven research hypotheses.

3. Method

3.1. Measurement Development and Data Collection

Survey studies necessitate a carefully designed questionnaire. To ensure the quality of the questionnaire, several actions were taken. The development of measurements of constructs was informed by prior research [45,64] (see Table A1). We used simple and concise wordings, excluding words that could be seen as offensive. Afterwards, we started the pretest phase. Then, based on respondents’ feedback, minimal refinements were made, resulting in the final version of our questionnaire.
Data collection lasted four months, from May to August 2022. Questionnaires were made available to employees in industrial firms based in four regions: “Fez-Meknes”, “Tangier-Tetouan-Al Hoceïma”, “Casablanca-Settat”, and “Rabat-Sale-Kenitra”. We selected geographical areas comprising the main manufacturing cities of Morocco.
Further to the data collection, 193 usable questionnaires were obtained. Most of the respondents were employees within a variety of departments such as production, finance, etc., who were arguably capable of providing answers regarding EEMs [64].
Our sampling constituted industrial firms that belonged to energy intensive sectors, composed of firms that implemented energy audits. These sectors were: automotive, energy, aircraft parts, and chemical and parachemical, as well as other sectors such as food processing and textiles. A total of 140 of our respondents were large companies; 53 of them were SMEs. Figure A1, Figure A2, Figure A3, Figure A4 and Figure A5 show how the size of companies impacted their answers regarding the adoption of EEMs, the adoption of energy audits, and the weight of internal barriers to energy efficiency.

3.2. Data Analysis Method

Once the data were collected, we employed the partial least square (PLS) technique and the software SmartPLS 3 to test the model and the research hypotheses. PLS was convenient to assess models with latent constructs and moderating effects [65,66].
Data analysis through PLS-SEM comprised two major steps [54]: the measurement model and the structural model. The measurement model examined the connection between variables/latent constructs and their corresponding items/measures. The structural model examined the connection among the various variables/latent constructs.
The measurement model assessment involved conducting several tests, namely factor loadings, Cronbach’s alpha, composite reliability, and average variance extracted. These tests were important for determining the validity and the reliability of the model by evaluating the strength and consistency of the relationships between indicators/items and their latent constructs. In addition, the measurement model assessment entailed performing tests such as the Fornell and Larcker criterion and the heterotrait–monotrait ratio of correlations in order to establish the divergent validity of the model by examining the correlations between different constructs and ensuring that they were lower than the correlations between items within the same construct.
The structural model assessment involved conducting several tests, namely R squared, with the aim of measuring the amount of variance explained by the endogenous latent variables in the model. Q squared was also important in the structural model assessment in order to estimate the predictive relevance of the model by measuring the amount of variance in the endogenous latent variables that could be predicted by the exogenous latent variables. Furthermore, the standardized root mean square residual (SRMR) was conducted to assess the overall goodness of fit of the measurement model by evaluating the discrepancies between the observed and predicted covariances in the model. Finally, the structural model assessment required the performing path coefficient to determine the strength and significance of the relationships between the latent variables in the structural model. In our research, SmartPLS allowed us to assess the connection between our five latent variables and their observed indicators/measures. In addition, SmartPLS allowed us to analyze the negative direct effects, which referred to the effects of the independent variables, namely competing interests, lack of information, and low technical competence, on the dependent variable (which was EEMs). Moreover, SmartPLS allowed us to analyze the positive direct effects, which referred to the effects of the independent variable energy audit, on the dependent variable (which was EEMs). Furthermore, SmartPLS allowed us to explore the indirect moderating effect, which referred to the effect of the energy audit as a variable that could reduce the intensity of the aforementioned internal barriers on the adoption of EEMs. Ultimately, SmartPLS allowed us to quantitatively assess and validate their hypotheses using collected data.

4. Results

4.1. Assessment of Measurement Model

4.1.1. Convergent Validity

To test the measurement model’s convergent validity, we began by assessing factor loadings. Factor loadings should be higher than 0.7 [67,68]. Table 1 indicates that all factor loadings that we kept were greater than 0.7. Thus, all measures were sufficiently correlated to their corresponding latent variables.
In addition, we determined latent variables’ Cronbach’s alpha (CA) and composite reliability (CR). As indicated in Table 2, the five latent variables of our model had a Cronbach’s alpha higher than the 0.7 threshold [69]. Our five latent variables also had a composite reliability greater than the recommended value of 0.7 [70]. Consequently, the five constructs of our model were internally consistent [71].
Furthermore, we determined the average variance extracted (AVE) for each construct. All five constructs had an AVE greater than the advised value of 0.5 [72]. Thus, all five latent variables of our model accounted for their corresponding measures [73].
Taken together, the convergent validity of the model was supported.

4.1.2. Discriminant Validity

To test the measurement model’s discriminant validity, we conducted both the Fornell and Larcker criterion and the heterotrait–monotrait ratio of correlations (HTMT ratio) [74,75].
Table 2 shows that diagonal values corresponding to the square root of AVE were greater than inter-construct correlations. Therefore, all latent variables explained better the variance of their respective measures compared with the variance of the remaining latent variables.
HTMT was also conducted to test the measurement model’s discriminant validity [76]. Table 3 indicates that all HTMT values were inferior to the recommended value of 0.85 [74,77].
Based on the results of both the Fornell and Larcker criterion and the HTMT ratio, the discriminant validity of our measurement model was stated.

4.2. Assessment of Structural Model

4.2.1. Direct Effect

Data analysis through PLS-SEM comprised two major phases. Following the evaluation of the measurement model, we proceeded with the evaluation of the structural model. We started by determining R² value for our dependent variable. R2 represented the fraction of variation in the endogenous variable, which could be accounted for by the combination of the independent variables [67]. Table 4 shows that R2 for our dependent variable “energy efficiency measures” was higher than 0.1 [78]. Therefore, the construct’s energy audits, competing interests, lack of information, and low technical competence together accounted for 58.2 percent of the dependent construct energy efficiency measures.
Moreover, we determined Q2 for our dependent construct. Table 4 shows that Q2 for the dependent construct was greater than 0 [79]. Thus, the model’s predictive validity was established.
To establish the model fit, we used the standardized root mean square residual (SRMR). Table 5 shows that the SRMR value was between 0 and 0.08, corresponding to the desirable range for SRMR [80] and signifying an adequate model fit.
Afterwards, we tested our research hypotheses. As indicated in Table 6, competing interests had a negative direct effect on the adoption of EEMs (beta = −0.331, t-value = 4.513, p-value = 0.000); H1 was then supported. The lack of information regarding energy efficiency opportunities had a negative direct effect on the adoption of EEMs (beta = −0.136, t-value = 2.055, p-value = 0.000); H2 was then also supported. Low technical competence within firms had a negative direct effect on the adoption of EEMs (beta = −0.191, t-value = 2.273, p-value = 0.000); thus, H3 was supported. H4, which assumed that energy audits had a positive direct effect on the adoption of EEMs, was also supported (beta = 0.425, t-value = 7.217, p-value = 0.000).

4.2.2. Indirect Moderating Effect

We also tested indirect moderating effects. The research model hypothesized that:
-
Energy audits mitigate the negative effect of competing interests on the adoption of EEMs.
-
Energy audits attenuate the negative effect of the lack of information on the adoption of EEMs.
-
Energy audits moderate the negative effect of low technical competence on the adoption of EEMs.
From Table 7, H5 (which posited that energy audits mitigate the negative effect of competing interests on the adoption of EEMs) was not supported (beta = 0.037, t-value = 1.781, p-value = 0.076). Energy audits attenuated the negative effect of the lack of information on the adoption of EEMs (beta = 0.365, t-value = 6.926, p-value = 0.000); H6 was then supported. Energy audits moderated the negative effect of low technical competence on the adoption of EEMs (beta = 0.321, t-value = 6.609, p-value = 0.000); H7 was also supported.
Results of direct and indirect moderating analysis “structural model” are presented in Figure 3.

5. Discussion

5.1. Discussion of Expected Results

This study emphasized the significance of energy audits as a valuable tool to mitigate the impact of internal barriers on energy efficiency, namely competing interests, lack of information, and low technical competence. After empirically testing our research model, the following findings were obtained:
H1, H2, H3, H4, H6, and H7 were consistent with our prior expectations and with the previous literature.
(1)
Competing interests have a negative direct effect on the adoption of EEMs. This could be explained by the fact that industrial firms tend to place their main attention on enhancing their productivity to the disadvantage of energy related issues, including the adoption of EEMS, even if these measures could be beneficial and could generate significant energy savings [11,15].
(2)
Lack of information regarding energy efficiency opportunities has a negative direct effect on the adoption of EEMs, meaning that the lack of information concerning energy efficiency opportunities leads to non-optimal decisions regarding energy issues [13,17].
(3)
Low technical competence has a negative direct effect on the adoption of EEMs. This result could be explained by management’s lack of awareness concerning energy efficiency opportunities [46] and also by inadequate managerial capacity [11].
(4)
Energy audits have a positive direct effect on the adoption of EEMs. One possible explanation is that, by implementing energy audits, firms determine energy efficiency opportunities and identify avenues for energy savings [21]. Therefore, energy audit results are likely to encourage firms to undertake energy efficiency investments [22].
(5)
Energy audits attenuate the negative effect of the lack of information regarding energy efficiency opportunities on the adoption of EEMs. One possible explanation is that energy audits create and share knowledge regarding technology options and their respective cost savings potential, which is likely to lead to the implementation of EEMs [22].
(6)
Energy audits attenuate the negative effect of low technical competence on the adoption of EEMs. One possible explanation is that energy audits upgrade employees’ technical competence through the introduction of energy KPIs, energy management software, and training programs [24].
It is noteworthy that these findings differed according to the size of firms within our sample. First, the intensity of internal barriers to energy efficiency was more significant for SMEs. One possible explanation is that non-energy-intensive SMEs tend to experience more internal barriers to energy barriers because they are compromised by their limited internal/managerial capabilities compared with big companies [33].
Second, big companies are more likely to adopt EEMs following the completion of energy audits. One plausible explanation is that employees within SMEs tend to be less trained to implement energy audit recommendations, which therefore jeopardizes the success of energy audits [81]. Another plausible explanation is that SMEs and non-energy-intensive firms receive less attention by policies in terms of audit subsidies, which makes the adoption of energy audit recommendations complicated due to cost considerations [82].

5.2. Discussion on Unexpected Results

However, H5 was not supported:
Energy audits did not attenuate the negative effect of competing interests on the adoption of EEMs. This finding was divergent from our prior prediction and from the previous literature that states that energy audits tackle internal barriers to energy efficiency, including competing/divergent interests [24]. Our findings extended prior research, as we found that, even if firms implemented energy audits and raised awareness regarding energy efficiency opportunities, they did not consistently attenuate the negative effect of competing interests between energy managers and production managers regarding the adoption of EMMs.
Table 8 indicates that H1, H2, H3, H4, H6, and H7 were consistent with prior research, whereas H5 was inconsistent with the previous research, summarizing our research findings and categorizing the research hypotheses and findings as either consistent or inconsistent with the previous literature.

6. Conclusions and Implications

In this research, we built a research model with the aim of determining the effect of energy audits on reducing the intensity of internal barriers to energy efficiency, namely competing interests, lack of information regarding energy issues and opportunities, and low technical competence. We collected data from 193 industrial firms in four Moroccan regions. We analyzed the collected data using the software SmartPLS 3; then, we tested our hypotheses. The following findings were obtained:
-
Competing interests within firms’ departments, lack of information regarding energy issues and opportunities, and employees’ lack of technical competence hinder the adoption of EEMs.
-
Energy audits lead to the implementation of EEMs. Energy audits are instruments that facilitate the adoption of EEMs within firms through the assessment of energy efficiency potential and the identification of new routes for energy savings.
-
Energy audits mitigate the negative effect of lack of information and low technical competence regarding the adoption of EEMs. Therefore, energy audits not only compensate firms’ lack of information regarding the best energy efficiency measures and technologies by providing tailored recommendations but also compensate employees’ lack of technical competence through expert guidance and support.
-
Energy audits do not attenuate the negative effect of competing interests regarding the adoption of EEMs. Despite the implementation of energy audits, competing interests could influence the decision-making process regarding the adoption of energy audit recommendations.
Based on our results, our research has theorical and policy implications.
Theorical implications: Our research reinforces previous studies with additional confirmation regarding the importance of energy audits as a factor that reduces the intensity of internal barriers to energy efficiency. Furthermore, our research invalidates the fact that energy audits reduce the intensity of competing interests within firms regarding the adoption of EEMs. In this regard, our study extends prior research.
Policy implications: because of the importance of energy audits in tackling internal barriers to energy efficiency, policy makers could carry out the following:
Regarding big companies, policy makers could promote energy audits among firms using information tools, namely energy efficiency networks [22]. Energy efficiency networks facilitate an early involvement of all pertinent stakeholders related to EEMs, including local administrations, ESCOs, and financial institutions [22].
Regarding non-energy-intensive SMEs, and based on our findings, policy makers could first provide training programs in favor of employees, aimed at enhancing their understanding of energy-efficient technologies and assisting in the implementation of energy audit recommendations [81]. Also, to help SMEs to implement energy audits’ recommendations, policy makers could provide audit subsidies to SMEs [82].

Author Contributions

Conceptualization, M.B. (Mehdi Bensouda), M.B. (Mimoun Benali), G.M., T.E.B.E.I. and A.E.B.; methodology, M.B. (Mehdi Bensouda), M.B. (Mimoun Benali), G.M., T.E.B.E.I. and A.E.B.; software, M.B. (Mehdi Bensouda); validation, M.B. (Mimoun Benali); formal analysis, M.B. (Mehdi Bensouda); investigation, M.B. (Mehdi Bensouda); resources, M.B. (Mimoun Benali); data curation, M.B. (Mehdi Bensouda); writing—original draft preparation, M.B. (Mehdi Bensouda); writing—review and editing, G.M., T.E.B.E.I. and A.E.B.; visualization, M.B. (Mehdi Bensouda); supervision, M.B. (Mimoun Benali), G.M., T.E.B.E.I. and A.E.B.; project administration, M.B. (Mimoun Benali). All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Wordings for study’s latent constructs.
Table A1. Wordings for study’s latent constructs.
Latent ConstructsIndicatorsPhrasing
Energy Efficiency Measures EEM1Your company incorporates energy-efficient practices into its daily work routines (efficient lighting, HVAC systems, etc.).
EEM2Employees are encouraged and incentivized to adopt energy-saving behaviors and practices in your daily work routines.
EEM3Energy-efficient technologies and equipment are actively utilized within your company’s operations.
EEM4Renewable and clean energy sources, such as solar panels, are utilized within your company to minimize energy consumption.
EEM5Your company monitors and analyzes energy consumption data to identify areas for EE improvement.
Energy AuditsEA1Your company conducts comprehensive energy audits to identify areas for EE improvements.
EA2Employees are involved in the energy audit process and contribute their insights and observations.
EA3The recommendations from energy audits are taken seriously and are used to guide decision making for EEMs within your company.
EA4Your company allocates resources specifically for the implementation of energy audit recommendations to achieve energy savings.
Competing InterestsCI1It is challenging to balance the focus on EE with the need to meet production targets.
CI2Your company prioritizes production demands over EE goals.
CI3Limited resources are allocated to EE due to competing production-related demands.
Lack of InformationLI1Your company does not have a process in place to identify potential energy efficiency opportunities.
LI2Your company has limited access to up-to-date information about EE technologies.
LI3The limited availability of information about EE technologies and suppliers poses a challenge for your company in making well-informed decisions regarding their adoption.
Low Technical CompetenceLTC1You may need further support and development in acquiring the technical skills necessary for effective implementation of EEMs.
LTC2Your company does not invest in trainings to enhance the technical competence of its employees in relation to EE.
LTC3The effective implementation of EEMs within your company may be influenced by a lack of technical competence
Sustainability 15 11552 g0a1 550
Figure A1. Adoption ratio of energy efficiency measures (N = 193; large companies = 140; SMEs = 53).
Figure A1. Adoption ratio of energy efficiency measures (N = 193; large companies = 140; SMEs = 53).
Sustainability 15 11552 g0a1
Sustainability 15 11552 g0a2 550
Figure A2. Adoption ratio of energy audits (N = 193; large companies = 140; SMEs = 53).
Figure A2. Adoption ratio of energy audits (N = 193; large companies = 140; SMEs = 53).
Sustainability 15 11552 g0a2
Sustainability 15 11552 g0a3 550
Figure A3. Ratio of the internal barrier: competing interests (N = 193; large companies = 140; SMEs = 53).
Figure A3. Ratio of the internal barrier: competing interests (N = 193; large companies = 140; SMEs = 53).
Sustainability 15 11552 g0a3
Sustainability 15 11552 g0a4 550
Figure A4. Ratio of the internal barrier: lack of information (N = 193; large companies = 140; SMEs = 53).
Figure A4. Ratio of the internal barrier: lack of information (N = 193; large companies = 140; SMEs = 53).
Sustainability 15 11552 g0a4
Sustainability 15 11552 g0a5 550
Figure A5. Ratio of the internal barrier: low technical competence (N = 193; large companies = 140; SMEs = 53).
Figure A5. Ratio of the internal barrier: low technical competence (N = 193; large companies = 140; SMEs = 53).
Sustainability 15 11552 g0a5

References

  1. Solnørdal, M.T.; Foss, L. Closing the energy efficiency gap—A systematic review of empirical articles on drivers to energy efficiency in manufacturing firms. Energies 2018, 11, 518. [Google Scholar] [CrossRef] [Green Version]
  2. Bensouda, M.; Benali, M. Overcoming Risk Aversion Regarding Energy Efficiency Practices through Mimetic Pressure and Financial Slack: Findings from the Moroccan Manufacturing Sector. Sustainability 2022, 14, 16261. [Google Scholar] [CrossRef]
  3. Çengel, Y.A. Energy efficiency as an inexhaustible energy resource with perspectives from the US and Turkey. Int. J. Energy Res. 2011, 35, 153–161. [Google Scholar] [CrossRef]
  4. Lawrence, A.; Thollander, P.; Andrei, M.; Karlsson, M. Specific energy consumption/use (SEC) in energy management for improving energy efficiency in industry: Meaning, usage and differences. Energies 2019, 12, 247. [Google Scholar] [CrossRef] [Green Version]
  5. Russell, C. Multiple Benefits of Business-Sector Energy Efficiency: A Survey of Existing and Potential Measures; American Council for an Energy-Efficient Economy: Washington, DC, USA, 2015. [Google Scholar]
  6. Zhang, S.; Worrell, E.; Crijns-Graus, W. Mapping and modeling multiple benefits of energy efficiency and emission mitigation in China’s cement industry at the provincial level. Appl. Energy 2015, 155, 35–58. [Google Scholar] [CrossRef]
  7. Killip, G.; Fawcett, T.; Cooremans, C.; Wijns-Craus, W.; Subramani, K.; Voswinkel, F. Multiple Benefits of Energy Efficiency at the Firm Level: A Literature Review; European Council for an Energy Efficient Economy: Stockholm, Sweden, 2019. [Google Scholar]
  8. Lung, B.; Nimbalkar, S.U.; Wenning, T. Multiple Benefits of Industrial Energy Efficiency–Lessons Learned and New Initiatives; Oak Ridge National Laboratory (ORNL): Oak Ridge, TN, USA, 2019.
  9. Bensouda, M.; Benali, M. Behavioral Barriers to Energy Efficiency and Policy Interventions: A Survey of the Literature. Int. J. Energy Econ. Policy 2022, 12, 305. [Google Scholar] [CrossRef]
  10. Hill, D.R. Energy Efficiency Financing: A review of risks and uncertainties. In Proceedings of the Energy Challenges for the Next Decade, 16th IAEE European Conference, Ljubljana, Slovenia, 25–28 August 2019. [Google Scholar]
  11. Soepardi, A.; Thollander, P. Analysis of relationships among organizational barriers to energy efficiency improvement: A case study in Indonesia’s steel industry. Sustainability 2018, 10, 216. [Google Scholar] [CrossRef] [Green Version]
  12. Abrardi, L. Behavioral barriers and the energy efficiency gap: A survey of the literature. J. Ind. Bus. Econ. 2019, 46, 25–43. [Google Scholar] [CrossRef]
  13. Cagno, E.; Worrell, E.; Trianni, A.; Pugliese, G. A novel approach for barriers to industrial energy efficiency. Renew. Sustain. Energy Rev. 2013, 19, 290–308. [Google Scholar] [CrossRef]
  14. Cattaneo, C. Internal and external barriers to energy efficiency: Which role for policy interventions? Energy Effic. 2019, 12, 1293–1311. [Google Scholar] [CrossRef] [Green Version]
  15. Cardoso, B.J.; Gomes, Á.; Gaspar, A.R. Barriers and drivers to energy efficiency in the Portuguese water sector: Survey analysis. Appl. Energy 2023, 333, 120630. [Google Scholar] [CrossRef]
  16. Lee, K.H. Drivers and barriers to energy efficiency management for sustainable development. Sustain. Dev. 2015, 23, 16–25. [Google Scholar] [CrossRef]
  17. Hesselink, L.X.; Chappin, E.J. Adoption of energy efficient technologies by households–Barriers, policies and agent-based modelling studies. Renew. Sustain. Energy Rev. 2019, 99, 29–41. [Google Scholar] [CrossRef]
  18. Kangas, H.L.; Lazarevic, D.; Kivimaa, P. Technical skills, disinterest and non-functional regulation: Barriers to building energy efficiency in Finland viewed by energy service companies. Energy Policy 2018, 114, 63–76. [Google Scholar] [CrossRef] [Green Version]
  19. Hasan, A.M.; Rokonuzzaman, M.; Tuhin, R.A.; Salimullah, S.M.; Ullah, M.; Sakib, T.H.; Thollander, P. Drivers and barriers to industrial energy efficiency in textile industries of Bangladesh. Energies 2019, 12, 1775. [Google Scholar] [CrossRef] [Green Version]
  20. Bensouda, M.; Benali, M. The Role of Institutional Pressure and Dynamic Capabilities in Promoting Energy Efficiency Practices: Evidence from the Moroccan Manufacturing Sector. Int. J. Energy Econ. Policy 2023, 13, 352. [Google Scholar] [CrossRef]
  21. Thumann, A.; Younger, W.J. Handbook of Energy Audits; The Fairmont Press, Inc.: Lilburn, GA, USA, 2008. [Google Scholar]
  22. Kalantzis, F.; Revoltella, D. Do energy audits help SMEs to realize energy-efficiency opportunities? Energy Econ. 2019, 83, 229–239. [Google Scholar] [CrossRef]
  23. Katole, M.R.; Katole, M. Electrical energy audit and conservation. Int. J. 2016, 3, 162–166. [Google Scholar]
  24. Chiaroni, D.; Chiesa, V.; Franzò, S.; Frattini, F.; Manfredi Latilla, V. Overcoming internal barriers to industrial energy efficiency through energy audit: A case study of a large manufacturing company in the home appliances industry. Clean Technol. Environ. Policy 2017, 19, 1031–1046. [Google Scholar] [CrossRef]
  25. Mohamed, B. Stratégies de Développement Durable en Efficacité Energétique au Maroc. 2017. Available online: https://idl-bnc-idrc.dspacedirect.org/bitstream/handle/10625/58969/IDL%20-%2058969.pdf?sequence=2 (accessed on 7 March 2023).
  26. Koirala, B.S.; Bohara, A.K. Do energy efficiency building codes help minimize the efficiency gap in the US? A dynamic panel data approach. Energy Environ. 2021, 32, 506–518. [Google Scholar] [CrossRef]
  27. Dunlop, T. Mind the gap: A social sciences review of energy efficiency. Energy Res. Soc. Sci. 2019, 56, 101216. [Google Scholar] [CrossRef]
  28. Gerarden, T.D.; Newell, R.G.; Stavins, R.N.; Stowe, R.C. An Assessment of the Energy-Efficiency Gap and Its Implications for Climate-Change Policy (No. w20905); National Bureau of Economic Research: Cambridge, MA, USA, 2015. [Google Scholar]
  29. Della Valle, N.; Bertoldi, P. Promoting energy efficiency: Barriers, societal needs and policies. Front. Energy Res. 2022, 9, 804091. [Google Scholar] [CrossRef]
  30. Reddy, A.K. Barriers to improvements in energy efficiency. Energy Policy 1991, 19, 953–961. [Google Scholar] [CrossRef] [Green Version]
  31. Weber, L. Some reflections on barriers to the efficient use of energy. Energy Policy 1997, 25, 833–835. [Google Scholar] [CrossRef]
  32. Schleich, J.; Gassmann, X.; Faure, C.; Meissner, T. Making the implicit explicit: A look inside the implicit discount rate. Energy Policy 2016, 97, 321–331. [Google Scholar] [CrossRef]
  33. Jalo, N.; Johansson, I.; Andrei, M.; Nehler, T.; Thollander, P. Barriers to and drivers of energy management in Swedish SMEs. Energies 2021, 14, 6925. [Google Scholar] [CrossRef]
  34. Vakili, S.V.; Ballini, F.; Dalaklis, D.; Ölçer, A.I. A conceptual transdisciplinary framework to overcome energy efficiency barriers in ship operation cycles to meet IMO’s initial green house gas strategy goals: Case study for an Iranian shipping company. Energies 2022, 15, 2098. [Google Scholar] [CrossRef]
  35. Jalo, N.; Johansson, I.; Kanchiralla, F.M.; Thollander, P. Do energy efficiency networks help reduce barriers to energy efficiency?-A case study of a regional Swedish policy program for industrial SMEs. Renew. Sustain. Energy Rev. 2021, 151, 111579. [Google Scholar] [CrossRef]
  36. Svensson, A.; Paramonova, S. An analytical model for identifying and addressing energy efficiency improvement opportunities in industrial production systems–Model development and testing experiences from Sweden. J. Clean. Prod. 2017, 142, 2407–2422. [Google Scholar] [CrossRef]
  37. Cooray, N. Cleaner production assessment in small and medium industries of Sri Lanka. In Proceedings of the Global Competitiveness through Cleaner Production: Second Asia Pacific Cleaner Production Roundtable, Brisbane, Australia, 21–23 April 1999. [Google Scholar]
  38. Johansson, M.T.; Thollander, P. A review of barriers to and driving forces for improved energy efficiency in Swedish industry–Recommendations for successful in-house energy management. Renew. Sustain. Energy Rev. 2018, 82, 618–628. [Google Scholar] [CrossRef]
  39. Lawrence, A.; Thollander, P.; Karlsson, M. Drivers, barriers, and success factors for improving energy management in the pulp and paper industry. Sustainability 2018, 10, 1851. [Google Scholar] [CrossRef] [Green Version]
  40. Zafar, S.S. Barriers involve in the energy efficiency in the manufacturing industries of Pakistan. Int. J. Energy Econ. Policy 2021, 11, 293–299. [Google Scholar] [CrossRef]
  41. Sardianou, E. Barriers to industrial energy efficiency investments in Greece. J. Clean. Prod. 2008, 16, 1416–1423. [Google Scholar] [CrossRef]
  42. Bleischwitz, R.; Andersen, L.M. Informational Barriers to Energy Efficiency–Theory and European Policies. 2009. Available online: http://aei.pitt.edu/58504/1/beer15_(1).pdf (accessed on 15 February 2023).
  43. Giraudet, L.G. Energy efficiency as a credence good: A review of informational barriers to energy savings in the building sector. Energy Econ. 2020, 87, 104698. [Google Scholar] [CrossRef]
  44. Hrovatin, N.; Cagno, E.; Dolšak, J.; Zorić, J. How important are perceived barriers and drivers versus other contextual factors for the adoption of energy efficiency measures: An empirical investigation in manufacturing SMEs. J. Clean. Prod. 2021, 323, 129123. [Google Scholar] [CrossRef]
  45. Backman, F. Barriers to energy efficiency in Swedish non-energy-intensive micro-and small-sized enterprises—A case study of a local energy program. Energies 2017, 10, 100. [Google Scholar] [CrossRef] [Green Version]
  46. Trianni, A.; Cagno, E. Dealing with barriers to energy efficiency and SMEs: Some empirical evidences. Energy 2012, 37, 494–504. [Google Scholar] [CrossRef]
  47. Cagno, E.; Trianni, A.; Abeelen, C.; Worrell, E.; Miggiano, F. Barriers and drivers for energy efficiency: Different perspectives from an exploratory study in the Netherlands. Energy Convers. Manag. 2015, 102, 26–38. [Google Scholar] [CrossRef] [Green Version]
  48. Paramonova, S.; Thollander, P. Ex-post impact and process evaluation of the Swedish energy audit policy programme for small and medium-sized enterprises. J. Clean. Prod. 2016, 135, 932–949. [Google Scholar] [CrossRef] [Green Version]
  49. Schleich, J. Do energy audits help reduce barriers to energy efficiency? An empirical analysis for Germany. Int. J. Energy Technol. Policy 2004, 2, 226–239. [Google Scholar] [CrossRef]
  50. Bertoldi, P.; Rezessy, S.; Vine, E. Energy service companies in European countries: Current status and a strategy to foster their development. Energy Policy 2006, 34, 1818–1832. [Google Scholar] [CrossRef]
  51. Bertoldi, P.; Boza-Kiss, B. Analysis of barriers and drivers for the development of the ESCO markets in Europe. Energy Policy 2017, 107, 345–355. [Google Scholar] [CrossRef]
  52. Palmer, K.; Walls, M.; Gordon, H.; Gerarden, T. Assessing the energy-efficiency information gap: Results from a survey of home energy auditors. Energy Effic. 2013, 6, 271–292. [Google Scholar] [CrossRef]
  53. Fleiter, T.; Gruber, E.; Eichhammer, W.; Worrell, E. The German energy audit program for firms—A cost-effective way to improve energy efficiency? Energy Effic. 2012, 5, 447–469. [Google Scholar] [CrossRef]
  54. Redmond, J.; Walker, B. The value of energy audits for SMEs: An Australian example. Energy Effic. 2016, 9, 1053–1063. [Google Scholar] [CrossRef]
  55. Lisauskas, A.; Kveselis, V.; Dzenajavičienė, E.F.; Masaitis, S.; Perednis, E. Analysis of energy audits results and impacts: Case of small and medium enterprises in Lithuania. Energy Effic. 2022, 15, 48. [Google Scholar] [CrossRef]
  56. Nabitz, L.; Hirzel, S. Transposing the requirements of the energy efficiency directive on mandatory energy audits for large companies: A policy-cycle-based review of the national implementation in the EU-28 Member States. Energy Policy 2019, 125, 548–561. [Google Scholar] [CrossRef]
  57. Herce, C.; Martini, C.; Salvio, M.; Toro, C. Energy performance of Italian oil refineries based on mandatory energy audits. Energies 2022, 15, 532. [Google Scholar] [CrossRef]
  58. Locmelis, K.; Blumberga, D.; Blumberga, A.; Kubule, A. Benchmarking of industrial energy efficiency. outcomes of an energy audit policy program. Energies 2020, 13, 2210. [Google Scholar] [CrossRef]
  59. Murphy, L. The influence of energy audits on the energy efficiency investments of private owner-occupied households in the Netherlands. Energy Policy 2014, 65, 398–407. [Google Scholar] [CrossRef]
  60. Barbetta, G.P.; Canino, P.; Cima, S. The impact of energy audits on energy efficiency investment of public owners. Evidence from Italy. Energy 2015, 93, 1199–1209. [Google Scholar] [CrossRef]
  61. Moya, D.; Torres, R.; Stegen, S. Analysis of the Ecuadorian energy audit practices: A review of energy efficiency promotion. Renew. Sustain. Energy Rev. 2016, 62, 289–296. [Google Scholar] [CrossRef]
  62. Petek, J.; Glavic, P.; Kostevsek, A. Comprehensive approach to increase energy efficiency based on versatile industrial practices. Clean. Prod. 2016, 112, 2813–2821. [Google Scholar] [CrossRef]
  63. Schleich, J.; Fleiter, T. Effectiveness of energy audits in small business organizations. Resour. Energy Econ. 2019, 56, 59–70. [Google Scholar] [CrossRef]
  64. Zhang, Y.; Wei, Y.; Zhou, G. Promoting firms’ energy-saving behavior: The role of institutional pressures, top management support and financial slack. Energy Policy 2018, 115, 230–238. [Google Scholar] [CrossRef]
  65. Zhang, Y.; Wang, Z.; Zhou, G. Determinants of employee electricity saving: The role of social benefits, personal benefits and organizational electricity saving climate. J. Clean. Prod. 2014, 66, 280–287. [Google Scholar] [CrossRef]
  66. Lacroux, A. L’analyse des modèles de relations structurelles par la méthode PLS: Une approche émergente dans la recherche quantitative en GRH. In Proceedings of the XXème Congrès de l’AGRH, Toulouse, France, 6–9 September 2009; Volume 9. [Google Scholar]
  67. Hair, J.; Hult, G.T.M.; Ringle, C.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); SAGE Publications, Incorporated: Los Angeles, CA, USA, 2014. [Google Scholar]
  68. Bensouda, M.; Benali, M. From “Fairly Good” to “Optimal” Energy Efficiency Practices within the Moroccan Manufacturing Sector: Are Financial Resources Sufficient? Int. J. Energy Econ. Policy 2023, 13, 478–488. [Google Scholar] [CrossRef]
  69. Adadan, E.; Savasci, F. An analysis of 16–17-year-old students’ understanding of solution chemistry concepts using a two-tier diagnostic instrument. Int. J. Sci. Educ. 2012, 34, 513–544. [Google Scholar] [CrossRef]
  70. Lenny, P.Y.; Kridanto, S. Analysis of user acceptance, service quality, and customer satisfaction of hospital management information system. J. Phys. Conf. Ser. 2019, 1193, 012001. [Google Scholar] [CrossRef]
  71. Netemeyer, R.G.; Bearden, W.O.; Sharma, S. Scaling Procedures: Issues and Applications; Sage Publications: Thousand Oaks, CA, USA, 2003. [Google Scholar]
  72. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  73. Dos Santos, P.M.; Cirillo, M.Â. Construction of the average variance extracted index for construct validation in structural equation models with adaptive regressions. Commun. Stat.-Simul. Comput. 2021, 52, 1–13. [Google Scholar] [CrossRef]
  74. Henseler, J.; Ringle, C.M.; Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef] [Green Version]
  75. Ab Hamid, M.R.; Sami, W.; Sidek, M.M. Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT criterion. J. Phys. Conf. Ser. 2017, 890, 012163. [Google Scholar]
  76. Kline, R.B. Principles and Practice of Structural Equation Modeling (3. Baskı); Guilford: New York, NY, USA, 2011; Volume 14, pp. 1497–1513.
  77. Voorhees, C.M.; Brady, M.K.; Calantone, R.; Ramirez, E. Discriminant validity testing in marketing: An analysis, causes for concern, and proposed remedies. J. Acad. Mark. Sci. 2016, 44, 119–134. [Google Scholar] [CrossRef]
  78. Falk, R.F.; Miller, N.B. A Primer for Soft Modeling; University of Akron Press: Akron, OH, USA, 1992. [Google Scholar]
  79. Janadari MP, N.; Sri Ramalu, S.; Wei, C.; Abdullah, O.Y. Evaluation of measurment and structural model of the reflective model constructs in PLS–SEM. In Proceedings of the 6th International Symposium—South Eastern University of Sri Lanka (SEUSL), Oluvil, Sri Lanka, 20–21 December 2016; pp. 20–21. [Google Scholar]
  80. Hu, L.T.; Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Model. Multidiscip. J. 1999, 6, 1–55. [Google Scholar] [CrossRef]
  81. Mbaye, A. Review on energy audit: Benefits, barriers, and opportunities. Am. J. Energy Nat. Resour. 2022, 1, 45–58. [Google Scholar] [CrossRef]
  82. Johansson, I.; Mardan, N.; Cornelis, E.; Kimura, O.; Thollander, P. Designing policies and programs for improved energy efficiency in industrial SMEs. Energies 2019, 12, 1338. [Google Scholar] [CrossRef] [Green Version]
Sustainability 15 11552 g001 550
Figure 1. Research model.
Figure 1. Research model.
Sustainability 15 11552 g001
Sustainability 15 11552 g002 550
Figure 2. Research model with hypotheses.
Figure 2. Research model with hypotheses.
Sustainability 15 11552 g002
Sustainability 15 11552 g003 550
Figure 3. Structural Model Assessment-Direct model assessment: direct and moderating analysis.
Figure 3. Structural Model Assessment-Direct model assessment: direct and moderating analysis.
Sustainability 15 11552 g003
Table
Table 1. Convergent validity assessment: measurement model.
Table 1. Convergent validity assessment: measurement model.
Latent VariablesIndicatorsLoadingsCACRAVE
Low Technical CompetenceLTC10.9460.9600.9740.926
LTC20.963
LTC30.960
Energy AuditsEA10.8190.9010.9320.776
EA20.941
EA30.957
EA40.795
Energy Efficiency MeasuresEEM10.7970.9340.9500.793
EEM20.849
EEM30.938
EEM40.946
EEM50.915
Competing InterestsCI10.9200.9230.9510.867
CI20.944
CI30.929
Lack of Information LI10.9400.8970.9360.830
LI20.931
LI30.860
Table
Table 2. Discriminant validity assessment: Fornell and Larcker criterion.
Table 2. Discriminant validity assessment: Fornell and Larcker criterion.
CIEEMEALTCLI
Competing Interests0.931
Energy Efficiency Measures−0.6130.891
Energy Audits−0.3890.6450.881
Low Technical Competence0.684−0.590−0.4740.962
Lack of Information0.496−0.529−0.5680.5590.911
Table
Table 3. Discriminant validity assessment: HTMT ratio.
Table 3. Discriminant validity assessment: HTMT ratio.
CIEEMEALTCLI
Competing Interests
Energy Efficiency Measures0.657
Energy Audits0.4200.702
Low Technical Competence0.7240.6220.503
Lack of Information0.5490.5780.6330.606
Table
Table 4. R square and Q square values.
Table 4. R square and Q square values.
R SquareQ Square
Energy Efficiency Measures0.5820.451
Table
Table 5. Model fit assessment: SRMR.
Table 5. Model fit assessment: SRMR.
Saturated ModelEstimated Model
SRMR0.0600.060
Table
Table 6. Structural model assessment: direct path.
Table 6. Structural model assessment: direct path.
Std. BetaStd. ErrorT Statisticp Values2.5%97.5%Supported?
H1—CI -> EEM−0.3310.0734.5130.000−0.478−0.193YES
H2—LI -> EEM−0.1360.0662.0550.000−0.266−0.014YES
H3—LTC -> EEM−0.1910.0492.2730.000−0.283−0.064YES
H4—EA -> EEM0.4250.0597.2170.0000.3100.541YES
Table
Table 7. Structural model assessment: indirect moderating path.
Table 7. Structural model assessment: indirect moderating path.
Std. BetaStd. ErrorT Statisticp Values2.5%97.5%Supported?
H5CI*EA -> EEM0.0290.0521.7810.076−0.0880.137NO
H6LI*EA -> EEM0.3650.0466.9260.0000.2920.465YES
H7LTC*EA -> EEM0.3210.0556.6090.0000.2990.457YES
*: CI affects EEM through the moderator EA. LI affects EEM through the moderator EA. LTC affects EEM through the moderator EA.
Table
Table 8. Research hypotheses and findings in light of the prior literature.
Table 8. Research hypotheses and findings in light of the prior literature.
HypothesesReferenceConsistent Findings with the Previous Literature
H1[11,15]Competing interests hinder the adoption of EEMs.
H2[17,40]Lack of information regarding EE impedes the adoption
  of EEMs.
H3[13,18,19,47]Low technical competence regarding EE inhibits the
  adoption of EEMs.
H4[53]Energy audits have a positive effect on the adoption of EEMs.
H6[22]Energy audits mitigate the negative effect of the lack of information on the adoption of EEMs
 
H7[24]Energy audits attenuate the negative effect of low technical competence on the adoption of EEMs.
 
HypothesesReferenceInconsistent findings with the previous literature
H5[24]: they found that energy audits reduce the intensity of competing/divergent interest within firms.We found that energy audits raise awareness regarding energy efficiency opportunities but do not consistently attenuate the negative effect of competing interests on the adoption of EEMs.
 
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Bensouda, M.; Benali, M.; Moufdi, G.; Idrissi, T.E.B.E.; Bouhadi, A.E. Energy Audit as an Instrument to Tackle Internal Barriers to Energy Efficiency: Lessons from Moroccan Industrial Firms. Sustainability 2023, 15, 11552. https://doi.org/10.3390/su151511552

AMA Style

Bensouda M, Benali M, Moufdi G, Idrissi TEBE, Bouhadi AE. Energy Audit as an Instrument to Tackle Internal Barriers to Energy Efficiency: Lessons from Moroccan Industrial Firms. Sustainability. 2023; 15(15):11552. https://doi.org/10.3390/su151511552

Chicago/Turabian Style

Bensouda, Mehdi, Mimoun Benali, Ghada Moufdi, Taoufik El Bouzekri El Idrissi, and Abdelhamid El Bouhadi. 2023. "Energy Audit as an Instrument to Tackle Internal Barriers to Energy Efficiency: Lessons from Moroccan Industrial Firms" Sustainability 15, no. 15: 11552. https://doi.org/10.3390/su151511552

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop