Innovation, Awareness and Readiness for Climate Action in the Energy Sector of an Emerging Economy: The Case of Kenya

Abstract

The public sector plays a pivotal role in setting the pace for climate action innovation through policy development and inter-organization collaborations for sustainable energy solutions. There is generally a lack of a proper understanding of innovation in the public sector compared to the private sector, with the public sector being considered slow, bureaucratic adopters of innovation. This study investigated the understanding and approach to innovation in public energy organizations, determining if and how these organizations innovate and their ability to innovate, especially towards climate action, in Kenya while comparing them with Iceland, a developed economy with equivalent geothermal energy potential. A questionnaire survey was conducted in public energy organizations in Kenya and Iceland. Statistical analysis was used to validate and evaluate the collected data. The study findings revealed that innovation collaboration systems in organizations positively predicted the employees’ innovation awareness, confirming that energy sector innovations shall require public–private sector collaboration in developing innovative, incremental, and disruptive energy solutions. Employee knowledge and skills, on the other hand, were found not to be a predictor of an organization’s innovation awareness. Furthermore, employees’ motivation to innovate, as well as organizational innovation strategy, management structure and leadership, were found to positively predict an organization’s readiness to innovate. Finally, the Kenyan energy sector was benchmarked against the Icelandic energy sector indicating some noteworthy differences in the prioritization of energy sector climate action initiatives, with most organizations identifying themselves as innovation generators and innovation adopters and the least being innovation imitators, showing the organizations’ commitment to developing new technologies, markets and policies towards sustainable energy solutions.

1. Introduction

Developed and emerging economies alike are investing in future energy solutions to meet the ever-increasing energy demand in a sustainable manner to meet the net-zero climate action goals [1]. With huge renewable energy potential, Africa holds some of the most significant renewable energy resources, including geothermal energy, especially along the East African rift system, that can only be unlocked through innovative approaches. Innovation is thus crucial to meet sustainability challenges in a world that is increasingly volatile, uncertain, complex and ambiguous, or what is coined as the VUCA world [2,3], especially in the energy sector. Global electricity access stands at 90%, with nearly half of Africa’s population (570 million) without access and 80% of the connected population suffering frequent supply interruptions [4,5,6]. In Kenya, an emerging economy in Africa, the primary energy source is over 60% biomass with high demand for cooking and heating using wood and charcoal, especially in the rural areas, with almost 16 million people not yet connected to electricity [7,8,9]. On the other hand, in developed economies like Iceland, the primary energy source is 90% renewable, geothermal and hydro, mainly for space heating and electricity production, with 100% electricity accessibility [10,11]. The economy of these two countries are poles apart, with the GDP per capita in Iceland being more than ten times the same economic metric in Kenya. Despite the differences between the economies and their varying energy challenges and development priorities, they have a focus on the exploitation of geothermal energy as they race toward meeting net-zero emissions targets. This requires investments in innovation to sustainably handle the energy trilemma of security, cost and emissions while meeting the ever-rising global energy demand, especially in emerging economies like Kenya [12,13,14].

Due to limited data and studies on public sector innovation, there is generally a lack of a proper understanding of innovation in the public sector compared to the private sector, with the public sector being considered slow, bureaucratic adopters of innovation [15,16]. Despite this, public sector organizations seem to be at the forefront of innovation, especially relating to policy development and collaborative innovation, albeit they are generally less equipped to respond effectively to nonroutine, nonstandard challenges due to their conservative, bureaucratic structures [1], making them slow-moving and late adopters of innovation [17,18,19]. Although previous research on public sector innovation has made significant contributions [19,20] (pp. 4–6 of [21]), we contend that research on public sector innovation has not done enough to raise awareness of innovation and to establish sustainable structures and leadership to support innovation, particularly with regard to rising energy demand and climate change.

The purpose of the study is to determine whether and how energy companies innovate in light of the impending challenges posed by climate change. It looks into Kenya’s energy sector’s innovation awareness and readiness to achieve net-zero emissions and other climate action goals by 2050. The study offers analogies with the Icelandic energy sector, providing an understanding of the innovation pull inside a developed and an emerging economy in times of the dire challenges of climate change while acknowledging their unique and complex energy concerns. In response to a need for immediate climate action, the study identifies innovation awareness, readiness and gaps in addressing the energy trilemma of security, cost, and emissions by the public energy sector.

2. Theoretical Review

It is an organization’s mission to remain relevant and ahead of the competition in fulfilling the customers’ needs. Organizations have to continuously evolve their products and processes to maintain or grow their market share or maintain their relevance with change as the core factor for their survival [22] and to have a competitive advantage over their competitors. This deliberate, systematic, and continuous approach by organizations to appraise and renew their products, processes, or market presence, is a step towards innovation. Innovation is thus a key driver for economic growth and development, providing a foundation for new businesses, new jobs, and productivity growth [23]. The Organization for Economic Co-operation and Development (OECD) Oslo Manual 2018 defines innovation as “a new or improved product or process, or a combination thereof, that differs significantly from the unit’s previous products or processes and that has been made available to potential users (product) or brought into use by the unit (process)” [24]. The challenge, therefore, is to develop new technologies and policies and/or improve the existing ones to ensure energy security through efficient production from renewable and low-carbon energy sources, improve energy access for all and abate energy greenhouse gas (GHG) emissions.

Schumpeter defined innovation as the process of formation of any or combinations of new products or services, new processes, new sources of raw materials, new markets and new forms of organizations [25] revolutionizing the economic structure from within, destroying the old one and creating a new one [26]. Schumpeter idealized that innovation had to be radical or revolutionary and now considered disruptive, rendering other existing products and processes irrelevant in the market space. Schumpeter theorized that economic transformation is a function of both innovation and entrepreneurial activities yielding satisfaction and profit as a reward for performance because of market power [22].

Peter Drucker, the founder of modern management, also considered innovation as a specific function of entrepreneurship; as the effort to create purposeful, focused change in an enterprise’s economy or social potential to set a standard; and as a direction for a new technology of new industry, or creating a business that is and remains ahead of others [27]. Drucker concludes that it is through innovation that entrepreneurs create new wealth-producing resources or endow existing resources with enhanced potential for creating wealth and that the practice of systematic innovation is the foundation of entrepreneurship. Drucker’s definition introduces the essence of value creation and incremental innovation with entrepreneurial hindsight.

Innovations involve converting ideas and inventions into useful applications for the market or an organization’s internal operation [24] (p. 13 of [28]). It is problem-driven, and its purpose is to solve problems with practical or organizational solutions [29].

Going by the definitions of Schumpeter, Drucker and the OECD, organizations need to invest time and effort in systematically studying the market trends and customer response to these trends and react accordingly by either developing products and/or processes to match or supersede the trending ones or to adopt new trends [30]. By doing so, the organizations maintain either their lead or market share for the provision of their products and services.

Bull and Willard (1993) share Schumpeter’s understanding of an entrepreneur as one who exploits innovation or carries out innovation activities, that is, the person who carries out new combinations causing discontinuity by revolutionizing the pattern of production [31]. Therefore, according to Schumpeter, it is the entrepreneur who creates innovation, and there is no innovation without entrepreneurship. The entrepreneur’s primary motivation is independence to work as they would like and ask for or develop novel unusual solutions acting in the venture’s long-term best interest [32]. In their research, Herbig et al. state that “Entrepreneurs themselves do not consciously innovate; they seek opportunities” ignored or not undertaken by other larger firms due to their bureaucratic tendencies and, therefore, do not in themselves guarantee innovation. Individual entrepreneurs want to achieve technical contribution, recognition, power and independence as much as money and, therefore, also require human resources (technical, marketing and managerial) to assist in turning their dream ideas into reality [32]. To the entrepreneur, change is a normal and healthy event, and so they respond to change to exploit opportunities and shift resources from areas of low productivity and yield to areas of high productivity and yield [32].

Entrepreneurship, according to Schumpeter, is the force that accounts for change and development in a dynamic economy, and its quality determines the speed of capital growth, as well as whether that growth will involve innovation and change resulting in a continuous wave of “creative destruction” of existing ideas, skills, technologies and equipment [22]. However, all these depend on the rate of diffusion of the innovation through the wide uptake of the innovation throughout the market of potential adopters. The rate of diffusion of innovation shall depend on the innovation’s compatibility with values, beliefs, customs and past experiences of individuals or organizations in the system [22,33] and the entrepreneur’s determination to promote their belief in the innovation despite setbacks and events that threaten its success [17] (p. 12).

Damanpour and Wischnevsky’s study on organizational innovation classifies technical organizations as either innovation-generating or innovation-adopting organizations or units of organizations. Innovation-generating organizations (IGO) depend on technological knowledge and market capabilities for the commercialization of developed innovation, while innovation-adopting organizations (IAO) rely on managerial and organizational abilities to select and assimilate innovation for their operations [34].

The choice of either being an IGO or an IAO is determined by the organization’s strategy–structure interplay for innovation and technology management. Christensen’s [35] analysis of the strategy–structure relationship in technology-based organizations found that the organization founder(s)’ initial strategic inclination, the organization’s initial product successes or the product market prospects had a great impact on the organization’s strategy and structure way into the future. Therefore, to understand an organization’s innovation, one must study if it is an innovation-generating or innovation-adopting organization [34].

While the assertion “innovate or die”, resonates soundly with organizations, technologies and enterprises that have fallen due to their lethargy in innovation, there is little evidence of a direct relationship between innovation and organization performance [36,37]. With adequate capital, appropriate organization infrastructure and the requisite entrepreneurial environment and capacity, innovation in organizations would thrive [32]. Positive organization structures including an increasing focus on capital formation, changing institutional relationships, supportive government programs, reassessment of intellectual property and new approaches to innovation are critical to creating a fertile environment for entrepreneurship and innovation.

3. Method

3.1. Conceptual Framework

The following hypotheses relating to the organization’s internal innovation drivers were advocated for the study.

Hypothesis 1 (H1). 

An organizations’ innovation culture is more positively related to the organizations’ innovation management, structure and leadership.

Innovation activities require a high acceptance of risk due to long gestation periods and experimentation with possibility of failure and, therefore, require huge financial resources to support the risks and costs. However, generally, governments are risk-averse, influencing the innovation strategy of the public organization to take on projects that do not have high risk of failure [38]. Organizations thus need sufficient leadership capacity to detect, interpret and act on ambiguous signals of threats and seize arising opportunities to maximize gains with a long-term plan [39].

Hypothesis 2 (H2). 

An organizations’ innovation readiness is more positively related to the employees’ motivation to innovate despite the country of operation.

The conditions of the organization, like ease of decision making, control of funds, lateral communication and reward schemes, inspire innovators to put more effort into new the organization’s innovation activities and remain in the organization [40]. Investing in employee welfare by improving work safety, easing communication and giving employees more decision-making roles may improve the organization’s innovation readiness score.

Hypothesis 3 (H3). 

Energy organizations’ innovation readiness is more positively related to employee knowledge, training and competence.

Skilled human resources are key to the innovation readiness of an organization. The private sector is seen to be most attractive to skilled personnel with the prospects of better pay, more freedom to experiment, higher risk appetite and less bureaucratic organizational structures. Therefore, opportunities for employees to access relevant training on emerging technologies within the organization would translate to better innovation response. Public organizations are posed to remain bureaucratic or create new bureaucracy with changes in the public administration processes [18], implying a less attractive environment for innovative personnel.

Hypothesis 4 (H4). 

Energy organizations’ innovation awareness is more positively related to the collaborative innovations undertaken by the organization.

In the era of New Public Management, cross-sector collaboration has been encouraged with the intention of spurring entrepreneurship and innovativeness into public organizations as the exchange of knowledge, experiences, information, values, cultures and resources over time produces innovative outcomes [41,42,43]. How the public organizations can best explore and exploit these opportunities determines their pathway in the innovation journey.

Hypothesis 5 (H5). 

Public sector organizations in Iceland within the energy sector innovate differently from those in Kenya.

Despite being the lowest contributor to greenhouse gas emissions with not more than 5% of global emissions, Africa is the most vulnerable region to climate change. The African nations, therefore, cannot follow the same climate action pathway towards clean energy, that is, just energy transition and energy equity, as the developed nations [5]. They are thus expected to have different innovation regimes compared to developed countries, especially towards climate action with some necessary cross-regional collaborations [44] to generate new products, technologies or processes or adopt and improve the existing products, technologies or processes.

The conceptual framework is visualized in Figure 1.

3.2. Sampling Frame

The energy sector is broad; therefore, the electricity sub-sector was considered for the survey of representative public organizations in electricity regulation, generation, transmission and distribution, where the government, national or municipal, has a controlling stake, that is, more than 50% shareholding. Purposive sampling was applied for this research, selecting known public energy sector organizations in Kenya and Iceland. The survey was directly sent to contact persons in selected organizations listed in

Table 1. Public electricity sub-sector organizations considered for the survey.

Sub-Sector	Kenya	Iceland
Regulation	Energy and Petroleum Regulatory Authority (EPRA)	National Energy Authority (NEA)/Orkustofnun, ISOR
Generation	Kenya Electricity Generating Company PLC (KenGen)	Landsvirkjun
	Geothermal Development Company (GDC)	Orka náttúrunnar ohf.
Transmission	Kenya Transmission Company (KETRACO)	Landsnet hf.
	Kenya Power and Lighting Company PLC (Kenya Power)
Distribution	Kenya Power and Lighting Company PLC (Kenya Power)	Landsnet hf.
	Rural Electrification and Renewable Energy Corporation (REREC)	ON Power ohf.
		RARIK ohf.

to ensure the right target group participated.

This research employed quantitative data collection techniques using standardized self-administered online questionnaires because of their lower cost of administration, faster correspondence between participants and survey administrator and reduced chances of false negatives [24]. However, this approach suffers a low response rate given that respondents may not complete or refuse to complete the survey [24], as was the case in this survey.

3.3. Questionnaire Design

The survey used a 6-point Likert scale questionnaire model containing ratings from 6 (“Strongly Agree/Highly Likely”) to 1 (“Strongly Disagree/Highly Unlikely”) for an increased measurement precision while avoiding the option of a neutral or middle category, which is equivalent to no response or confusion [45].

The survey was conducted for response in Iceland and Kenya. The list of some public energy organizations considered for the participation, as listed in Table 1, was generated based on the organization’s ownership and engagement in the electricity sub-sector.

The questionnaire design was guided by the OECD Manual 2018 guidelines on questionnaires, and question design was organized in themes to logically obtain the participants’ responses and reactions. The survey questions were created with close reference to previous research on energy sector innovation and climate action [38,46,47]. In the first theme, the participants’ demographic data, including age, gender, education level, sub-sector engaged, section in organization, position held and employment terms, as detailed in

Table 2. Participants’ demographic data.

	Categories
Age	20–29; 30–39; 40–49; 50 years and above
Gender	Male; Female
Education	Diploma; Bachelors; Masters; PhD; Other
Sub-sector	Regulation; Generation; Transmission; Distribution; Other
Country	Iceland; Kenya
Profession	Engineering and Science; Environment and Natural Resources; Finance and Administration; Human Resources; ICT; Legal; Supply Chain; Other
Position	Top Management; Middle-level Management; Consultant; Engineer; Scientist; Technician; Other
Experience	0–4; 5–10; 11–14; 15 years and above
Employment terms	Permanent and Pensionable; Short-term Contract (Up to 1 Year); Long-term Contract (More than 1 Year)

, were collected.

The second theme had questions on the participants’ awareness to innovate, which were drawn to assess their skills and knowledge, workplace environment, motivation levels and their understanding of innovation.

The third theme assessed how ready the organization is to innovate in two parts. The first part included questions on the organizations’ innovation management systems: leadership and structure, innovation strategy, resource availability for innovation, organization innovation culture and organization collaboration strategy. The second part of innovation readiness survey included question on the energy sector organizations’ innovation strategy and commitment towards UN SDGs, 2050 net-zero emissions targets and innovation activities towards solving the energy trilemma (energy security, equity (access and affordability) and the environment).

The fourth theme of the survey was on the future outlook of public energy organizations in context of innovation. The section attempts to find out the organizations’ long-term (over 5 years) focus and prioritization of innovation, research and development of energy solutions, technology and policy in areas of electricity generation, energy transition, technology advancements and climate action.

In conclusion, the participants’ opinions were sought on their agreement with their organization’s innovation pathway and, finally, their agreement with the adopted definition of innovation in relation to their public energy organization’s mandate.

Table 3. The variables used for the analysis. The total list of questions is accessible in Appendix A.

Marker	Variable
Q04	Employee skills and knowledge
Q05	Employee workplace environment
Q06	Employee motivation to innovate
Q07	Employee understanding of innovation concepts
Q09	Organization innovation strategy
Q10	Organization innovation management: leadership and structure
Q11	Organization innovation resources availability
Q12	Organization innovation culture
Q13	Organization innovation collaboration systems
Q16	Organization response to global action
Q17	Organization commitment toward energy trilemma

presents the variables used in the study with markers.

4. Data Analysis and Results

The results of the survey response were analysis for normality, reliability and validity to allow testing of the formulated hypotheses. This section presents these results for discussion.

4.1. Country of Organization

A total of 58 respondents indicated the country their organization operates in, with 12 (20.7%) indicating Iceland and 46 (79.3%) indicating Kenya (

Table 4. Organizations’ country of operation.

Country	Frequency	Valid Percent
Iceland	12	20.7
Kenya	46	79.3

).

4.2. Respondents’ Professions

A total of 57 respondents indicated their profession (see

Table 5. Respondents’ profession groups.

Profession Groups	Frequency	Valid Percent
Engineering and Science	32	56.1
Environment and Natural Resources	3	5.3
Finance and Administration	3	5.3
Health and Safety	2	3.5
Human Resources	4	7.0
ICT	2	3.5
Supply Chain	1	1.8
Other	10	17.5

).

4.3. Respondents’ Experience

A total of 56 respondents indicated their years of experience in the public energy sector (see

Table 6. Respondents’ years of experience.

Experience	Frequency	Valid Percent
0–4 years	4	7.1
5–10 years	21	37.5
11–15 years	16	28.6
Over 15 years	15	26.8

).

4.4. Respondents’ Position in Organization

A total of 58 respondents indicated their position in their organization (see

Table 7. Respondents’ position in organization.

Position	Frequency	Valid Percent
Top Management	5	8.6
Middle-level Management	20	34.5
Consultant	1	1.7
Engineer	13	22.4
Scientist	12	20.7
Technician	2	3.4
Other	5	8.6

).

4.5. Reliability and Validity Analysis

After a survey period between 5 July and 2 August 2022, a total of 59 responses were received by the study’s conclusion (63 days). After a visual inspection revealed a high likelihood of not being engaged during the survey, one response was removed from the survey. Eight (8) negatively worded items were reversed and recoded to correspond with the Likert scale.

4.6. Test of Normality

Following an assumption that the values from the survey were taken from a normally distributed population, it is therefore necessary that the data be subject to a test for normality to draw accurate and reliable conclusions [48]. Testing the data for normality was conducted by analyzing the frequency distribution graphs, Q-Q plots and boxplots. Using statistical testing approaches for normality, like the Shapiro–Wilk test of normality, the p-value (α) was examined to determine if the values were sampled from a normally distributed population. Since our sample size was considered small-to-moderate (i.e., 50 ≤ n < 300), the Shapiro–Wilk test was considered in the statistical test for normality to check the significance of grouped data from the survey [48].

Table 8. Skewness, kurtosis and normality test results.

Variable	Skewness			Kurtosis
	Value	Std. Error	Zskewness	Value	Std. Error	Zkurtosis	Shapiro–Wilk Test
Q04	0.090	0.330	0.273	−1.099	0.650	−1.691	0.068
Q05	0.009	0.330	0.026	−0.664	0.650	−1.022	0.581
Q06	−0.635	0.330	−1.922	0.756	0.650	1.162	0.188
Q07	0.500	0.330	1.513	1.150	0.650	1.769	0.046
Q09	−1.253	0.330	−3.791	1.277	0.650	1.965	<0.001
Q10	−0.819	0.330	−2.479	0.068	0.650	0.104	0.008
Q11	−0.412	0.330	−1.247	−0.382	0.650	−0.587	0.028
Q12	−0.643	0.330	−1.945	−0.313	0.650	−0.482	0.004
Q13	−0.611	0.330	−1.849	1.158	0.650	1.781	0.054
Q16	−0.524	0.330	−1.587	0.103	0.650	0.159	0.022
Q17	−1.255	0.330	−3.832	2.274	0.650	3.529	<0.001

gives the results of normality test carried out on the original collected data for eleven (11) variables. The other Likert items were not grouped and hence not analyzed for normality.

Based on the Shapiro–Wilk test results, only variables Q04 (Employee Skills and Knowledge), Q05 (Employee Workplace Environment), Q06 (Employee Motivation to Innovate) and Q13 (Organization Innovation Collaboration Systems) exhibit a normal distribution with p > 0.05, while all the rest of the variables are non-normally distributed with p < 0.05, and hence, parametric statistical tests were used in analysis.

The log transformation of variables Q07, Q09, Q10, Q11, Q12, Q16 and Q17 was performed to confirm non-normality. Conducting the test of normality on these log transforms, the Shapiro–Wilk test results showed that only log-transformed variable Q07 was normally distributed with p = 0.300 with a confidence interval of 95%, hence not rejecting the null hypothesis that the data are not significantly different from normally distributed data; thus, linear regression analysis shall be used to analyze the variable. Log-transformed variables Q09, Q10, Q11, Q12, Q16 and Q17 were determined to be non-normal with p < 0.05, showing that they are significantly different from normally distributed data; thus, non-parametric statistical tests were used in analysis (

Table 9. Tests of normality.

Tests of Normality
	Kolmogorov–Smirnov a			Shapiro–Wilk
	Statistic	df	Sig.	Statistic	df	Sig.
Organization employee innovation awareness	0.075	58	0.200 *	0.982	58	0.560
Employee skills and knowledge	0.193	58	0.000	0.908	58	0.000
Employee workplace environment	0.098	58	0.200 *	0.976	58	0.298
Employee motivation to innovate	0.153	58	0.002	0.958	58	0.042
Organization innovation readiness	0.153	58	0.002	0.906	58	0.000
Organization innovation strategy	0.185	58	0.000	0.889	58	0.000
Organization innovation management: leadership and structure	0.157	58	0.001	0.885	58	0.000
Organization innovation resources availability	0.170	58	0.000	0.936	58	0.004
Organization innovation culture	0.144	58	0.004	0.964	58	0.083
Organization innovation collaboration systems	0.116	58	0.049	0.968	58	0.130
Organization response to global climate action	0.162	58	0.001	0.917	58	0.001
Organization commitment toward energy trilemma	0.159	58	0.001	0.896	58	0.000

Note: * This is a lower bound of the true significance. ^a Lilliefors Significance Correction.

).

4.7. Descriptive Statistics

The mean and standard deviation of the collected data were then analyzed to determine how best they represent the data of the 58 samples.

Table 10. Summary of mean and standard deviation.

Variable		Mean	Std. Deviation
Q04	Employee skills and knowledge	4.8444	0.5313
Q05	Employee workplace environment	4.7638	0.6042
Q06	Employee motivation to innovate	4.0873	0.9962
Q07	Employee understanding of innovation concepts	4.1997	0.5039
Q09	Organization innovation strategy	4.4262	0.8770
Q10	Organization innovation management: leadership and structure	4.4219	1.0044
Q11	Organization innovation resources availability	3.9831	1.2024
Q12	Organization innovation culture	4.3427	1.0345
Q13	Organization innovation collaboration systems	4.2297	1.0021
Q16	Organization response to global action	4.8260	0.7334
Q17	Organization commitment toward energy trilemma	4.8117	0.8576

shows a summary of the mean (M) and standard deviation (SD) of the collected data after replacement of missing values. All variables had a mean (M) and standard deviation (SD) of all items in the range of 3.480 < M < 5.086 and 0.504 < SD < 1.451, respectively, indicating a fair distribution of the data representing the respondents’ views.

To assess the reliability, that is, the data’s stability or consistency, Cronbach’s alpha was performed on the data’s measurement scale items to check on their internal correlation.

Table 11. Cronbach’s alpha reliability of the grouped items.

Latent Construct	Variable	Cronbach’s α Coefficient (Pooled Data from 5 Imputations)
		0 *	1	2	3	4	5
Organization innovation awareness	Q04	0.628	0.608	0.619	0.597	0.627	0.618
	Q05	0.788	0.787	0.782	0.783	0.784	0.784
	Q06	0.722	0.712	0.708	0.706	0.708	0.714
	Q07	0.038	0.006	0.025	0.012	−0.001	0.022
Organization innovation readiness	Q09	0.827	0.835	0.844	0.838	0.835	0.844
	Q10	0.860	0.871	0.864	0.872	0.871	0.868
	Q11	0.910	0.903	0.898	0.905	0.909	0.908
	Q12	0.769	0.757	0.771	0.757	0.772	0.774
	Q13	0.865	0.865	0.865	0.858	0.862	0.867
Organization response to climate action	Q16	0.593	0.571	0.576	0.574	0.574	0.568
	Q17	0.850	0.846	0.848	0.849	0.844	0.847

* Original data.

shows the results of the Cronbach’s alpha reliability analysis performed on the pooled data.

Variables Q04, Q07 and Q16 reported Cronbach’s alpha coefficients of α < 0.7 and mean inter-item correlations of 4.8, 4.19 and 4.8, respectively. To improve the Cronbach’s alpha coefficient, weak scale items seen to not and having low α values were excluded from the scale. Scale items Q04F_SKL (Participation in organization’s research activities), Q04G_SKL (Skill in change management) and Q16D_GLO (Climate action is prioritized in innovation strategy) were excluded from their scales.

4.8. Statistical Test Results

T-tests were used to compare the dependent latent variables between the public energy organization’s countries of operation (Iceland and Kenya) in this study.

4.9. Independent samples Mann–Whitney U Test

The non-normally distributed ordinal or continuous variables, namely, organization innovation readiness, organization response to climate action and organization commitment toward the energy trilemma, were subjected to the independent samples Mann–Whitney U-test.

Table 12. Hypothesis test summary.

	Null Hypothesis	Sig. a,b	Decision
1	The distribution of organization innovation readiness is the same across categories of Country.	<0.001	Reject the null hypothesis.
2	The distribution of organization response to global climate action is the same across categories of Country.	0.310	Retain the null hypothesis.
3	The distribution of organization commitment toward the energy trilemma is the same across categories of Country.	0.062	Retain the null hypothesis.

Note: ^a. The significance level is 0.050. ^b. Asymptotic significance is displayed.

shows the summary results of the test.

The variables organization response to global climate action (Mann–Whitney U = 328.00, p = 0.310 two-sided) and organization commitment toward the energy trilemma (Mann–Whitney U = 372.50, p = 0.062 two-sided), however, had the same distribution across Iceland (N = 12) and Kenya (N = 46), hence not rejecting the null hypothesis that the distribution of the organization’s response to global climate action and the organization’s commitment toward the energy trilemma is the same across the two countries. This implies that the two variables are not influenced by the organizations’ country of operation.

4.10. Hypothesis Testing

To test the set hypotheses, regression analysis, a qualitative data analysis technique, was performed.

Ordinal regression was conducted with innovation culture as the dependent variable and organization innovation strategy and organization innovation management structure and leadership as the independent variables. First, the model was tested using maximum likelihood estimation to determine if it fits the data. The model-fitting information indicated that it was statistically significant with p < 0.00, showing that the model fits the dataset well.

From the parameter estimates, it is deduced that:

Organization innovation strategy was a significant positive predictor of the organization’s innovation culture, having a coefficient p = 0.009 (p < 0.05). Thus, for every unit increase in the organization innovation strategy, there is a predicted increase of β = 1.123 in the log odds of being at a higher level of innovation culture.

The organization’s innovation management structure and leadership were also found to be a positive predictor of the organization innovation culture, with a coefficient p = 0.005 (p < 0.05). Therefore, for every unit increase in the organization’s innovation management structure and leadership, there is a predicted increase of β = 0.810 in the log odds of being at a higher level of innovation culture.

There was a significant and strong positive correlation between variables Q09 and Q10, r(56) = 0.732, p = 0.000. The variables Q09 and Q12 also exhibited a significant and strong positive correlation, r(56) = 0.567, p < 0.001. Similarly the correlation between variables Q10 and Q12 was also significant, strong and positive, r(56) = 0.649, p < 0.001 [49].

H2 proposes that the independent variable of employee motivation to innovate (Q06) has a positive influence on the organization’s innovation readiness (Q09–Q12). The latent construct variable of organization innovation readiness is the transformed mean of the variable Q09, Q10, Q11 and Q12. Since the variable of organization innovation readiness had a non-normal distribution and employee motivation to innovate had a normal distribution, the non-parametric statistical test, ordinal logistic regression (OLR) and Spearman’s rank correlation were performed to test the Hypothesis H2.

From the parameter estimates in

Table 13. H2 MLR parameter estimates.

Country a		B	Std. Error	Wald	df	Sig.	Exp(B)	95% CI for Exp(B)
								Lower Bound	Upper Bound
Kenya	Intercept	−4.113	1.704	5.829	1	0.016
	Employee motivation to innovate	−0.065	0.374	0.030	1	0.862	0.937	0.450	1.951
	Organization innovation readiness	1.400	0.436	10.327	1	0.001	4.055	1.726	9.523

Note: ^a The reference category is Iceland.

, organizations which scored higher on organization innovation readiness were more likely to be in Kenya than in Iceland, this being the only predictor that was statistically significant (β = 1.400, SE = 0.436, p = 0.001). Employee motivation to innovate was therefore not a statistically significant predictor, with β = −0.065, SE = 0.374, p = 0.862. The odds ratio (exp(β)) of 4.055 indicated that for every one unit increase in organization’s innovation readiness, the odds of a public energy organization being in Kenya changed by a factor of 4.055.

A classification table (

Table 14. H2 MLR classification.

Observed	Predicted
	Iceland	Kenya	Percent Correct
Iceland	6	6	50.0%
Kenya	2	44	95.7%
Overall Percentage	13.8%	86.2%	86.2%

) shows which group memberships were best predicted by the model. The Icelandic public energy organizations were predicted correctly by the model 50.0% of the time compared to the Kenyan public energy organizations, which were correctly predicted by the model 95.7% of the time, nearly twice better.

Spearman’s rank correlation was performed on the dataset to determine the strength and direction of correlation between the two variables of organization innovation readiness (Q09–Q12) and employee motivation to innovate (Q06). The Spearman’s correlation coefficients were as presented in

Table 15. H2 Spearman’s rank correlation.

		Q09–Q12	Q06
Q09–Q12	Correlation Coefficient	1.000
	Sig. (2-tailed)
	N	58
Q06	Correlation Coefficient	0.320 *	1.000
	Sig. (2-tailed)	0.014
	N	58	58

Note: * Correlation is significant at the 0.05 level (2-tailed).

.

There was a statistically significant but weak positive correlation between variables Q09–Q12 and Q06, r(56) = 0.320, p = 0.014 [49].

The Pearson and Deviance chi-square goodness-of-fit tests were conducted to determine if the model fits the dataset well. The Pearson (χ²(179) = 188.299, p = 1.052) and Deviance chi-square test (χ²(179) = 135.654, p = 0.758) were not significant (p > 0.05) as shown, suggesting a good model fit.

The omnibus test results also confirm that the model fits the dataset well with a statistically significant chi-square (χ²(1) = 2.464, p = 0.117), as shown in

Table 16. H3 OLR Omnibus Test ^a.

Likelihood Ratio Chi-Square	Df	Sig.
2.464	1	0.117

Note: Dependent Variable: Organization innovation readiness. Model: (Threshold), Employee motivation to Innovate. ^a Compares the fitted model against the thresholds-only model.

, implying that the new model is not significantly better and hence does not fit the dataset well.

The test of parallel lines tests for the violation of the assumption of proportional odds. It was found statistically significant p = 0.008 violating the assumptions of proportional odd.

Spearman’s rank correlation was performed on the dataset to determine the strength and direction of correlation between the two variables of organization innovation readiness (Q09–Q12) and employee skills and knowledge (Q04). The Spearman’s correlation coefficients were as presented in

Table 17. H3 Spearman’s rank correlation.

		Organization Innovation Readiness	Employee Skills and Knowledge
Organization innovation readiness	Correlation Coefficient	1.000
	Sig. (2-tailed)
	N	58
Employee skills and knowledge	Correlation Coefficient	0.232	1.000
	Sig. (2-tailed)	0.080
	N	58	58

Note: Correlation is significant at the 0.05 level (2-tailed).

.

The correlation between variables Q09–Q12 and Q04 was found to be statistically not significant, r(56) = 0.232, p = 0.080 (p > 0.05).

Since the dependent and the independent variable being investigated by this hypothesis were both normally distributed, parametric statistical tests (linear regression and Pearson correlation tests) were conducted to determine the suitability of the dataset for a model in predicting the outcome of organization innovation awareness and to assess their strengths and direction of correlation.

Linear regression was conducted to test if an organization’s innovation collaboration systems significantly predicted employee innovation awareness. The Pearson correlation coefficient, r = 0.421, showed that there was a positive correlation between the two variables. The r-value describes the strength and direction of a linear relationship between two or more variables. The R-squared value of 0.177 shows that about 17.7% of changes in the organization’s employee innovation awareness is explained by the organization’s innovation collaboration systems, while a greater part of about 82.3% is captured by an error term showing that the model has a poor fit.

The adjusted R-squared value of 0.162 shows that about 16.2% of changes in the organization’s innovation awareness is explained by the organization’s innovation collaboration systems, while about 83.8% is captured by the error term, further showing that the model has a poor fit. The Durbin–Watson test measures evidence of autocorrelation in the residual, with an acceptable range of no autocorrelation being 1.5 to 2.5 [50]. The DW value was 1.963, which is within the acceptable range of no autocorrelation; thus, the observations are independent (

Table 18. H4 LR model summary.

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Durbin-Watson
1	0.421 a	0.177	0.162	0.50442	1.963

Note: ^a. Predictors: (Constant), Organization innovation collaboration systems. Dependent Variable: Organization employee innovation awareness.

).

The analysis of variance (ANOVA) measured the overall significance of the model. The results confirmed that the overall regression model was significant for the data based on the ANOVA (F-statistic) value, F = 12.036, and its associated probability value of p = 0.001 (F(1,56) = 12.036, p < 0.001), which was found to be statistically significant at a 5% level, as shown in

Table 19. H4 LR ANOVA ^a.

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	3.063	1	3.063	12.036	0.001 b
	Residual	14.249	56	0.254
	Total	17.311	57

Note: ^a Dependent Variable: Organization employee innovation awareness. ^b Predictors: (Constant), Organization innovation collaboration systems.

, showing that the regression model was a good fit for the data.

From the coefficients table (

Table 20. H4 LR coefficients ^a.

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Collinearity Statistics
		B	Std. Error	Beta			Tolerance	VIF
1	(Constant)	3.650	0.290		12.601	0.000
	Organization innovation collaboration systems	0.231	0.067	0.421	3.469	0.001	1.000	1.000

Note: ^a Dependent Variable: Organization employee innovation awareness.

), the organization’s innovation collaboration system B coefficient value was found to be B = 0.231, showing that a unit increase in organization innovation collaboration system on average increased organization innovation awareness by 0.231. The calculated t-value for the relationship between the organization’s innovation collaboration system and the organization’s employee innovation awareness is given as t = 3.469, with an associated p-value of P = 0.01 (p < 0.05), showing conclusively that the organization’s innovation collaboration system has a positive and significant impact on the organization’s employee innovation awareness. The tolerance value for the independent value, the organization’s innovation collaboration system, is 1.000, which is not less than 0.10 and therefore does not violate the multicollinearity assumption, which is supported by the VIF value of 1.000, which is well below the cut-off value of 10, meaning that the model is free from multicollinearity.

Pearson correlation analysis was carried out to determine the direction and strength of the linear relationship between organization innovation collaboration systems and the employee innovation awareness. From the Pearson correlation table (

Table 21. H4 Pearson correlation.

		Employee Innovation Awareness	Innovation Collaboration Systems
Employee innovation awareness	Pearson Correlation	1
	Sig. (2-tailed)
	N	58
Innovation collaboration systems	Pearson Correlation	0.421 **	1
	Sig. (2-tailed)	0.001
	N	58	58

Note: ** Correlation is significant at the 0.01 level (2-tailed).

), the correlation between employee innovation awareness and the innovation collaboration system was found to be moderate and positive, r = 0.421, and statistically significant at p = 0.01 [49].

The respondents were asked how they would classify their organizations’ innovation. Variable Q14 highlights three types of innovative organizations. Q14A (The organization develops new products, or services with its own internal resources) described an organization that is an innovation generator, utilizing its own internal resources to develop new products and services for its market environment. Q14B (The organization adopts new products or services developed by other organizations) described an organization that is an innovation adopter. These organizations procure or implement new products or services developed by other organizations but which are not yet available in their market environment. Finally, Q14C (The organization replicates new products or services developed by other organizations) describes an organization that is an innovation imitator. These organizations develop or re-engineer products and services developed by other organizations with significant improvement for their market environments.

To assess the relationship between the three classes of organizations, Pearson’s correlation was conducted. From the results in

Table 22. H5 Pearson’s correlation.

		Organization Is an Innovation Generator	Organization Is an Innovation Adopter	Organization Is an Innovation Imitator
Organization is an innovation generator	Pearson Correlation	1
	Sig. (2-tailed)
	N	58
Organization is an innovation adopter	Pearson Correlation	0.267 *	1
	Sig. (2-tailed)	0.043
	N	58	58
Organization is an innovation imitator	Pearson Correlation	0.070	0.503 **	1
	Sig. (2-tailed)	0.602	0.000
	N	58	58	58

* Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).

, there is a significant weak positive correlation between an organization that is an innovation generator and an organization that is an innovation adopter, r = 0.267, p = 0.043. Similarly, there is a not significant, weak positive correlation between an organization that is an innovation generator and an organization which is an innovation imitator, r = 0.070, p = 0.602. However, there is a strong positive correlation between an organization that is an innovation adopter and an organization that is an innovation imitator, r = 0.503, p < 0.001.

Table 23. H5 descriptive statistics summary.

	Mean	Std. Deviation	N
Organization is an innovation generator	4.474	1.179	58
Organization is an innovation adopter	4.231	1.186	58
Organization is an innovation imitator	3.480	1.451	58

highlights the descriptive statistics of the variables. It is noted that most respondents identified their organizations as innovation generators (N = 58, M = 4.474, SD = 1.179). The least number of respondents identified their organization as innovation imitators (N = 58, M = 4.231, SD = 1.186).

Table 24. H5 descriptive statistics by country.

						95% CI for Mean
		M	Mdn		SD	Lower Bound	Upper Bound
Organization is an innovation generator	Iceland	4.000	4	Slightly Agree	1.044	3.336	4.664
	Kenya	4.597	5	Agree	1.191	4.243	4.951
Organization is an innovation adopter	Iceland	4.683	5	Agree	0.955	4.076	5.290
	Kenya	4.114	4	Slightly Agree	1.221	3.752	4.477
Organization is an innovation imitator	Iceland	4.167	5	Agree	1.467	3.235	5.099
	Kenya	3.300	3	Disagree	1.408	2.882	3.718

details the classification of the organization further by country. Icelandic public energy organizations were identified generally as either innovation adopters (N = 58, M = 4.683, Mdn = 5 (Agree)) or innovation imitators (N = 58, M = 4.167, Mdn = 5 (Agree)) with slight agreement that they are innovation generators (N = 58, M = 4.000, Mdn = 4 (Slightly Agree)). The Kenyan organizations were identified mainly as innovator generators (N = 58, M = 4.597, Mdn = 5 (Agree)) with slight agreement that they are innovation adopters (N = 58, M = 4.114, Mdn = 4 (Slightly Agree)) and slight disagreement that they are innovation imitators (N = 58, M = 3.300, Mdn = 5 (Slightly Disagree)).

5. Discussion

This research attempts to answer if and how energy organizations innovate in context of the challenges of imminent climate changes. To answer this question, the fourth and fifth hypotheses were evaluated. These hypotheses are related to the levels of innovation awareness in public energy organizations, their innovation collaboration systems and the types of innovations they undertake.

The fourth hypothesis, H4: Public energy organizations’ innovation awareness is more positively related to the collaborative innovations undertaken by the organization, was tested to establish the relationship between an organization’s innovation collaboration systems and its employees’ innovation awareness. The findings revealed that the organizations’ collaboration systems were a positive predictor of employee innovation awareness, with a moderate statistically significant positive correlation between the two variables. Energy organizations in both countries determined that collaboration with private enterprises was likely.

Surviving in a projectified VUCA world requires a quick response to ever-changing policies, political commitments and citizen pressure. Organizations must have an agile structure in place to manage the urgency of response. Climate action policies have declared short-term and long-term plans that necessitate immediate action to avoid a potentially disastrous future. Organizations must train their employees to quickly identify opportunities for innovation to respond to these changes. Experimentation necessitates a capital pool of technical experts, as well as adequate funding, depending on how familiar of unfamiliar the technology and/or the market is to the organization [28] (p. 24). A collaborative approach will ensure that a diverse pool of entrepreneurs, researchers and innovators from other public organization units, private enterprises, and research and academic institutions are available to safely navigate the VUCA territories in the energy sector. This requires public organizations to seek collaboration with other public and private organizations in order to collect relevant data, implement relevant policies and coordinate decisively in order to implement appropriate innovative techniques to avert a climate crisis.

Testing the fifth hypothesis, H5, revealed that Kenya and Iceland innovate differently, with most respondents in Kenya identifying their organizations as innovation generators and less as innovation adopters, while the Icelandic respondents identified their organizations more as innovation adopters than innovation generators. The Kenyan and Icelandic energy organizations, however, both identified less as innovation imitators. These findings support the necessity for emerging economies to invest greater effort in creating new environmental-specific technology, procedures and regulations with regard to climate action innovations being the most vulnerable to climate change impacts [5]. Additionally, the developed economies may have created necessary technologies, processes or regulations to combat climate change, and as a result, they may now be taking a collective approach to the issue, adopting innovations already proven or adequately researched.

The first, second and third hypotheses are concerned with the organization’s innovation culture and, as a result, its ability to innovate. The quote “culture eats strategy for breakfast” by Peter Drucker resonates quite well with public organizations characterized as bureaucratic, with hierarchical structures and rigid leadership. While an innovation strategy is meant to guide a ready, flexible and agile workforce with responsive, flexible and agile leadership, such strategies remain on paper in organizations that have not embraced changes in their structures and management to be aggressively flexible and agile. Actionable innovation strategies require a healthy innovation culture characterized by teamwork, freedom of experimentation, appropriate reward mechanisms, tolerance for diversity, respect and integrity [28] (Ch. 4). While it is difficult to measure motivation, a motivated and qualified workforce manifests in the organizations’ outcomes or performance through improved productivity and customer satisfaction.

The first hypothesis, H1: An organizations’ innovation culture is more positively related to the organizations’ innovation management, structure and leadership, was tested to find out whether the organizations’ innovation strategy, innovation management and leadership had a statistically positive relationship with the organizations’ innovation culture. According to the findings, there was a moderate statistically significant positive correlation between the variables, with an organization’s management leadership and structure having a stronger positive influence on the organization’s innovation culture than the organization’s innovation strategy. Theory agrees that organizational culture and structure support change and innovation in the organization [32].

The second hypothesis, H2: An organization’s innovation readiness is more positively related to the employees’ motivation to innovate despite the country of operation, was tested to determine how employees’ motivation to innovate influences the organization’s innovation readiness based on the country of operation. Employee motivation was found to have a weak positive correlation to organizational innovation readiness, even though it was not a statistically significant predictor of an organization’s innovation readiness. Furthermore, Kenyan energy organizations demonstrated greater readiness for innovation than Icelandic organizations which may come as a surprise. These findings indicate that most employees are motivated by personal reasons and recognition rather than financial rewards, which aligns with entrepreneurs’ expectations for motivation and success to be driven by passion rather than monetary payoffs [21] (pp. 4–6).

The third hypothesis, H3: Public energy organizations’ innovation readiness is more positively related to the employee knowledge, training and competence, was tested to determine the influence of employees’ skill and knowledge on the organization’s innovation readiness. According to the findings, employees’ skills and knowledge were not a good predictor of public energy organizations’ innovation readiness and did not have a statistically significant correlation to public energy organizations’ innovation readiness, as hypothesized. The hypothesis, therefore, did not align as true and was thus rejected based on the results of the empirical analysis. While the hypothesis did not receive empirical support, education and training equip employees with requisite knowledge and skills in solving challenging tasks, hence empowering them to innovate and adapt to changing environments and markets [38].

According to these findings on future electricity generation projects, Iceland and Kenya have the same focus, with geothermal energy being highly likely for future electricity generation due to their rich geothermal energy resource and coal-fired power plants being highly unlikely for future electricity generation despite Kenya’s discovery of coal deposits. However, because of Kenya’s diverse energy mix, hydroelectric, solar and wind projects are more likely than in Iceland. The findings show that both countries’ public energy organizations are focused on implementing low-carbon energy sources for future electricity generation. This was reinforced by both countries’ emphasis on energy transition.

Solving the energy trilemma requires the development and adoption of energy-efficient technologies, as well as collaboration with private enterprises to find the most efficient energy solutions for electricity generation, transmission, distribution, monitoring and storage systems. Energy storage solutions reduce waste during electricity generation and allow for later consumption. According to the findings, Icelandic energy organizations are evenly split when it comes to grid interconnection, electricity market expansion, energy storage, e-mobility infrastructure, net metering and smart grid solutions. Following the shelving of electrical interconnectivity projects between Iceland and neighboring countries [51], there seems to be uncertainty in the expansion of electricity markets and grid-strengthening projects, going by the results of this study. Kenyan organizations, on the other hand, indicated a higher likelihood of undertaking these projects. This is probably due to the availability of grid interconnectivity across national borders, the expansion and upgrading of existing electricity networks and the focused exploitation of geothermal resources and other renewable energy solutions.

Through an empirical assessment of innovation in developed (Iceland) and developing (Kenya) economies, this study has contributed to the body of knowledge on public sector innovation with a specific focus on the energy sector and climate change. The study did, however, have several limitations. First and foremost, the study only looked at the public electricity sub-sector, which included regulation, generation, transmission and distribution organizations where the government had a controlling stake of more than 50%, without considering other energy-related industries such as transportation, manufacturing, building and construction. Second, the study only considered the internal environment (drivers) of innovation of public energy organizations, taking note of the fact that the contribution of external variables is equally important. Third, the time constraints prevented the use of additional data collection and analytical tools such as one-on-one interviews, workshops and focus group discussions, as well as the use of system dynamics or modeling to verify or validate the study’s findings. Fourth, the electricity generation sub-sector received the most responses. The low response rate to the survey in both countries, as well as the low response from other electricity sub-sectors, impacted the statistical analysis’s generalizability. However, it was discovered to be fairly representative in terms of population representation between Iceland and Kenya. Finally, the questionnaires were administered online to save on time and costs, which could have possibly contributed to the low response rate.

Therefore, future research on public energy sector innovation may include transportation, manufacturing, building and construction sub-sectors overcoming the detailed limitations of this study.

6. Conclusions

The goal of this study was to determine if and how public energy organizations innovate in emerging economies, particularly with regard to accomplishing climate change goals. The research sought to suggest areas for development to strengthen the innovation and entrepreneurship culture in public energy sector organizations, rather than to assess the organizations’ innovation performance. The public energy sectors of Kenya and Iceland, two distinct economies, were specifically compared given their similarities in geothermal energy focus despite being geographically and economically poles apart.

The study was conducted through a survey administered to members of public energy sectors. The response rate was, however, low, with a total of 59 responses received by the end of the survey period. Hence, a statistical analysis approach was considered to ensure the data were reliable and valid for the generalization of findings. The majority of the survey participants were middle-level management engineers and scientists with 5 to 10 years of experience in the public energy industry. According to the statistical analysis’s findings, organizations with innovative leadership, management structures, and strategies have better innovation cultures. The organizations’ commitment to the innovation processes and requirements is necessary for there to be a conscious intention to innovate. The study findings show that for organizations to be ready for innovation, their management structure and leadership must be flexible and agile to promote constructive innovation culture characterized by employee creativity and cross-sector and public–private collaborations. The study also revealed that a motivated workforce enhanced an organization’s capacity for innovation, with the majority of employees being driven to innovate for personal rather than financial reasons. Even though it was not empirically proven, it is critical for organizations to prepare their workforce for innovation through education and training programs and the provision of a conducive and motivating working environment.

While it is unimportant how an organization innovates, it is critical that the innovation’s novelty be considered in solving the arising energy challenges, i.e., energy security, affordability and emissions to protect the environment. Innovation “newness” is thus an important feature of innovation made available to customers, i.e., process users and product markets. Icelandic and Kenyan public energy organizations are shown to be making concerted efforts to develop, adopt and imitate new technologies, policies and processes to combat climate change, with most respondents agreeing with their organizations’ innovation pathway for clean, green and efficient energy alternatives and solutions.

Climate action goals are dependent on key sectors’ contributions to innovative approaches to reducing GHG emissions, developing policy frameworks and developing strategic collaboration systems, the majority of which are covered by this study. This study improves the understanding of innovation awareness and readiness in the public sectors. Despite the study’s limitations, the study contributes to innovation research, particularly in the under-researched area of public sector innovation, with a focus on the energy sector being the greatest contributor to climate change.