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Article

Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies

Department of Management and Humanities, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(15), 4254; https://doi.org/10.3390/su11154254
Submission received: 25 May 2019 / Revised: 27 June 2019 / Accepted: 30 June 2019 / Published: 6 August 2019

Abstract

:
Increased greenhouse gas (GHG) emissions in the past decades have created concerns about the environment. To stymie global warming and the deterioration of the natural environment, global CO2 emissions need to reach approximately 1.3 tons per capita by 2050. However, in Malaysia, CO2 output per capita—driven by fossil fuel consumption and energy production—is expected to reach approximately 12.1 tons by the year 2020. GHG mitigation strategies are needed to address these challenges. Cleaner production, through eco-innovation, has the potential to arrest CO2 emissions and buttress sustainable development. However, the cleaner production process has been hampered by lack of complete data to support decision making. Therefore, using the resource-based view, a preliminary study consisting of energy and utility firms is undertaken to understand the impact of big data analytics towards eco-innovation. Linear regression through SPSS Version 24 reveals that big data analytics could become a strong predictor of eco-innovation. This paper concludes that information and data are key inputs, and big data technology provides firms the opportunity to obtain information, which could influence its production process—and possibly help arrest increasing CO2 emissions.

1. Introduction

Increasing attention is being paid towards assessing the role greenhouse gas (GHG) emissions play in the current and future state of the environment. The emission of GHGs, such as carbon dioxide (CO2), has been posited as potentially playing a role in increasing the greenhouse effect, subsequently affecting the planet’s temperatures. This change in the planet’s temperature, known as global warming, could have a catastrophic effect on the natural environment [1,2,3]. Concentration of GHG in the atmosphere has been steadily increasing—primarily driven by anthropogenic activities [4,5,6]. CO2, one of the main GHG [1], needs to be contained to approximately 1.3 tons per capita by the year 2050 in order to avert environmental crises in the future [7]. In Malaysia however, levels of CO2 emission per capita have been on the rise. They are expected to reach 12.1 tons per capita, raising notable concerns about the sustainability of the environment [8]. Therefore, finding solutions towards arresting rising GHG emissions and ensuring sustainable development ought to be a key priority amongst the various industries and stakeholders involved [9].
Consumption of fossil fuels is one of the primary drivers of CO2 emissions [10,11]. Malaysia, which enjoys abundant reserves of oil and gas, has managed to generate adequate revenues and grow the economy from this resource [12]. As a result of economic growth, Malaysia’s oil and gas consumption has also continued to grow, spurring further economic growth [13]. This has resulted in an increased reliance on oil and gas in Malaysia’s energy mix, with the majority of energy derived from fossil fuels [14]. However, this reliance on fossil fuels could be problematic on two fronts—firstly, the depletion of fossil fuels reserves [15] and secondly, inefficiency in the use of these fossil fuels for energy [16,17]. Therefore, there is a need to increase energy efficiency [17], as this may subsequently proliferate the efficient use of non-renewable energy resources. The greater efficiency will, in turn, help to tackle the growing GHG emissions.
Industry 4.0 promises to be a nexus for greater energy efficiency [18,19]. By making processes such as energy generation, transmission, and distribution smart, energy efficiency can be greatly improved [20]. Big data has been heralded as a key proponent in smart technology [21]. Hence, the ability to assimilate big data for increasing energy efficiency is crucial [22,23]. However, the full contextualization of big data has been problematic [24], and it has yet to be fully utilized towards increasing energy efficiency [25].
Interestingly, Industry 4.0 technologies such as big data analytics have displayed immense potential to drive improved energy efficiency and subsequently sustainable development [23,26] through fostering eco-innovations [27,28]. Eco-innovation has been proposed as being key to reducing negative environmental impacts and achieving sustainable development [27,29]. In fact, such is the increasing importance of eco-innovation—it has been demonstrating its potential ability in tackling rising environmental and ecological issues [30]. Hence, by improving environmental sustainability, eco-innovations are actively contributing towards sustainable development [31]. The adoption of eco-innovations has also been suggested as contributing to greater energy efficiency [32].
Whilst attention towards eco-innovation has been increasing, literature on the level of eco-innovation practices, and its impacts has yet to peak [33]. Investigations that explain the linkage between big data and analytics to eco-innovation, especially through quantitative models, are scant [34]. Despite the key role played by energy sector, investigations that specifically focus on energy sector firms’ applications of big data towards eco-innovation are lacking [35].
This paper is motivated by the gap currently existing in the literature and thus, aims to explore the influence of big data analytics capability towards process eco-innovation. To explore this relationship through quantitative means a research model is proposed comprising of the capabilities of big data analytics. Hence, the objective of this paper is to investigate the influence of big data analytics capabilities on process eco-innovation of energy firms.
The rest of the paper is organized as follows: Section 2 consists of the materials and methods, including the review of literature. The results, discussion, and conclusion are found in Section 3, Section 4 and Section 5 respectively.

2. Materials and Methods

2.1. Development of Research

2.1.1. Big Data Analytics

‘Big data’ is a phrase often used to describe enormous amounts of data, collected for the purpose of processing [36]. This enormous amount of data is described in terms of its three key characteristics—i.e., volume, velocity, and variety [37].
Volume refers to the amount of data being generated [37]. Industry 4.0 technologies such as Internet of Things (IoT) and cyber-physical systems (CPS) have enabled firms to capture more parameters of data, hence increasing the required digital space to store this data. For example, sensors on aircraft jet engines, which use kerosene as a combustion fuel, generate hundreds of terabytes (TB) worth of data per flight operation. When this is put into perspective, a single flight alone can potentially generate far more data than what some firms have generated over their lifetime [38].
Velocity is concerned with the speed at which the data arrives. The technologies underpinning Industry 4.0 enable rapid and real-time transfer of generated data [39]. For example, an RFID chip placed in a multiphase pipeline enables the sensors measuring the displacement and corrosion rates to communicate the data in real-time [40]. Variety, meanwhile, is concerned with the different formats or types of data. Data can basically be broken down into three main categories: structured, semi structured, or unstructured [41].
Structured data is data that is organized, has a defined format—i.e., numeric or alphanumeric—and can be stored in a database. The presence of separators makes data not have a defined format—thus making it fall into the semi-structured category [42]. Lastly, data that is maintained in the format that it was captured is classified as unstructured data. Examples of this data include natural language, audio, or images. This data is usually difficult to process via conventional software techniques and databases [42].
In order to leverage any value from this data in its different formats, firms need to have the relevant analytics technology to ingest, store, model, and secure this data [43,44]. Hence, firms seeking to improve their performance may realize performance gains through the utilization of analytics to process this data [45]. Structured data, which is storable in a database, can be processed by various analytics systems. However, it is also the least amount of data compared to the other two categories [45].
Semi-structured and unstructured data, which is more readily available to firms that structured data, require more advanced analytics, algorithms, and software to process and secure. As a result, firms are now developing more advanced analytics techniques such as machine learning, to analyze their semi-structured and unstructured data and improving their process efficiency [46]. Other firms are also developing capabilities in advanced analytics such as deep learning and natural language processing (NLP) to optimize their business processes activities such as product development, manufacture, and distribution [47]. Deep learning, which essentially bundles the dual functions of feature learning and model construction in one model, can potentially enhance business process efficiency through mapping and modelling complex input/output relationships in the production process [48].

2.1.2. Eco-Innovation

As was noted by the Brundtland Commission of 1987, mounting environmental challenges have necessitated the need for corrective action to ensure the longevity and foreseeable functioning of the biosphere, which houses both industry and people [49]. The transition towards sustainable development, which looks at ensuring economic growth without damaging the planet as well as prospects of tomorrow’s generations [50], requires a shift from the “business as usual” attitude. All stakeholders—chiefly firms, governments, and industries—ought to be involved in addressing sustainable development challenges.
One method that provides stakeholders such as firms the pathway to address the mounting environmental challenges in economically viable way is eco-innovation [51]. This is because eco-innovation goes an extra mile in that it is centered on the environment and economy. Such aims contribute towards sustainable development by reducing the ecological footprint of firms [52]. Eco-innovation has therefore demonstrated its ability to drive firms’ transition towards sustainable development [53,54]. This is perhaps due to the nature of eco-innovation—defined as innovation that seeks to improve environmental sustainability [55]. By improving environmental sustainability, eco-innovations are contributing towards sustainable development.
Eco-innovation has also been conceived as any innovation which aims to reduce negative impact to environment. Eco-innovation can also be used to refer to products or processes which buttress sustainable development [56]. Eco-innovation is also closely linked to changes in product and production process technologies [57,58]. Therefore, in this study, eco-innovation refers to new or significantly improved products or processes that support sustainable development through environmental and economic means.

2.1.3. Overview on the State of Big Data Analytics and Eco-Innovation

Enhancing firms’ knowledge transfer mechanisms and innovation networks can improve the flow of information [59]. Having more knowledge and information can enable firms to identify and exploit eco-innovation opportunities [59]. This is because information and knowledge are key resources and capabilities that can buttress breakthrough innovations [60,61]. Big data analytics, through the increasing parameters and amount of data at its disposal, allows firms to generate information and knowledge that may be utilized towards churning out eco-innovations [62]. As sustainable development has become the benchmark to address the pressing global environmental issues, eco-innovation should be embedded in organizations’ activities [63].
Big data analytics also enables real-time knowledge about market conditions. Hence, this knowledge can subsequently catalyze the ability of firms to incorporate servitization through the innovative combination of products and services (product-service systems) (see Stock, Obenaus, Kunz, and Kohl [34]). As firms practicing eco-innovation move towards the servitization of their business models [64], embracing big data analytics necessitates the usage of information by the various departments and divisions of the firm to increase productivity in a sustainable manner [65]. Other potential benefits of eco-innovative firms using servitization in tandem with big data analytics includes lower production costs and risks [66,67]. In addition, real-time optimization of production processes can be conducted, resulting in a better firm and production fit, as well as increased operational efficiency [68]. Hence, big data analytics has the potential to impact sustainable development through its utilization in eco-innovation.
The rising prominence of eco-innovation is being buttressed by various enablers. Examples of such as enablers include renewable energy policies or niches like carbon offsetting trading [69]. In order to arrive at a leaner understanding of eco-innovation, most research has actively sought to discuss drivers of eco-innovation [70]. Besides identifying drivers, other eco-innovation research has also attempted to understand the impact of drivers [71].
However, several areas are still lacking in the literature. To begin with, research that assesses firms’ level of eco-innovation practices, and the impact of these practices have on the firms, is still scant [72]—particularly from a strategic resource and capability perspective [73].
Secondly, Industry 4.0 has proliferated big data analytics [74]. This rapid increase in big data analytics has gifted firms the opportunity of turning information and knowledge into a competitive advantage at faster rates, even in real-time [75]. Even though big data analytics has the potential to drive eco-innovation, research confirming this notion is still in its infancy [76]. Investigations using quantitative methods are lacking, especially the ones that specifically measure the ecological impacts of big data analytics technology [34,77,78]. In fact, ecological dimensions have been omitted from previously developed models [79]. Hence, it is vital for more empirical research that maps big data analytics to eco-innovation.
Third, current big data and analytics research has yet to fully incorporate human capital dimensions. Hence, there is a need for research to consider the human capital aspect of big data and analytics implementation in eco-innovation, especially from different geographical contexts. This is important, given that this is an emerging field and political as well as industrial approaches may differ based on different cultures and acceptance of technology [68].
Fourth, research has mostly taken a broad approach to applications of big data and analytics. However, studies have been unable to consider the possible sectoral differences due to different polluting potentials—i.e., some industries may potentially be more susceptible to polluting that others due to their nature of business [80]. In fact, research has yet to specifically focus on the energy sector, despite its increasing application of eco-innovations [80]. Research ought to assess the impact of digitization capabilities on sustainable development efforts of the energy sector [80].

2.1.4. Resource-Based View

Firms need resources in order to function, as resources form the foundation for a firm to convert inputs into outputs—generating value in the process [81]. Resources can be turned into capabilities, which in turn, can possibly lead to a sustained competitive advantage [82]. Without resources, firms would not be able to produce value and the subsequent competitive advantage [83,84,85]. Therefore, the resource-based view places importance on the critical role that resources play in attaining and sustaining a competitive advantage.
The resources of a firm not only incorporate assets [86], but also includes information, knowledge, and firm processes [87,88]. Information and knowledge are important, due to their key role in the firm’s information processing ability [89,90]. Information processing often influences the decision-making of the firm [91]. Decision-making itself is critical as numerous decisions which influence the firm’s performance (such as the production, process, or service dimensions of the firm) need to be made [92]. Hence, gathering more information enables firms to arrive to better decisions [93].
Information is also critical as it can expand the firm’s knowledge, further enhancing the firms’ decision-making process. Information and knowledge in themselves are key resources. Information and knowledge are the key resources for decision making [94]. As a result, utilization of these resources results in competency and enhanced decision making—ultimately leading to a competitive advantage [95]. The key to information and knowledge is data [96]. This data can be analyzed and processed for value to be unlocked from it [97,98]. To tap into massive amounts of data (big data), firms need the systems and technologies [96].
Generally, information communication technologies have been rapidly developing and evolving, affording firms new methods for gathering information and knowledge. Examples of such technologies include cloud computing, IoT, and CPS [44]. These technologies, which consist of a mix of hardware and software, provide platforms for big data analytics through their intra-communication, inter-communication, and connectivity [44]. Having the above information technologies can enable establishment of big data services [99] and provide firms with the platform to develop analytics capability [100]. Hence, big data analytics systems can work in tandem with information communication technologies, in sifting data [101].
When observed from the resource-based view, the capability for big data analytics can propel firm performance [102,103]. Because data is sourced from various departments or divisions, cross functional co-ordination is improved, consequently helping to bridge information gaps in the firm and remove silos [100]. However, without the necessary skills to manage the analytics systems, firms would not be able to generate value from this information and knowledge [104]. Therefore, the human capital is one of the three vital pillars of big data analytics capability [104,105]. Human capital (through their skills and abilities to carry out analytics related tasks) enables firms to leverage the data [106].
By adopting the resource-based view, this paper conceptualizes that big data is a critical resource, which enables firms to develop capabilities. These capabilities in turn, enable firms to develop a competitive advantage. Big data analytics is thus summated by the three capabilities identified earlier in the literature, which are information technology [77], management [97], and personnel expertise [107].

2.1.5. Information Technology Capability

Whilst research has expounded on the technical implications of Industry 4.0, there is a lack of research that maps Industry 4.0 technological capabilities to economic and ecological benefits [108]. More research mapping Industry 4.0 capabilities is necessary, particularly as firms create environments for sensors for data collection from the evolving information communication technologies [109]. Consequently, this creates numerous big data points of origins. Adding onto the proliferation of big data collection are other Industry 4.0 technologies such as CPS, IoT and Cloud computing, presenting a challenge for firms in terms of convergence, fusion, and processing of this data from numerous sources [100].
This complex challenge of big data, however, is also an opportunity. Big data is an asset for firms, as it is helpful towards problem solving. In addition, it assists in improving efficiencies in firm processes, activity co-ordination and production planning [110]. Hence, the usage of big data can enable firms to churn out more innovations, sustaining competitive advantage and/or cutting costs in its production processes [111]. Big data analytics information technology could potentially impact firm strategic processes such as innovation capability [77,78,105,112]. Therefore, we hypothesize the following:
Hypothesis 1 (H1).
Big data analytics information technology capability is positively related to process eco-innovation.

2.1.6. Personnel Expertise Capability

The ability for a firm to leverage its analytics is dependent upon its human capital [113]. Even if firms have invested in state-of-the-art analytics technology to collect and manage the data, the skill sets of the analytics personnel available within the firm is a critical link to its analytics capability [105]. Research has also indicated that having appropriately skilled personnel can positively impact the firm [114]. For instance, it is theorized that skilled and experienced personnel can understand and draw conclusions from the large volumes of data that have been mined and aggregated [115]. Skilled personnel such as data scientists could support decision-making of firms through generating new understandings from data streams [116]. Hence, harnessing the human capital to leverage value from big data can be instrumental towards eco-innovation related activities such as product development or process refinement [117]. Human capital can also be vital in generating more value from big data through visualization and analysis at critical levels such as product/process design [118]. Despite these findings, research that investigates the contributory played by a firm’s personnel in analytics is scant. Therefore, we propose the following hypothesis:
Hypothesis 2 (H2).
Big data analytics personnel expertise capability is positively related to process eco-innovation.

2.1.7. Management Capability

In addition to information technology, having the ability to manage and utilize the big data can be a significant contributor to organizational performance, as firms that utilize big data analytics could experience an increase in their performance [119]. In fact, management capability could positively support performance through influencing the innovative ability of firms, together with its processes, products, and services [120,121,122]. Another reason for the upsurge in performance could be stemmed from the application of analytics to the organization’s innovation process [111]. The applicability of big data analytics to the eco-innovation process of the organization could be useful in decision making [123], helping to determine the firm’s eco-innovation strategy and configuration [124]. One practical example of this potential usage of big data in decision making and strategy is its application to eco-product lifecycle management [109] and new eco-product development processes [125]. Despite these facts, research has also noted the need to infuse a multi-faceted approach in mapping capabilities to sustainable value creation [79]. As a result of the above, we propose the following hypothesis:
Hypothesis 3 (H3).
Big data analytics management capability is positively related to process eco-innovation.
The hypotheses proposed in this study, illustrating the relationship between big data analytics and eco-innovation, are exhibited by Figure 1 below:
Information technology, personnel expertise, and management are the independent variables whilst process eco-innovation is the dependent variable.

2.2. Methodology

2.2.1. Data Collection and Sample

In order to examine the influence of big data analytics capabilities towards process eco-innovation, this paper adopted a quantitative approach. This approach was undertaken as it enables this study to evaluate and map the three identified capabilities towards process eco-innovation in a deductive manner. Applying a quantitative research design is necessary as it enables the researchers to follow the rigorous procedures necessary to collect the data appropriately and achieve the objectives of the study.
This paper focuses on process eco-innovation of energy companies. The focal firms are, therefore, energy companies active within Malaysia. The sample is drawn from energy companies listed on Bursa Malaysia. This sample was chosen as public listed companies have access to a larger resource base, enabling them to readily adopt and practice process eco-innovation [128]. In fact, listed energy firms, have more resources to allocate towards research and development, a critical innovation function [129]. This enables them to be more eco-innovative, and thus a more appropriate representative.
Given the nature of questions in this study, the target respondents were operations managers of the sample of companies—drawn from the 41 energy and utility companies listed on the Bursa Malaysia Main Market as of the 1 June 2018. These listed energy and utility companies, which include the major oil and gas firms operating in Malaysia, collectively have a market capitalization of approximately RM190 billion on the Bursa Malaysia Main Market [130]. The companies were therefore ranked based upon their market capitalization, satisfying the requirements of a sampling frame [131].
Initial data collection for this study consisted of obtaining contact information of energy and utilities companies listed on Bursa Malaysia as of the 1 June 2018. After obtaining contact information, an invitation letter and memo to participate in the survey was distributed to the companies via electronic mail. Electronic mail provides an avenue for the researchers to communicate with the firms, as firms listed on Bursa Malaysia are required to have an electronic mail [130]. The data collection was then conducted via a questionnaire, consistent with the quantitative approach. The questionnaires were distributed in June 2018. Following distribution of questionnaires, the researchers followed up the survey via telephone call 7 days after sending them out. Follow up phone calls were also initiated at 14 and 21 days, respectively. In total, 41 questionnaires were distributed. Out of 41 questionnaires distributed, there were 32 completed questionnaires with complete responses for this preliminary study, representing a response rate of 78%. This number also satisfied the recommended minimum sample of an exploratory study [132,133].

2.2.2. Measures

Undertaking a review of the literature enabled this study to identify the analytics and eco-innovation indicators. The measurement scales for capabilities were hence identified from existing literature discussing big data analytics. The measurement scales for process eco-innovation were identified from existing literature discussing eco-innovation.
The three identified capabilities (information technology [77], management [97], and personnel expertise [108]) and their expected positive influence on strategic functions such as innovation consisted of 12 adapted variables each [126,127,134]. The measurement scale of process eco-innovation was derived from literature that identified six variables for its measurement [135].
Prior to distribution of the questionnaires, the adapted scales were face validated by experts from industry, policy, and academia to ensure that the adapted items were correctly worded within the context of their usage. Minor adjustments and refinements were then made to the wording of some of the items.
The adapted multiple-items constituting the capabilities as well as process eco-innovation were measured using a seven-point Likert scale—ranging from 1 (strongly disagree) to 7 (strongly agree). The seven-point scale was chosen because of its reliability [136]. An overview of the items can be found in Appendix A.

2.3. Data Analysis

To evaluate the three hypotheses proposed by this study in Section 2, multiple regression analysis was utilized [137]. Regression analysis has been instrumental in modern research, hence its continued use within management research [138].
Given that the aim of this study aims is to predict the influence of big data analytics on process eco-innovation, a multiple regression is an acceptable and beneficial approach. This approach is the most appropriate given the small sample size, as multiple regression analysis provides an adequate level of statistical power despite small sample sizes [139]. In addition, the model developed by this study in linear in nature, as highlighted in Figure 1. Hence, multiple regression analysis allows this study to distinguish the individual prediction ability of the identified capabilities [137], further developing theory. Statistical Package for Social Science Version 24 was the software package used by this study to undertake the multiple regression analysis.

2.4. Model Validation

To validate the developed model, this study followed the established procedures associated with multiple regression [140]. Normality tests were conducted first to assess the check whether the data was normal, then followed by a battery of checks that included linearity, homoscedasticity, as well as reliability [140]. Table 1 below provides the results of the procedures undertaken to validate the model.
The skewness and kurtosis ranged between −1.2 to 1.2, indicating that data is normally distributed [141,142]. The relationship between the variables exhibited a linear distribution pattern indicating linearity and homoscedasticity. Internal consistency of the items constituting the variables was measured using Cronbach’s alpha. Cronbach alpha values of 0.6 and above give an indication that the variables are acceptable and reliable [143,144]. All three variables had Cronbach alpha scores above 0.90, indicating high internal consistency [144]. Hence, all items constituting the variables were retained.
The correlation amongst the variables were also assessed, to detect if they may highly correlated. The correlation was assessed using the variance inflation factor (VIF) scores of the explanatory variables. VIF scores above 10 may indicate possible multicollinearity [145]. In addition, a correlation matrix above 0.9 suggests multicollinearity [140]. Both the VIF and correlation matrix of are below the established threshold, indicating that multicollinearity is not a concern in this study. Table 2 below indicates the VIF scores for this study’s predictor variables.
Correlation assessment is also an important step taken prior to running the regression is to establish the correlation of the variables [139]. The results in Table 2 also indicate that all three independent variables are significantly correlated to the dependent variable.

3. Results

3.1. Correlation Analysis

Correlation assessment is also an important step taken prior to running the regression analysis, as it helps to validate the research hypotheses [139].
The results in Table 3 indicate that all three independent variables are significantly correlated to the dependent variable, validating the hypotheses in this study.

3.2. Hypothesis Testing

In order to test the hypothesis, the researchers performed a regression analysis. SPSS Version 24 was utilized to conduct the regression analysis. We entered the variables block by block, hence creating three models. Model 1 is information technology. Model 2 includes both information technology and management. Model 3 is inclusive of all three variables. The results from these models are presented in Table 4, thus explain the outcome of the hypothesis testing.
Hypothesis 1 proposes that information technology capability has a positive effect on the process eco-innovation of energy firms. The empirical results from Model 1 highlight a significant and positive relationship between information technology and process eco-innovation as R2 = 0.366, β = 0.605, and p < 0.05. The proposed Hypothesis H1 is thus, accepted.
Hypothesis 2 posits that the expertise of personnel positively affects process eco-innovation of energy firms. The results from Model 2 seem to indicate a positive but statistically insignificant relation (β = 0.177, p > 0.05) between personnel expertise and process eco-innovation. From Model 1 to Model 2 the R2 increased by 0.013. The F-test, used to scrutinize if there was a significant change in the square of the multiple correlation coefficients, also was statistically insignificant. Hence, the Hypothesis H2 is rejected.
Hypothesis 3 suggested that management capability had a positive influence towards the process eco-innovation of energy firms. There was no increase in the variance from Model 2 to Model 3 (ΔR2 = 0.000), suggesting a statistically insignificant and negative relationship (β = −0.029, p > 0.05). The F-test also indicated a statistically insignificant relationship. The suggested Hypothesis H3 is thus rejected.

4. Discussion

4.1. Capabilities and Process-Eco-Innovation

This study sought to mainly investigate how process eco-innovation in energy firms is influenced by big data analytics capabilities. Conceptual and empirical studies posit that Industry 4.0 technologies such as big data and analytics have a formidable role to play in addressing sustainable development challenges [28,34,146]. This study contributes towards understanding Industry 4.0 from a unique perspective. Another contribution is the applicability of a distinct Industry 4.0 perspective in addressing a specific area of sustainable development—the environment. In addition, this study focuses on energy firms. Energy is a critical area for sustainable development as numerous industries are linked to it [147]. This study’s results seem to suggest that big data analytics capabilities could possibly influence process eco-innovation of energy firms—which concurs with our theoretical assumptions. However, not every capability significantly influenced process eco-innovation.
Information technology proved to be an important capability in predicting process eco-innovation. As has been highlighted in Section 2.1.2, information technology plays a critical role in big data analytics, as it provides firms the vehicle to mine data from various points. Information technology also enables firms to mine higher quality data and information. High quality data enables energy firms to have a better understanding of their operating environment, enabling them to make better predictions and more efficient operational decisions. Other research corroborated this finding, as information technology is essential in mining higher quality data [148]. Operational decisions can have large implications towards the sustainable development of the firm. Hence, it is critical that energy firms collect as high-quality data as possible. This notion is in accordance with previous studies that highlighted the importance of information technology to collect data for operational decision making of energy firms [146,149]. Without the latest information technology, energy firms would have to rely on secondary or redundant sources to obtain data for their decision-making. This would severely hamper their strategic decision making, as operational, financial, and strategic decisions need the latest, up-to-date information from various points. Previous research also concurs with this viewpoint [150] particularly for energy firms [151].
Personnel expertise was not a significant predictor to process eco-innovation. In the case of Malaysia, this provides an interesting result. Although firms are investing in state-of-the-art information technology to obtain more data, Industry 4.0 and its sub-sets such as big data analytics are still in their infancy. In fact, in the context of Malaysia, numerous firms have yet to develop all the relevant capabilities to leverage the big data. Other research has indicated that Malaysian companies have an interest in Industry 4.0, but they are not fully ready [152]. The lack of preparedness for Malaysian firms can be attributed to personnel expertise. Hence, whilst the information technology is present, there is yet to be a sufficient pool of data qualified and experienced personnel, similar to what other research has found [153,154,155]. Malaysia also, may be facing such a situation, as also found by other research [156]. In addition to a potential shortage of personnel, some of the personnel in Malaysian companies are skeptical of Industry 4.0 technologies which includes big data, suggesting possible resistance [157,158].
Unsurprisingly, management capability did not yield any significance towards predicting process eco-innovation. Without the necessary human capital, managing the big data would remain a challenge for firms. Management of big data is hinged upon the human resource that is at the disposal of the firm. Other research has confirmed similar findings, given that without the right skills it is not possible to manage well [159]. In fact, in the case of energy firms in Malaysia, the results seem to suggest that management capability negatively influenced process eco-innovation. The reason for this could be due to personnel. Without appropriately qualified and experienced personnel, wrong inferences and understandings may be deduced from the collected big data. Hence, wrong strategic, operational or financial decisions could be made—at the detriment of the firm. Other research confirms this argument [160,161,162].

4.2. Implications for Theory

Firms need to develop a sufficient complement of capabilities in order to establish innovativeness [163]. Innovativeness, is increasingly becoming indispensable, given the hyper-competitive nature of the modern business environment [164]. Adding on to the hyper-competitive business landscape are mounting environmental challenges facing the planet. Firms, especially those of the energy sector need to shift towards sustainable development by focusing on environmental innovations. As an extension of the resource-based view (RBV), eco-innovativeness can be a new frontier for energy firms’ competitive advantage. Energy firms can develop Industry 4.0 capabilities such as big data analytics. The more valuable, rare, costly to imitate, and non-substitutable the big data analytics capabilities of the firm is, the more competitive it can be. Other research also supports this viewpoint, as firms are adopting proprietary analytics systems to have a competitive edge and outperform rivals [165]. Hence, energy firms can also derive eco-innovativeness from big data analytics capabilities. Eco-innovativeness will enhance energy firms’ economic gains whilst concurrently minimizing harm to the environment.
As new challenges and technologies emerge, new capabilities and the drivers of these capabilities change, competitive advantage takes on a new dimension. However, the RBV is still relevant today, particularly in applying resources and capabilities towards tackling the wave of environmental challenges facing the planet. Tackling environmental challenges in an economically sustainable way has thus revolutionized what constitutes a competitive advantage. Evidence of this notion can be seen by the rise in energy companies that conceptualize innovative ways to tackle environmental challenges. In fact, other research supports this, as more energy companies are shifting away from the traditional approach and beginning to base their competitiveness on tackling environmental challenges [166].

4.3. Implications for Managers

In addition to theoretical implications, this study highlights some interesting managerial implications. Firstly, given the importance of analytics capabilities for developing eco-innovativeness, managers need to allocate more financial resources to develop and maintain high capability levels. As has been pointed out by this study, personnel expertise did not contribute to process eco-innovation. As a result of not having the personnel, management capabilities are impacted. Although these capabilities are distinct, they never-the-less hinge on each other. For without the information technology, energy firms cannot mine data. If firms indeed do have the necessary information technology to mine data, without the required human capital, they will not be able to generate any value from it. Therefore, energy firms should not only focus their R&D investments towards technology acquisition only, but they must also intensify their training and development expenditure, particularly towards developing Industry 4.0 human capital. Data scientists and other Industry 4.0 ready personnel can be the resource that drives big data analytics capabilities of energy firms. Other research seems to support this perspective, particularly in the Malaysian context [156,158].
Secondly, although commercial analytics technology, applications and systems are readily available, intensifying R&D expenditure towards human capital can enable energy firms to develop their own proprietary analytics. Developing solutions in-house, although costly, can give energy firms an even greater competitive edge compared to rivals. In addition, trade secrets, formulas, and other sensitive data can potentially be compromised in the hands of third parties. Hence, housing data and doing the analytics in-house enhances data security, integrity, and can give energy firms an edge. Earlier research has also supported this argument [167,168]. However, this can only be realized with the appropriate human capital which is Industry 4.0 ready.

4.4. Limitations and Future Research

This study was not without limitations. Firstly, although gathering momentum, research on Industry 4.0 and its subset—big data analytics is still in its infancy. Hence, the big data analytics capabilities identified are within the current knowledge available. As more knowledge on the subject is added, other capabilities may be identified. Secondly, this study employed quantitative methods. Future research could employ a different approach. An approach with a qualitative element such as mixed methods approach. Taking this approach could possibly unearth other capabilities, which can then be quantitatively tested for significance.
Secondly, this study focused on the influence of one aspect of Industry 4.0 towards process eco-innovation of energy companies. Future research could thus explore how other Industry 4.0 dimensions such as additive manufacturing, cloud computing, cyber-physical systems, and Internet of Things may impact process eco-innovation. Another dimension worth further exploring is automation, particularly its applications within advanced analytics (unsupervised machine and deep learning, etc.).
Third, only one dimension of eco-innovation was considered in this study. Future research could also explore the effect big data analytics capabilities have on other eco-innovation dimensions such as product, service, marketing, and organization.
Fourth, only public listed energy firms were considered in this study. Future research could also focus on non-listed energy firms. The results may then be used in a comparative study to produce meaningful cross-sectional findings, especially since innovativeness is also key to the survival of small-to-medium enterprises (SME’s).
Also, the results of this study were captured in one moment in time. Longitudinal studies could analyze the influence of time on big data analytics capabilities on process eco-innovation.
Lastly, the sample size is relatively small. The researchers faced challenges increasing the response rate. Because of the size of the sample, caution must be exercised with generalizing the results. Further investigations with a larger sample size would help to reaffirm the findings of this study.

5. Conclusions

Despite its small sample size, this study makes meaningful theoretical and managerial contributions. Eco-innovation is an emerging area of research. Interest in this research area is increasing as firms strive to become more sustainable in their economic functions. Furthermore, eco-innovation could usher in sustainable development. Energy firms can realize economic performance gains whilst reducing harm to the environment by adopting eco-innovations. For example, energy generating firms can adopt eco-innovations such as combined cycle power plants to increase efficiency. Increased efficiency leads to better economic and environmental performance. Energy distributing firms can also adopt eco-innovations, such as smart grids, to improve their energy efficiency, leading to better performance. Oil and gas firms can adopt also process eco innovations such as carbon capture and utilization (CCU) to reduce CO2 emissions and costs. CO2 from energy generation using fossils fuels can be captured and utilized. Other examples of process eco-innovations can be utilization of captured CO2 in enhanced oil recovery (EOR). Since Industry 4.0 technologies can facilitate eco-innovativeness, this study aimed to analyze the potential influence the capabilities of big data analytics towards process eco-innovation. Information technology, personnel expertise, and management capabilities are all capable of driving process eco-innovation. However, in the case of Malaysian energy firms, information technology capability was the strongest predictor. Hence, energy firms are able to mine more data due to this capability. This means that energy firms in Malaysia are readily able to provide higher quality data for process eco-innovations such as CCU. Oil and gas producers can immensely benefit from big data analytics with their operations such as EOR. However, despite having the information technology, Malaysian energy firms seem to lack the human capital to capitalize on this data. Information and knowledge are key aspects in firm’s strategic decision making. Hence, energy firms in Malaysia could possibly invest more on their big data analytics teams and personnel to ensure that they extract value from the data sourced from their information technology.

Author Contributions

Conceptualization, R.T.M. and S.K.J.; Methodology, R.T.M. and S.K.J.; Writing—original draft, R.T.M.; Writing—review & editing, R.T.M. and S.K.J.

Funding

This research was supported by Yayasan UTP Fundamental Research Grant (YUTP-FRG) 015LC0-016.

Acknowledgments

The authors would like to thank the Department of Management and Humanities for their support. The authors would also like to thank the four anonymous reviewers whose careful reading and valuable comments significantly improved the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Summary of Indicator Scores.
Table A1. Summary of Indicator Scores.
VariableFactorMeanSD
Information Technology Capability [126]Analytics systems: better than competitors5.031.49
α = 0.950Data sharing intraorganizational: branch office connection to main office5.581.29
Network connectivity: utilization of open systems.5.101.01
Intraorganizational communication: removal of communication barriers in sharing analytics results4.931.27
Data integrity: security and firewalls5.031.42
Ease of distribution of software (apps) for multiple analytics platforms4.891.44
Transparency and ease of access: interface of applications and platforms5.031.37
Intraorganizational communication: ease of sharing of analytics driven information5.031.34
External end-users of big data: provision of points of entry4.961.20
External end-users of big data: allow creation of external analytics apps through object-oriented modules5.511.27
Analytics software: utilization of reusable modules.4.961.17
Analytics software: usage of object-oriented technologies.5.200.94
Analytics software: adaptability of applications5.751.12
Management Capability [126]Usage of big data analytics: strategic innovation5.481.21
α = 0.964Usage of big data analytics: planning5.341.14
Usage of big data analytics: systematic organizational processes5.061.30
Usage of big data analytics: adaptability to dynamic conditions5.131.32
Usage of big data analytics: organizational investment decisions5.000.92
Usage of big data analytics: organizational human resource impact5.311.28
Usage of big data analytics: end-user decision making5.481.05
Usage of big data analytics: end user training requirements4.891.17
Intraorganizational usage: line and analysts’ interaction frequency5.101.01
Intraorganizational usage: cross-functional meetings frequency5.241.09
Intraorganizational usage: line and analysts co-ordination5.171.10
Intraorganizational usage: information sharing and ease of access between line and analysts5.311.00
External communication: outperform competitors5.171.10
Competitiveness: we are more cost effective than competitors5.201.28
Competitiveness: we boast more advanced analytical methods than competitors5.061.06
Competitiveness: we are better at information gathering than competition5.310.92
Personnel Expertise [126,127]Big data experience: analytics personnel capability4.961.32
α = 0.974Programming skills: analytics personnel capability5.311.16
Management of project lifecycle: analytics personnel capability5.410.98
Network management and maintenance: analytics personnel capability5.131.15
Decision support systems: analytics personnel capability5.131.15
Technological development trends: analytics personnel ability to understand trends5.061.13
Technological development trends: Analytics personnel learning ability of new technologies5.241.15
Organizational assimilation: analytics personnel knowledge levels of critical factors for the success of organization4.961.37
Organizational assimilation: analytics personnel knowledgeable about the role of big data analytics5.371.11
Organizational assimilation: Analytics personnel’s understand of organizational policies and plans5.511.24
Technical solutions: analytics personnel capability5.411.11
Business functions: analytics personnel knowledge5.241.18
Business environment: analytics personnel knowledge5.131.12
Project planning, leading, organizing, and controlling: analytics personnel capability5.241.21
Execution of work: analytics personnel capability5.171.19
Teaching others: capability of analytics personnel5.241.12
Customer relationship: ability of analytics personnel to maintain productive relationships with users and clients5.371.01
Process Eco-Innovation [135]Lowering consumption of energy during production4.481.29
α = 0.956Reuse of material4.681.10
Adoption of cleaner technology4.581.11
Reduction in emissions and waste generation4.931.09
Reduction in raw material usage4.621.14
Energy saving technology adoption4.721.16

References

  1. Barbera, A.C.; Vymazal, J.; Maucieri, C. Greenhouse Gases Formation and Emission. In Encyclopedia of Ecology, 2nd ed.; Fath, B., Ed.; Elsevier: Oxford, UK, 2019; pp. 329–333. [Google Scholar]
  2. Huang, J.-B.; Wang, S.-W.; Luo, Y.; Zhao, Z.-C.; Wen, X.-Y. Debates on the Causes of Global Warming. Adv. Clim. Chang. Res. 2012, 3, 38–44. [Google Scholar]
  3. Shukla, J.B.; Verma, M.; Misra, A.K. Effect of global warming on sea level rise: A modeling study. Ecol. Complex. 2017, 32, 99–110. [Google Scholar] [CrossRef]
  4. Mikayilov, J.I.; Galeotti, M.; Hasanov, F.J. The impact of economic growth on CO2 emissions in Azerbaijan. J. Clean. Prod. 2018, 197, 1558–1572. [Google Scholar] [CrossRef]
  5. Fraile, I.; Arrizabalaga, H.; Groeneveld, J.; Kölling, M.; Santos, M.N.; Macías, D.; Addis, P.; Dettman, D.L.; Karakulak, S.; Deguara, S.; et al. The imprint of anthropogenic CO2 emissions on Atlantic bluefin tuna otoliths. J. Mar. Syst. 2016, 158, 26–33. [Google Scholar] [CrossRef]
  6. Kumar, M.K.; Shiva Nagendra, S.M. Quantification of anthropogenic CO2 emissions in a tropical urban environment. Atmos. Environ. 2016, 125, 272–282. [Google Scholar] [CrossRef]
  7. Princiotta, F.T. Global climate change the CO2 per capita challenge. In Air and Waste Management Association—Addressing Climate Change: Emerging Policies, Strategies, and Technological Solutions; Air and Waste Management Association: Oak Brook, IL, USA, 2016; pp. 101–105. [Google Scholar]
  8. Yii, K.-J.; Geetha, C. The Nexus between Technology Innovation and CO2 Emissions in Malaysia: Evidence from Granger Causality Test. Energy Procedia 2017, 105, 3118–3124. [Google Scholar] [CrossRef]
  9. Hannan, M.A.; Begum, R.A.; Abdolrasol, M.G.; Hossain Lipu, M.S.; Mohamed, A.; Rashid, M.M. Review of baseline studies on energy policies and indicators in Malaysia for future sustainable energy development. Renew. Sustain. Energy Rev. 2018, 94, 551–564. [Google Scholar] [CrossRef]
  10. Tsai, B.-H.; Chang, C.-J.; Chang, C.-H. Elucidating the consumption and CO2 emissions of fossil fuels and low-carbon energy in the United States using Lotka-Volterra models. Energy 2016, 100, 416–424. [Google Scholar] [CrossRef]
  11. Mundaca, G. How much can CO2 emissions be reduced if fossil fuel subsidies are removed? Energy Econ. 2017, 64, 91–104. [Google Scholar] [CrossRef]
  12. Doraisami, A. Has Malaysia really escaped the resource curse? A closer look at the political economy of oil revenue management and expenditures. Resour. Policy 2015, 45, 98–108. [Google Scholar] [CrossRef]
  13. Park, S.-Y.; Yoo, S.-H. The dynamics of oil consumption and economic growth in Malaysia. Energy Policy 2014, 66, 218–223. [Google Scholar] [CrossRef]
  14. Lean, H.H.; Smyth, R. Disaggregated energy demand by fuel type and economic growth in Malaysia. Appl. Energy 2014, 132, 168–177. [Google Scholar] [CrossRef]
  15. Bello, M.O.; Solarin, S.A.; Yen, Y.Y. Hydropower and potential for interfuel substitution: The case of electricity sector in Malaysia. Energy 2018, 151, 966–983. [Google Scholar] [CrossRef]
  16. Rahman, M.S.; Noman, A.H.M.; Shahari, F. Does economic growth in Malaysia depend on disaggregate energy? Renew. Sustain. Energy Rev. 2017, 78, 640–647. [Google Scholar] [CrossRef]
  17. Fernando, Y.; Hor, W.L. Impacts of energy management practices on energy efficiency and carbon emissions reduction: A survey of malaysian manufacturing firms. Resour. Conserv. Recycl. 2017, 126, 62–73. [Google Scholar] [CrossRef] [Green Version]
  18. Mudin, D.K.D.; How, S.E.; Rahman, M.M.; Ibrahim, P.; Jopony, M. Industrial revolution 4.0: Universiti Malaysia Sabah perspective. In Proceedings of the 4th International Workshop on UI GreenMetric World University Rankings, IWGM 2018, Semarang, Indonesia, 8–10 April 2018; Suwartha, N., Hadiyanto, H., Sari, R.F., Eds.; EDP Sciences: Kuala Lumpur, Malaysia, 2018. [Google Scholar]
  19. Newell, D.; Twohig, R.; Duffy, M. Effect of energy management circuitry on optimum energy harvesting source configuration for small form-factor autonomous sensing applications. J. Ind. Inf. Integr. 2018, 11, 1–10. [Google Scholar] [CrossRef]
  20. Liu, W.J.; Chi, M.; Liu, Z.W.; Guan, Z.H.; Chen, J.; Xiao, J.W. Distributed optimal active power dispatch with energy storage units and power flow limits in smart grids. Int. J. Electr. Power Energy Syst. 2019, 105, 420–428. [Google Scholar] [CrossRef]
  21. Pasteris, S.; Wang, S.; Makaya, C.; Chan, K.; Herbster, M. Data distribution and scheduling for distributed analytics tasks. In Proceedings of the 2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI, San Francisco, CA, USA, 4–8 August 2017; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2018; pp. 1–6. [Google Scholar]
  22. Micheli, G.; Soda, E.; Vespucci, M.T.; Gobbi, M.; Bertani, A. Big data analytics: An aid to detection of non-technical losses in power utilities. Comput. Manag. Sci. 2019, 16, 329–343. [Google Scholar] [CrossRef]
  23. Faheem, M.; Shah, S.B.H.; Butt, R.A.; Raza, B.; Anwar, M.; Ashraf, M.W.; Ngadi, M.A.; Gungor, V.C. Smart grid communication and information technologies in the perspective of Industry 4.0: Opportunities and challenges. Comput. Sci. Rev. 2018, 30, 1–30. [Google Scholar] [CrossRef]
  24. Fong, S.; Li, J.; Song, W.; Tian, Y.; Wong, R.K.; Dey, N. Predicting unusual energy consumption events from smart home sensor network by data stream mining with misclassified recall. J. Ambient Intell. Humaniz. Comput. 2018, 9, 1197–1221. [Google Scholar] [CrossRef]
  25. Reddy, D.V.S.; Mehta, R.V.K. Study on computational intelligence approaches and big data analytics in smart transportation system. In SpringerBriefs in Applied Sciences and Technology; Springer: Berlin/Heidelberg, Germany, 2019; pp. 95–102. [Google Scholar]
  26. Gobbo, J.A.; Busso, C.M.; Gobbo, S.C.O.; Carreão, H. Making the links among environmental protection, process safety, and industry 4.0. Process Saf. Environ. Prot. 2018, 117, 372–382. [Google Scholar] [CrossRef] [Green Version]
  27. Kuo, T.-C.; Smith, S. A systematic review of technologies involving eco-innovation for enterprises moving towards sustainability. J. Clean. Prod. 2018, 192, 207–220. [Google Scholar] [CrossRef]
  28. Munodawafa, R.T.; Johl, S.K. Eco-Innovation and Industry 4.0: A Big Data Usage Conceptual Model. In Proceedings of the International Conference on Leadership and Management (ICLM 2018), Kuala Lumpur, Malaysia, 13–14 August 2018; Ghazali, Z.B., Ed.; EDP Sciences: Kuala Lumpur, Malaysia, 2018; Volume 56, p. 20. [Google Scholar]
  29. Pialot, O.; Millet, D. Towards Operable Criteria of Eco-Innovation and Eco-Ideation Tools for the Early Design Phases. Procedia CIRP 2018, 69, 692–697. [Google Scholar] [CrossRef]
  30. Machiba, T. Understanding eco-innovation for enabling a green industry transformation. In Strategies for Sustainable Technologies and Innovations; Edward Elgar Publishing: Northampton, MA, USA, 2013; pp. 21–50. [Google Scholar] [Green Version]
  31. Bossle, M.B.; Dutra de Barcellos, M.; Vieira, L.M.; Sauvée, L. The drivers for adoption of eco-innovation. J. Clean. Prod. 2016, 113, 861–872. [Google Scholar] [CrossRef]
  32. Diaz-Rainey, I.; Ashton, J.K. Investment inefficiency and the adoption of eco-innovations: The case of household energy efficiency technologies. Energy Policy 2015, 82, 105–117. [Google Scholar] [CrossRef]
  33. Horbach, J. Empirical determinants of eco-innovation in European countries using the community innovation survey. Environ. Innov. Soc. Transit. 2016, 19, 1–14. [Google Scholar] [CrossRef]
  34. Stock, T.; Obenaus, M.; Kunz, S.; Kohl, H. Industry 4.0 as enabler for a sustainable development: A qualitative assessment of its ecological and social potential. Process Saf. Environ. Prot. 2018, 118, 254–267. [Google Scholar] [CrossRef]
  35. Florescu, M.S.; Ceptureanu, E.G.; Cruceru, A.F.; Ceptureanu, S.I. Sustainable Supply Chain Management Strategy Influence on Supply Chain Management Functions in the Oil and Gas Distribution Industry. Energies 2019, 12, 1632. [Google Scholar] [CrossRef]
  36. Liu, L. Computing infrastructure for big data processing. Front. Comput. Sci. 2013, 7, 165–170. [Google Scholar] [CrossRef]
  37. Sagiroglu, S.; Sinanc, D. Big data: A review. In Proceedings of the 2013 International Conference on Collaboration Technologies and Systems, CTS 2013, San Diego, CA, USA, 20–24 May 2013; pp. 42–47. [Google Scholar]
  38. Dutta, S.; Shen, H.; Chen, J. In Situ Prediction Driven Feature Analysis in Jet Engine Simulations. In Proceedings of the 2018 IEEE Pacific Visualization Symposium (PacificVis), Kobe, Japan, 10–13 April 2018; pp. 66–75. [Google Scholar]
  39. Frank, A.G.; Dalenogare, L.S.; Ayala, N.F. Industry 4.0 technologies: Implementation patterns in manufacturing companies. Int. J. Prod. Econ. 2019, 210, 15–26. [Google Scholar] [CrossRef]
  40. Zarifi, M.H.; Deif, S.; Daneshmand, M. Wireless passive RFID sensor for pipeline integrity monitoring. Sens. Actuators A Phys. 2017, 261, 24–29. [Google Scholar] [CrossRef]
  41. Campos, J.; Sharma, P.; Gabiria, U.G.; Jantunen, E.; Baglee, D. A Big Data Analytical Architecture for the Asset Management. Procedia CIRP 2017, 64, 369–374. [Google Scholar] [CrossRef]
  42. Lin, Y.; Jun, Z.; Hongyan, M.; Zhongwei, Z.; Zhanfang, F. A Method of Extracting the Semi-Structured Data Implication Rules. Procedia Comput. Sci. 2018, 131, 706–716. [Google Scholar] [CrossRef]
  43. Boyd, D.; Crawford, K. Critical Questions for Big Data Provocations for a cultural, technological, and scholarly phenomenon. Inf. Commun. Soc. 2012, 15, 662–679. [Google Scholar] [CrossRef]
  44. Hu, H.; Wen, Y.G.; Chua, T.S.; Li, X.L. Toward Scalable Systems for Big Data Analytics: A Technology Tutorial. IEEE Access 2014, 2, 652–687. [Google Scholar]
  45. Mikalef, P.; Boura, M.; Lekakos, G.; Krogstie, J. Big data analytics and firm performance: Findings from a mixed-method approach. J. Bus. Res. 2019, 98, 261–276. [Google Scholar] [CrossRef]
  46. Qin, S.J.; Chiang, L.H. Advances and opportunities in machine learning for process data analytics. Comput. Chem. Eng. 2019, 126, 465–473. [Google Scholar] [CrossRef]
  47. Ramaswamy, S.; DeClerck, N. Customer Perception Analysis Using Deep Learning and NLP. Procedia Comput. Sci. 2018, 140, 170–178. [Google Scholar] [CrossRef]
  48. Wang, J.; Ma, Y.; Zhang, L.; Gao, R.X.; Wu, D. Deep learning for smart manufacturing: Methods and applications. J. Manuf. Syst. 2018, 48, 144–156. [Google Scholar] [CrossRef]
  49. Dahle, K. Toward governance for future generations: How do we change course? Futures 1998, 30, 277–292. [Google Scholar] [CrossRef]
  50. Olawumi, T.O.; Chan, D.W.M. A scientometric review of global research on sustainability and sustainable development. J. Clean. Prod. 2018, 183, 231–250. [Google Scholar] [CrossRef]
  51. Antonioli, D.; Mancinelli, S.; Mazzanti, M. Is environmental innovation embedded within high-performance organisational changes? The role of human resource management and complementarity in green business strategies. Res. Policy 2013, 42, 975–988. [Google Scholar] [CrossRef]
  52. Ghita, S.I.; Saseanu, A.S.; Gogonea, R.M.; Huidumac-Petrescu, C.E. Perspectives of ecological footprint in European context under the impact of information society and sustainable development. Sustainability 2018, 10, 3224. [Google Scholar] [CrossRef]
  53. Prieto-Sandoval, V.; Jaca, C.; Ormazabal, M. Towards a consensus on the circular economy. J. Clean. Prod. 2018, 179, 605–615. [Google Scholar] [CrossRef]
  54. Geissdoerfer, M.; Savaget, P.; Bocken, N.M.P.; Hultink, E.J. The Circular Economy—A new sustainability paradigm? J. Clean. Prod. 2017, 143, 757–768. [Google Scholar] [CrossRef]
  55. Freidenfelds, D.; Kalnins, S.N.; Gusca, J. What does environmentally sustainable higher education institution mean? Energy Procedia 2018, 147, 42–47. [Google Scholar] [CrossRef]
  56. Bocken, N.M.P.; Short, S.W.; Rana, P.; Evans, S. A literature and practice review to develop sustainable business model archetypes. J. Clean. Prod. 2014, 65, 42–56. [Google Scholar] [CrossRef]
  57. Yang, H.; Zhu, Y.; Li, G. The Relationship between Corporation’s Profitability and Eco-Innovation: Empirical Evidence from China. In Proceedings of the 2018 International Conference on Construction and Real Estate Management: Sustainable Construction and Prefabrication, ICCREM 2018, Charleston, SC, USA, 9–10 August 2018; Al-Hussein, M., Shen, G.Q.P., Zhu, Y., Wang, Y., Eds.; American Society of Civil Engineers (ASCE): Reston, VA, USA, 2018; pp. 14–20. [Google Scholar]
  58. Ciobanu, G.; Ghinăraru, C.; Teodor, C. Eco-innovation and the development of new new opportunities on SMEs. Qual. Access Success 2018, 19, 154–159. [Google Scholar]
  59. Zeng, R.; Grøgaard, B.; Steel, P. Complements or substitutes? A meta-analysis of the role of integration mechanisms for knowledge transfer in the MNE network. J. World Bus. 2018, 53, 415–432. [Google Scholar] [CrossRef]
  60. Huarng, K.-H.; Mas-Tur, A.; Calabuig Moreno, F. Innovation, knowledge, judgment, and decision-making as virtuous cycles. J. Bus. Res. 2018, 88, 278–281. [Google Scholar] [CrossRef]
  61. Mardani, A.; Nikoosokhan, S.; Moradi, M.; Doustar, M. The Relationship between Knowledge Management and Innovation Performance. J. High Technol. Manag. Res. 2018, 29, 12–26. [Google Scholar] [CrossRef]
  62. Bonilla, S.H.; Silva, H.R.O.; da Silva, M.T.; Gonçalves, R.F.; Sacomano, J.B. Industry 4.0 and sustainability implications: A scenario-based analysis of the impacts and challenges. Sustainability 2018, 10, 3740. [Google Scholar] [CrossRef]
  63. Chen, J.; Cheng, J.; Dai, S. Regional eco-innovation in China: An analysis of eco-innovation levels and influencing factors. J. Clean. Prod. 2017, 153, 1–14. [Google Scholar] [CrossRef]
  64. De Jesus Pacheco, D.A.; ten Caten, C.S.; Jung, C.F.; Navas, H.V.G.; Cruz-Machado, V.A.; Tonetto, L.M. State of the Art on the Role of the Theory of Inventive Problem Solving in Sustainable Product-Service Systems: Past, Present, and Future. J. Clean. Prod. 2019, 212, 489–504. [Google Scholar] [CrossRef]
  65. Opazo-Basáez, M.; Vendrell-Herrero, F.; Bustinza, O.F. Uncovering productivity gains of digital and green servitization: Implications from the automotive industry. Sustainability 2018, 10, 1524. [Google Scholar] [CrossRef]
  66. Janssen, M.; van der Voort, H.; Wahyudi, A. Factors influencing big data decision-making quality. J. Bus. Res. 2017, 70, 338–345. [Google Scholar] [CrossRef]
  67. Kim, K.; Lee, S. How can big data complement expert analysis? A value chain case study. Sustainability 2018, 10, 709. [Google Scholar] [CrossRef]
  68. Müller, J.M.; Kiel, D.; Voigt, K.-I. What Drives the Implementation of Industry 4.0? The Role of Opportunities and Challenges in the Context of Sustainability. Sustainability 2018, 10, 247. [Google Scholar] [CrossRef]
  69. Horbach, J.; Rammer, C. Energy transition in Germany and regional spill-overs: The diffusion of renewable energy in firms. Energy Policy 2018, 121, 404–414. [Google Scholar] [CrossRef]
  70. Hojnik, J.; Ruzzier, M. What drives eco-innovation? A review of an emerging literature. Environ. Innov. Soc. Transit. 2016, 19, 31–41. [Google Scholar] [CrossRef]
  71. Sanni, M. Drivers of eco-innovation in the manufacturing sector of Nigeria. Technol. Forecast. Soc. Chang. 2018, 131, 303–314. [Google Scholar] [CrossRef]
  72. Aloise, P.G.; Macke, J. Eco-innovations in developing countries: The case of Manaus Free Trade Zone (Brazil). J. Clean. Prod. 2017, 168, 30–38. [Google Scholar] [CrossRef]
  73. Tumelero, C.; Sbragia, R.; Evans, S. Cooperation in R & D and eco-innovations: The role on the companies’ socioeconomic performance. J. Clean. Prod. 2019. [Google Scholar] [CrossRef]
  74. Santos, M.Y.; Oliveira e Sá, J.; Costa, C.; Galvão, J.; Andrade, C.; Martinho, B.; Lima, F.V.; Costa, E. A big data analytics architecture for industry 4.0. In Proceedings of the 5th World Conference on Information Systems and Technologies, WorldCIST, Porto Santo Island, Madeira, Portugal, 11–13 April 2017; Adeli, H., Correia, A.M., Costanzo, S., Reis, L.P., Rocha, A., Eds.; Springer: Berlin/Heidelberg, Germany, 2017; Volume 570, pp. 175–184. [Google Scholar]
  75. Lee, J.; Kao, H.A.; Yang, S. Service innovation and smart analytics for Industry 4.0 and big data environment. In Proceedings of the 6th CIRP Conference on Industrial Product Service Systems, IPSS 2014, Windsor, ON, Canada, 1–2 May 2014; Elsevier: Windsor, ON, Canada, 2014; pp. 3–8. [Google Scholar]
  76. Mani, V.; Delgado, C.; Hazen, B.T.; Patel, P. Mitigating supply chain risk via sustainability using big data analytics: Evidence from the manufacturing supply chain. Sustainability 2017, 9, 608. [Google Scholar] [CrossRef]
  77. Babiceanu, R.F.; Seker, R. Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook. Comput. Ind. 2016, 81, 128–137. [Google Scholar] [CrossRef]
  78. Yang, C.W.; Yu, M.Z.; Hu, F.; Jiang, Y.Y.; Li, Y. Utilizing Cloud Computing to address big geospatial data challenges. Comput. Environ. Urban Syst. 2017, 61, 120–128. [Google Scholar] [CrossRef] [Green Version]
  79. Müller, J.M.; Voigt, K.-I. Sustainable Industrial Value Creation in SMEs: A Comparison between Industry 4.0 and Made in China 2025. Int. J. Precis. Eng. Manuf.-Green Technol. 2018, 5, 659–670. [Google Scholar] [CrossRef] [Green Version]
  80. Maresova, P.; Soukal, I.; Svobodova, L.; Hedvicakova, M.; Javanmardi, E.; Selamat, A.; Krejcar, O. Consequences of Industry 4.0 in Business and Economics. Economies 2018, 6, 46. [Google Scholar] [CrossRef]
  81. Wernerfelt, B. A resource-based view of the firm. Strateg. Manag. J. 1984, 5, 171–180. [Google Scholar] [CrossRef]
  82. Barney, J. Firm Resources and Sustained Competitive Advantage. J. Manag. 1991, 17, 99–120. [Google Scholar] [CrossRef]
  83. Rua, O.L.; Ortiz, R.F.; França, A.; Emeterio, M.C.S. Intangible resources, absorptive capabilities, innovation and export performance: Exploring the linkage. In Proceedings of the 3rd Conference on Innovation, Engineering and Entrepreneurship, Regional HELIX 2018, Guimaraes, Portugal, 27–29 June 2018; Veiga, G., Machado, J., Soares, F., Eds.; Springer: Berlin/Heidelberg, Germany, 2019; Volume 505, pp. 963–970. [Google Scholar]
  84. Dyer, J.H.; Singh, H. The relational view: Cooperative strategy and sources of interorganizational competitive advantage. Acad. Manag. Rev. 1998, 23, 660–679. [Google Scholar] [CrossRef]
  85. Helfat, C.E.; Peteraf, M.A. The dynamic resource-based view: Capability lifecycles. Strateg. Manag. J. 2003, 24, 997–1010. [Google Scholar] [CrossRef]
  86. Knight, G.A.; Cavusgil, S.T. Innovation, organizational capabilities, and the born-global firm. J. Int. Bus. Stud. 2004, 35, 124–141. [Google Scholar] [CrossRef] [Green Version]
  87. Hall, R. The strategic analysis of intangible resources. Strateg. Manag. J. 1992, 13, 135–144. [Google Scholar] [CrossRef]
  88. Lee, C.; Lee, K.; Pennings, J.M. Internal capabilities, external networks, and performance: A study on technology-based ventures. Strateg. Manag. J. 2001, 22, 615–640. [Google Scholar] [CrossRef]
  89. Kampfner, R.R. The need of compatibility of information processing with the control structure of the organization. In Proceedings of the 50th Annual Meeting of the International Society for the Systems Sciences 2006, ISSS 2006, Rohnert Park, CA, USA, 9–14 July 2006; pp. 743–753. [Google Scholar]
  90. Li, Y.; Yao, S.; Chia, W.M. Demand uncertainty, information processing ability, and endogenous firm: Another perspective on the impact of ICT. Nankai Bus. Rev. Int. 2011, 2, 447–474. [Google Scholar] [CrossRef]
  91. Choo, C.W. The Knowing Organization: How Organizations Use Information to Construct Meaning, Create Knowledge, and Make Decisions; Oxford University Press: Oxford, UK, 2007; pp. 1–384. [Google Scholar]
  92. Järvenpää, E.; Siltala, N.; Hylli, O.; Lanz, M. The development of an ontology for describing the capabilities of manufacturing resources. J. Intell. Manuf. 2019, 30, 959–978. [Google Scholar] [CrossRef]
  93. Vollmer, T.; Schmitt, R. Integrated shop floor data management for increasing energy and resource efficiency in manufacturing. In Proceedings of the 23rd International Conference for Production Research, ICPR 2015, Manila, Philippines, 2–6 August 2015; International Foundation for Production Research (IFPR): Nantes, France, 2015. [Google Scholar]
  94. Agarwal, P.; Ahmed, R.; Ahmad, T. Identification and ranking of key persons in a social networking website using hadoop & big data analytics. In Proceedings of the 2016 International Conference on Advances in Information Communication Technology and Computing, AICTC 2016, Bikaner, India, 12–13 August 2016; Kuri, M., Goar, V., Bishnoi, S.K., Eds.; Association for Computing Machinery: New York, NY, USA, 2016. [Google Scholar]
  95. Lin, T.Y.; Yang, C.; Zhuang, C.; Xiao, Y.; Tao, F.; Shi, G.; Geng, C. Multi-centric management and optimized allocation of manufacturing resource and capability in cloud manufacturing system. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 2017, 231, 2159–2172. [Google Scholar] [CrossRef]
  96. Olszak, C.M.; Mach-Król, M. A Conceptual Framework for Assessing an Organization’s Readiness to Adopt Big Data. Sustainability 2018, 10, 3734. [Google Scholar] [CrossRef]
  97. Garmaki, M.; Boughzala, I.; Wamba, S.F. The effect of big data analytics capability on firm performance. In Proceedings of the 20th Pacific Asia Conference on Information Systems, PACIS 2016, Chiayi, Taiwan, 27 June–1 July 2016; Pacific Asia Conference on Information Systems: Kaohsiung, Taiwan, 2016. [Google Scholar]
  98. Chatfield, A.T.; Ojo, A.; Puron-Cid, G.; Reddick, C.G. Census big data analytics use: International cross case analysis. In Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, DG.O 2018, Delft, The Netherlands, 30 May–1 June 2018; Hinnant, C.C., Zuiderwijk, A., Eds.; Association for Computing Machinery: New York, NY, USA, 2018. [Google Scholar]
  99. Gray, E.A.; Thorpe, J.H. Comparative effectiveness research and big data: Balancing potential with legal and ethical considerations. J. Comp. Eff. Res. 2015, 4, 61–74. [Google Scholar] [CrossRef]
  100. Bressanelli, G.; Adrodegari, F.; Perona, M.; Saccani, N. Exploring how usage-focused business models enable circular economy through digital technologies. Sustainability 2018, 10, 639. [Google Scholar] [CrossRef]
  101. Pugna, I.B.; Duțescu, A.; Stănilă, O.G. Corporate Attitudes towards Big Data and Its Impact on Performance Management: A Qualitative Study. Sustainability 2019, 11, 684. [Google Scholar] [CrossRef]
  102. Feng, L.; Sun, B.; Wang, K.; Tsai, S.B. An empirical study on the design of digital content products from a big data perspective. Sustainability 2018, 10, 3092. [Google Scholar] [CrossRef]
  103. De Camargo Fiorini, P.; Roman Pais Seles, B.M.; Chiappetta Jabbour, C.J.; Barberio Mariano, E.; de Sousa Jabbour, A.B.L. Management theory and big data literature: From a review to a research agenda. Int. J. Inf. Manag. 2018, 43, 112–129. [Google Scholar] [CrossRef]
  104. Erevelles, S.; Fukawa, N.; Swayne, L. Big Data consumer analytics and the transformation of marketing. J. Bus. Res. 2016, 69, 897–904. [Google Scholar] [CrossRef]
  105. Bedeley, R.T.; Nemati, H. Big Data Analytics: A key capability for competitive advantage. In Proceedings of the 20th Americas Conference on Information Systems, AMCIS 2014, Savannah, GA, USA, 7–9 August 2014; Association for Information Systems: Savannah, GA, USA, 2014. [Google Scholar]
  106. Jun, W.; Honglei, S.; Jiaping, Y. Are big data talents different from business intelligence expertise?: Evidence from text mining using job recruitment advertisements. In Proceedings of the 2017 International Conference on Service Systems and Service Management, Dalian, China, 16–18 June 2017; pp. 1–6. [Google Scholar]
  107. Hazen, B.T.; Skipper, J.B.; Ezell, J.D.; Boone, C.A. Big data and predictive analytics for supply chain sustainability: A theory-driven research agenda. Comput. Ind. Eng. 2016, 101, 592–598. [Google Scholar] [CrossRef]
  108. Kiel, D.; Müller, J.M.; Arnold, C.; Voigt, K.I. Sustainable industrial value creation: Benefits and challenges of industry 4.0. Int. J. Innov. Manag. 2017, 21, 1740015. [Google Scholar] [CrossRef]
  109. Emmanouilidis, C.; Bertoncelj, L.; Bevilacqua, M.; Tedeschi, S.; Ruiz-Carcel, C. Internet of Things—Enabled Visual Analytics for Linked Maintenance and Product Lifecycle Management. IFAC-PapersOnLine 2018, 51, 435–440. [Google Scholar] [CrossRef]
  110. Torrecilla, J.L.; Romo, J. Data learning from big data. Stat. Probab. Lett. 2018, 136, 15–19. [Google Scholar] [CrossRef] [Green Version]
  111. Yau, Y.; Lau, W.K. Big data approach as an institutional innovation to tackle Hong Kong’s illegal subdivided unit problem. Sustainability 2018, 10, 2709. [Google Scholar] [CrossRef]
  112. Grover, V.; Chiang, R.H.L.; Liang, T.P.; Zhang, D. Creating Strategic Business Value from Big Data Analytics: A Research Framework. J. Manag. Inf. Syst. 2018, 35, 388–423. [Google Scholar] [CrossRef]
  113. Debortoli, S.; Müller, O.; Vom Brocke, J. Comparing business intelligence and big data skills: A text mining study using job advertisements. Bus. Inf. Syst. Eng. 2014, 6, 289–300. [Google Scholar] [CrossRef]
  114. Mandal, S. An examination of the importance of big data analytics in supply chain agility development: A dynamic capability perspective. Manag. Res. Rev. 2018, 41, 1201–1219. [Google Scholar] [CrossRef]
  115. LaDeau, S.L.; Han, B.A.; Rosi-Marshall, E.J.; Weathers, K.C. The Next Decade of Big Data in Ecosystem Science. Ecosystems 2017, 20, 274–283. [Google Scholar] [CrossRef]
  116. Landon-Murray, M. Big data and intelligence: Applications, human capital, and education. J. Strateg. Secur. 2016, 9, 92–121. [Google Scholar] [CrossRef]
  117. Meyer, M.A. Healthcare data scientist qualifications, skills, and job focus: A content analysis of job postings. J. Am. Med. Inform. Assoc. 2019, 26, 383–391. [Google Scholar] [CrossRef]
  118. Quiñones-Gómez, J.C. Moving away from the basic, adopting a new approach to the creative process. In Lecture Notes in Mechanical Engineering; Springer Nature Switzerland AG: Basel, Switzerland, 2019; pp. 670–679. [Google Scholar]
  119. Hooi, T.K.; Abu, N.H.B.; Rahim, M.K.I.A. Relationship of big data analytics capability and product innovation performance using smartPLS 3.2.6: Hierarchical component modelling in PLS-SEM. Int. J. Supply Chain Manag. 2018, 7, 51–64. [Google Scholar]
  120. Tan, K.H. Managerial perspectives of big data analytics capability towards product innovation. Strateg. Dir. 2018, 34, 33–35. [Google Scholar] [CrossRef]
  121. Chatfield, A.T.; Reddick, C.G. Customer agility and responsiveness through big data analytics for public value creation: A case study of Houston 311 on-demand services. Gov. Inf. Q. 2018, 35, 336–347. [Google Scholar] [CrossRef]
  122. Kwon, O.; Lee, N.; Shin, B. Data quality management, data usage experience and acquisition intention of big data analytics. Int. J. Inf. Manag. 2014, 34, 387–394. [Google Scholar] [CrossRef]
  123. Herman, J.; Herman, H.; Mathews, M.J.; Vosloo, J.C. Using big data for insights into sustainable energy consumption in industrial and mining sectors. J. Clean. Prod. 2018, 197, 1352–1364. [Google Scholar] [CrossRef]
  124. Bertoni, A. Role and Challenges of Data-Driven Design in the Product Innovation Process. IFAC-PapersOnLine 2018, 51, 1107–1112. [Google Scholar] [CrossRef]
  125. Zhan, Y.; Tan, K.H.; Li, Y.; Tse, Y.K. Unlocking the power of big data in new product development. Ann. Oper. Res. 2018, 270, 577–595. [Google Scholar] [CrossRef]
  126. Wamba, S.F.; Gunasekaran, A.; Akter, S.; Ren, S.J.-F.; Dubey, R.; Childe, S.J. Big data analytics and firm performance: Effects of dynamic capabilities. J. Bus. Res. 2017, 70, 356–365. [Google Scholar] [CrossRef] [Green Version]
  127. Gupta, M.; George, J.F. Toward the development of a big data analytics capability. Inf. Manag. 2016, 53, 1049–1064. [Google Scholar] [CrossRef]
  128. You, D.; Zhang, Y.; Yuan, B. Environmental regulation and firm eco-innovation: Evidence of moderating effects of fiscal decentralization and political competition from listed Chinese industrial companies. J. Clean. Prod. 2019, 207, 1072–1083. [Google Scholar] [CrossRef]
  129. Peng, H.; Liu, Y. How government subsidies promote the growth of entrepreneurial companies in clean energy industry: An empirical study in China. J. Clean. Prod. 2018, 188, 508–520. [Google Scholar] [CrossRef]
  130. Malaysia, B. Bursa Sectorial Index Series Factsheet; Bursa Malaysia: Kuala Lumpur, Malaysia, 2018. [Google Scholar]
  131. Cooper, D.; Schindler, P. Business Research Methods, 12th ed.; McGraw-Hill Higher Education: New York, NY, USA, 2013. [Google Scholar]
  132. Ruel, E.E.; Wagner, W.E.; Gillespie, B.J. The Practice of Survey Research: Theory and Applications; SAGE Publications Inc.: Thousand Oaks, CA, USA, 2016. [Google Scholar]
  133. Khamis, H.; Kepler, M. Sample size in multiple regression: 20 + 5 k. J. Appl. Stat. Sci. 2010, 17, 505–517. [Google Scholar]
  134. Akter, S.; Wamba, S.F.; Gunasekaran, A.; Dubey, R.; Childe, S.J. How to improve firm performance using big data analytics capability and business strategy alignment? Int. J. Prod. Econ. 2016, 182, 113–131. [Google Scholar] [CrossRef] [Green Version]
  135. Hojnik, J.; Ruzzier, M. The driving forces of process eco-innovation and its impact on performance: Insights from Slovenia. J. Clean. Prod. 2016, 133, 812–825. [Google Scholar] [CrossRef]
  136. Harpe, S.E. How to analyze Likert and other rating scale data. Curr. Pharm. Teach. Learn. 2015, 7, 836–850. [Google Scholar] [CrossRef]
  137. Miller, D. The Correlates of Entrepreneurship in Three Types of Firms. Manag. Sci. 1983, 29, 770–791. [Google Scholar] [CrossRef]
  138. Astivia, O.L.O.; Zumbo, B.D. Heteroskedasticity in multiple regression analysis: What it is, how to detect it and how to solve it with applications in R and SPSS. Pract. Assess. Res. Eval. 2019, 24, 2. [Google Scholar]
  139. Green, S.B. How Many Subjects Does It Take to Do a Regression Analysis? Multivar. Behav. Res. 1991, 26, 499–510. [Google Scholar] [CrossRef]
  140. Tabachnick, B.G.; Fidell, L.S. Using Multivariate Statistics, 6th ed.; Pearson Education: Boston, MA, USA, 2013. [Google Scholar]
  141. Malkovich, J.F.; Afifi, A.A. On tests for multivariate normality. J. Am. Stat. Assoc. 1973, 68, 176–179. [Google Scholar] [CrossRef]
  142. Binti Yusoff, S.; Bee Wah, Y. Comparison of conventional measures of skewness and kurtosis for small sample size. In Proceedings of the 2012 International Conference on Statistics in Science, Business and Engineering, ICSSBE 2012, Langkawi, Malaysia, 10–12 September 2012; pp. 518–523. [Google Scholar]
  143. Goodman, R. Psychometric properties of the strengths and difficulties questionnaire. J. Am. Acad. Child Adolesc. Psychiatry 2001, 40, 1337–1345. [Google Scholar] [CrossRef] [PubMed]
  144. Tavakol, M.; Dennick, R. Making sense of Cronbach’s alpha. Int. J. Med. Educ. 2011, 2, 53–55. [Google Scholar] [CrossRef] [PubMed]
  145. Thompson, C.G.; Kim, R.S.; Aloe, A.M.; Becker, B.J. Extracting the Variance Inflation Factor and Other Multicollinearity Diagnostics from Typical Regression Results. Basic Appl. Soc. Psychol. 2017, 39, 81–90. [Google Scholar] [CrossRef]
  146. Stock, T.; Seliger, G. Opportunities of Sustainable Manufacturing in Industry 4.0. Procedia CIRP 2016, 40, 536–541. [Google Scholar] [CrossRef] [Green Version]
  147. Biggs, E.M.; Bruce, E.; Boruff, B.; Duncan, J.M.A.; Horsley, J.; Pauli, N.; McNeill, K.; Neef, A.; Van Ogtrop, F.; Curnow, J.; et al. Sustainable development and the water-energy-food nexus: A perspective on livelihoods. Environ. Sci. Policy 2015, 54, 389–397. [Google Scholar] [CrossRef]
  148. Saleh, L.D.; Wei, M.; Bai, B. Data analysis and updated screening criteria for polymer flooding based on oilfield data. SPE Reserv. Eval. Eng. 2014, 17, 15–25. [Google Scholar] [CrossRef]
  149. Wan Yen, S.; Morris, S.; Ezra, M.A.G.; Jun Huat, T. Effect of smart meter data collection frequency in an early detection of shorter-duration voltage anomalies in smart grids. Int. J. Electr. Power Energy Syst. 2019, 109, 1–8. [Google Scholar] [CrossRef]
  150. Kou, T.C.; Chiang, C.T.; Chiang, A.H. Effects of IT-based supply chains on new product development activities and the performance of computer and communication electronics manufacturers. J. Bus. Ind. Mark. 2018, 33, 869–882. [Google Scholar] [CrossRef]
  151. Zhou, K.; Yang, S.; Shao, Z. Energy Internet: The business perspective. Appl. Energy 2016, 178, 212–222. [Google Scholar] [CrossRef]
  152. Ooi, K.B.; Lee, V.H.; Tan, G.W.H.; Hew, T.S.; Hew, J.J. Cloud computing in manufacturing: The next industrial revolution in Malaysia? Expert Syst. Appl. 2018, 93, 376–394. [Google Scholar] [CrossRef]
  153. Asamoah, D.A.; Sharda, R.; Hassan Zadeh, A.; Kalgotra, P. Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course. Decis. Sci. J. Innov. Educ. 2017, 15, 161–190. [Google Scholar] [CrossRef]
  154. Demchenko, Y.; Belloum, A.; Los, W.; Wiktorski, T.; Manieri, A.; Brocks, H.; Becker, J.; Heutelbeck, D.; Hemmje, M.; Brewer, S. EDISON data science framework: A foundation for building data science profession for research and industry. In Proceedings of the 8th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2016, Luxembourg, 12–15 December 2017; IEEE Computer Society: Washington, DC, USA, 2017; pp. 620–626. [Google Scholar]
  155. Hofmann, E.; Rutschmann, E. Big data analytics and demand forecasting in supply chains: A conceptual analysis. Int. J. Logist. Manag. 2018, 29, 739–766. [Google Scholar] [CrossRef]
  156. Kamsi, N.S.; Radin Firdaus, R.B.; Abdul Razak, F.D.; Ridha Siregar, M. Realizing Industry 4.0 through STEM Education: But Why STEM Is Not Preferred? In Proceedings of the 1st South Aceh International Conference on Engineering and Technology, SAICOET 2018, Aceh Selatan, Indonesia, 8–9 December 2018; Sani, M.S.M., Ed.; Institute of Physics Publishing: Philadelphia, PA, USA, 2019. [Google Scholar]
  157. Kadar, H.H.B.; Sameon, S.S.B.; Din, M.B.M.; Rafee, P.A.B.A. Malaysia towards Cashless Society. In Proceedings of the 3rd International Symposium of Information and Internet Technology, SYMINTECH 2018, Langkawi, Malaysia, 18–20 December 2018; Othman, M.A., Abd Aziz, M.Z.A., Md Saat, M.S., Misran, M.H., Eds.; Springer: Berlin/Heidelberg, Germany, 2019; Volume 565, pp. 34–42. [Google Scholar]
  158. Ghani, E.K.; Muhammad, K. Industry 4.0: Employers’ expectations of accounting graduates and its implications on teaching and learning practices. Int. J. Educ. Pract. 2019, 7, 19–29. [Google Scholar] [CrossRef]
  159. Veerankutty, F.; Ramayah, T.; Ali, N.A. Information technology governance on audit technology performance among Malaysian public sector auditors. Soc. Sci. 2018, 7, 124. [Google Scholar] [CrossRef]
  160. Asamoah, D.A.; Doran, D.; Schiller, S. Interdisciplinarity in Data Science Pedagogy: A Foundational Design. J. Comput. Inf. Syst. 2018. [Google Scholar] [CrossRef]
  161. Belloum, A.S.Z.; Koulouzis, S.; Wiktorski, T.; Manieri, A. Bridging the demand and the offer in data science. Concurr. Comput. 2019. [Google Scholar] [CrossRef]
  162. Mittelmeier, J.; Edwards, R.L.; Davis, S.K.; Nguyen, Q.; Murphy, V.L.; Brummer, L.; Rienties, B. ‘A double-edged sword. This is powerful but it could be used destructively’: Perspectives of early career education researchers on learning analytics. Frontline Learn. Res. 2018, 6, 20–38. [Google Scholar] [CrossRef]
  163. Stieglitz, N.; Heine, K. Innovations and the role of complementarities in a strategic theory of the firm. Strateg. Manag. J. 2007, 28, 1–15. [Google Scholar] [CrossRef]
  164. Kyrgidou, L.P.; Spyropoulou, S. Drivers and Performance Outcomes of Innovativeness: An Empirical Study. Br. J. Manag. 2013, 24, 281–298. [Google Scholar] [CrossRef]
  165. Balachandran, B.M.; Prasad, S. Challenges and Benefits of Deploying Big Data Analytics in the Cloud for Business Intelligence. Procedia Comput. Sci. 2017, 112, 1112–1122. [Google Scholar] [CrossRef]
  166. Knuth, S. “Breakthroughs” for a green economy? Financialization and clean energy transition. Energy Res. Soc. Sci. 2018, 41, 220–229. [Google Scholar] [CrossRef]
  167. Salleh, K.A.; Janczewski, L. Technological, Organizational and Environmental Security and Privacy Issues of Big Data: A Literature Review. Procedia Comput. Sci. 2016, 100, 19–28. [Google Scholar] [CrossRef] [Green Version]
  168. Yebenes, J.; Zorrilla, M. Towards a Data Governance Framework for Third Generation Platforms. Procedia Comput. Sci. 2019, 151, 614–621. [Google Scholar] [CrossRef]
Figure 1. Research model of hypothesis utilized in this research [126,127].
Figure 1. Research model of hypothesis utilized in this research [126,127].
Sustainability 11 04254 g001
Table 1. Summary of Normality and Reliability Scores.
Table 1. Summary of Normality and Reliability Scores.
SkewnessKurtosisCronbach’s α
Information Technology−0.3530.0480.929
Management0.224−0.1730.919
Personnel Expertise−0.1460.6800.962
Process Eco-innovation−0.1631.1370.918
Table 2. Summary of Correlation Coefficient and VIF Score.
Table 2. Summary of Correlation Coefficient and VIF Score.
VIF ScoreProcess Eco-InnovationInformation TechnologyManagementPersonnel Expertise
Process Eco-Innovation 1
Information Technology2.6230.6051
Management2.2340.4280.6661
Personnel Expertise3.0560.5380.7710.7231
Table 3. Summary of Correlation Analysis.
Table 3. Summary of Correlation Analysis.
Process Eco-InnovationInformation TechnologyManagementPersonnel Expertise
Process Eco-Innovation1
Information Technology0.001 11
Management0.021 20.000 11
Personnel Expertise0.003 10.000 10.000 11
1. Correlation is significant at the 0.01 level (two-tailed). 2. Correlation is significant at the 0.05 level (two-tailed).
Table 4. Summary of Regression Models.
Table 4. Summary of Regression Models.
Model 1Model 2Model 3
R 20.3660.3790.379
ΔR 20.3660.0130.000
ΔF15.608 10.531 20.015 2
1. p ≤ 0.001 2. p ≤ 0.01.

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Munodawafa, R.T.; Johl, S.K. Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies. Sustainability 2019, 11, 4254. https://doi.org/10.3390/su11154254

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Munodawafa RT, Johl SK. Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies. Sustainability. 2019; 11(15):4254. https://doi.org/10.3390/su11154254

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Munodawafa, Russell Tatenda, and Satirenjit Kaur Johl. 2019. "Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies" Sustainability 11, no. 15: 4254. https://doi.org/10.3390/su11154254

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