Does Environmental Innovation Drive Digitalisation?
Shaped by Internal Knowledge Dissemination and External
Knowledge Dissimilarity
Master Thesis MSc BA Strategic Innovation Management & MSc BA Change Management
Marije T. Dalstra S2725762
marijedalstra@hotmail.nl
University of Groningen Faculty of Economics and Business
1st Supervisor: dr. J.Q Dong
2nd Supervisor: prof. dr. P.M.M. de Faria
Co-assessor: prof. dr. J. Surroca 24th of June 2019
1
ABSTRACT
In today’s changing world, firms are pressured to adapt to and implement environmental strategies. Prior literature has mainly focused on the drivers of environmental innovation, however, a gap remains whether environmental innovation can drive the need for digitalisation. Drawing upon the absorptive capacity literature, I propose that an increase in environmental innovation is associated with an increase in the need to digitalise and that this relationship is influenced by internal knowledge dissemination and external knowledge dissimilarity. By using two large data sets with 2,555 matched German companies, I find that environmental innovation indeed fosters the need for digitalisation. Internal knowledge dissemination positively moderates this relationship, where on the other hand, external knowledge dissimilarity negatively moderates the relationship. These results are in line with the theoretical assumptions made that environmental innovations are often more complex than regular innovations, and therefore better internal knowledge dissemination is beneficial and stimulated the need for digitalisation. The complexity of environmental innovations in combination with external knowledge dissimilarity will make it more difficult to integrate the knowledge in digital data management systems. These findings make an important contribution to the existing literature on the absorptive capacity of environmentally innovating firms.
2
INTRODUCTION
Nowadays, companies are faced with increasing pressure to adapt and implement environmental business practices in order to cope with the rapidly degrading environment. Companies change to become more sustainable not only because they are forced by the government and consumers but also because environmental strategies create business opportunities (Calza, Parmentola, & Tutore, 2017). The pressure on companies intensifies due to rising evidence on melting glaciers, ocean acidification, resource depletion, etc. (Melville, 2010). Companies respond to these pressures by implementing environmental innovations and green technologies (El-Kassar & Singh, 2018). While research on environmental innovations is growing rapidly (Dangelico, 2016), prior literature mainly focused on the drivers and determinants of environmental innovation (Cai & Li, 2018; Frondel, Horbach, & Rennings, 2008; Li & Found, 2017; Melville, 2010). Literature has not yet empirically explored the relationship of how environmental innovations can trigger subsequent needs for capabilities such as digital systems and big data usage, while there are many digital systems developed in order to reduce waste and pollution (Li & Found, 2017). Examples are the usage of remote conference calls instead of flying over for a meeting, light sensors that respond to movements, or digital platforms that promote the sharing economy (Melville, 2010).
Following the widespread definition, environmental innovation is a new or significantly improved product, process or system that creates environmental benefits or reduces or avoids environmental harm (Horbach, 2008; Kemp, 2000). Complementary terms such as eco-innovation (Cai & Li, 2018; Horbach, Belin, & Olta, 2013), sustainable eco-innovation, green innovation (Chen, Chang, & Wu, 2012; Cuerva, Triguero-cano, & Córcoles, 2014; Tariq, Badir, Tariq, & Saeed, 2017) and energy and resource efficiency innovations (Rennings & Rammer, 2009) are used in the literature. Although the terminologies show little variations in the description, they can be used interchangeably (Tariq et al., 2017). To avoid confusion, this paper will use the concept of environmental innovation.
3
environment (Cai & Li, 2018; Zhou, Hong, Zhu, Yang, & Zhao, 2018). Third, environmental innovations are more difficult to bring to the market because customers often find it difficult to evaluate the added environmental value compared to the likely higher costs (Rennings & Rammer, 2009). Because of these difficulties, environmental innovations are more cooperation-intensive (Calza et al., 2017). Especially cooperation within the enterprise group is frequently chosen because deep industry knowledge is needed in order to overcome the innovation barriers that are perceived as more intense for environmental innovations (Rennings & Rammer, 2009). Cooperation with other companies or institutes might at first look beneficial since it is argued that environmental innovations rely on knowledge from different sources more than regular innovations (Mothe & Nguyen-thi, 2017). Therefore, Mothe and Nguyen-thi (2017) examined the importance of external knowledge on environmental technological innovations. However, they found weak support and concluded that internal knowledge search was more relevant for environmental innovations. Other studies found a curvilinear relationship and suggested that too broad knowledge can have an adverse effect (Cuerva et al., 2014; Ghisetti, Marzucchi, & Montresor, 2015). However, none of the studies empirically examined this adverse effect. Therefore, this thesis will also focus on internal and external knowledge (dis)similarity in relation to environmental innovation and digitalisation. Internal knowledge dissemination is the distribution of knowledge which is obtained through cooperation within the enterprise group. External knowledge dissimilarity is theorised as knowledge that is the least similar to the firm and obtained through cooperation with universities. These institutions operate the farthest away from the market and have the least cognitive and geographical proximity with firms (Giannopoulou, Barlatier, & Pénin, 2019).
4
since they act as storage or infrastructure to dissimilate the knowledge more efficient throughout the company (El-Kassar & Singh, 2018; Lewin, Massini, & Peeters, 2011).
Prior literature on the absorptive capacity theory has not yet examined the relationship between environmental innovation and the need for digitalisation. Moreover, what effect internal and external knowledge has on this relationship is not been researched either. It is important to study these effects in order to find out what managers should change about their innovation strategy when following an environmental innovation strategy. It already has been shown in the literature that environmental innovations are more complex, however, a clear and concrete approach of what cooperation strategy to choose when pursuing environmental innovation with the use of digitalisation has not been addressed. The growing pressure for companies to become sustainable, together with the rise in digital technologies, requires a good understanding in order for companies to cope with the future. Therefore, this thesis attempts to answer the following question:
“How will environmental innovation drive the need for digitalisation and how will internal knowledge dissemination and external knowledge dissimilarity shape this relationship?”
5
more comprehensive knowledge management which is in greater need of digitalisation. In the final section, the limitations and possible future research opportunities are discussed.
THEORETICAL BACKGROUND Absorptive Capacity Theory
Absorptive capacity has been broadly considered as a key determinant for innovation (Cohen & Levinthal, 1990; Hensen & Dong, in press.; Lewin, Massini, & Peeters, 2011; Zahra & George, 2002). Cohen and Levinthal (1990) define absorptive capacity as the “ability of a firm to recognize the value of new, external information, assimilate it, and apply it to commercial ends” (Cohen & Levinthal, 1990, p.128). This definition neglects the role of internal knowledge which is a common limitation in prior literature on absorptive capacity (Hensen & Dong, in press.; Lewin et al., 2011). Lewin et al. (2011) emphasised the twofold purpose of absorptive capacity of generating internally new knowledge and absorbing externally generated knowledge. They conceptualised internal absorptive capacity as “managing the processes of internal variation, selection, and replication described in evolutionary economics” and external absorptive capacity as “management of exploration for new knowledge in the external environment and its assimilation” (Lewin et al., 2011, p.83). Gluch, Gustafsson, and Thuvander (2009) also stressed the importance of not only applying theories focusing on external knowledge exchange but also apply theories focussing on internal knowledge management. They applied the absorptive capacity literature to environmental innovation and found that firms can improve their capacity to absorb environmental innovations. In order to improve the absorptive capacity, firms need to focus on the acquisition, assimilation, transformation, and exploitation of environmental knowledge (Gluch et al., 2009).
6
Absorptive capacity is also highly relevant in explaining how the need for digitalisation is influenced by environmental innovation. Literature suggests that IT is one of the most important facilitators of absorptive capacity (Joshi et al., 2010; Roberts et al., 2012). Digital systems are particularly important in knowledge management since they act as storage or infrastructure to dissimilate the knowledge more efficient throughout the company (El-Kassar & Singh, 2018; Lewin et al., 2011). Environmental innovations require a deeper and more detailed industry knowledge compared to regular innovations and big data can, for example, be used to find out the exact needs of the customers. Hensen and Dong (in press.) have theorised IT-enabled internal and external absorptive capacity routines. An IT-enabled internal absorptive capacity routine is the routine use of IT to gather and share knowledge within a company. Examples of such IT systems are knowledge portals and databases that are used to collect and distribute knowledge. An IT-enabled external absorptive capacity routine is the use of IT for identifying and forming relationships with external partners. An example is the use of websites to find valuable partners (Hensen & Dong, in press.).
Cooper and Molla (2012) introduce the term Green IT in their research with the use of the absorptive capacity theory. Green IT seeks to reduce the negative ecological impact with IT. Prior research found that in order to effectively develop green IT capabilities, organisational learning is required to process new information, to improve internal structures, and to adapt the organisation to the changing environment (Roome & Wijen, 2005). Moreover, an organisation should develop a favourable Green IT attitude to better acquire and assimilate knowledge. A social integration mechanism such as a shared domain of knowledge about sustainable technology can facilitate knowledge dissemination across the organisation and help develop a shared attitude towards environmental innovations (Cooper & Molla, 2012). These findings from prior literature suggest that it is important to effectively store and disseminate knowledge about environmental innovations within the company and this can be done through the use of digital systems.
7
Dubey et al. (2017) focus on both environmental innovations and digitisation. They argue that transparency in the supply chain and integration of knowledge is key in becoming more sustainable and that this can be achieved using big data and predictive analytics. Analysing big data improves transparency and decision-making, but also improves collaboration. It has been argued that external knowledge is required for environmental innovations since they are more complex. However, why external knowledge might not be as effective for environmental innovation as is believed can also be explained by the absorptive capacity literature. Not all external knowledge can be fully absorbed. The more diverse the knowledge is, the more complicated it is to integrate the knowledge and it might even lead to a random drift in the firm’s knowledge base (Cohen & Levinthal, 1990; Dong, McCarthy, & Schoenmakers, 2017; Zahra & George, 2002).
This paper will draw upon the absorptive capacity theory because firms are faced with the increasing pressure to adopt environmental business practices, together with a fast-changing digital market, it is becoming more important to be able to acquire and absorb the right knowledge (El-Kassar & Singh, 2018). Innovation challenges will become greater with the adoption of environmental innovations, however, digital technologies such as big data and predictive analytics can overcome these challenges by providing a better means of knowledge acquisition and dissemination (Ar, 2012; Gunasekaran et al., 2017; Rajesh, 2017). The following chapter will draw upon existing literature in order to develop the hypotheses.
HYPOTHESES DEVELOPMENT Environmental Innovation and the Need for Digitalisation
8
Environmental innovations can either be developed with the intention to reduce environmental harm or they are developed with the unintentional side effects of benefiting the environment (Kemp, 2000). To be as inclusive as possible, the survey that is used states the following extensive definition for environmental innovations: “An environmental innovation is a new or significantly improved product (good or service), process, organisational method or marketing method that creates environmental benefits compared to alternatives. The environmental benefits can be the primary objective of the innovation or the result of other innovation objectives. The environmental benefits of an innovation can occur during the production of a good or service, or during the after sales use of a good or service by the end user” (Federal Ministry of Education and Research, 2015, p.7).
In order to define digitalisation, this paper draws upon the digital innovation literature that states that digital technologies lead to disruption, offers flexibility and has a repurposed use (Dong, in press.). Digitalisation includes the following technologies: ICT, Internet of Things, augmented reality, social media, big data and predictive analytics (Li & Found, 2017). Big data also includes the clickstream data from the web and social media and is often stored in the cloud (El-Kassar & Singh, 2018). In this paper, digitalisation includes the following: digital interconnection between production, service provision and logistics, with customers and suppliers, teleworking, software-based communication, intranet-based platforms, E-Commerce, social media, cloud computing, cloud applications, and big data analysis (Federal Ministry of Education and Research, 2016).
It is believed that there is a relationship between digitalisation and environmental innovation (Horbach, 2008; Li & Found, 2017; Melville, 2010; Tariq et al., 2017). However, the direction of this relationship remains unclear. Is it digitalisation that fosters environmental innovation, or does environmental innovation lead to an increase in the need for digitalisation?
9
was gathered at one point in time. Therefore, some caution is needed with making implications about a causal relationship. Literature has not yet explored the reverse direction, thus how environmental innovations can trigger subsequent needs for digitalisation. Although there are many digital systems developed in order to reduce waste and pollution (Li & Found, 2017). Kuzmina, Prendeville, Walker, and Charnley (2018) provide a long and detailed list of ways in how digital systems benefit the implementation of circular concepts and environmental innovations. For example, tracking systems such as radio frequency identification (RFID) tags will optimise logistics and better manage the value chain. Big data can be used to get insights into customer behaviour and demand, which can then be better predicted and over-supply will be reduced. Moreover, leasing and subscription business models have been introduced that enable consumers to use products without owning them (Kuzmina et al., 2018). Thus, practice shows that more digital systems are implemented when firms pursue an environmental strategy. Since literature has not yet addressed this phenomenon, this thesis will develop a better understanding of this relationship. I propose that environmental innovation will foster the need for digitalisation for the following reasons.
First, a higher need for digitalisation when pursuing environmental innovation is expected since the complexity of environmental innovations requires better communication. More stakeholders are involved with environmental innovations which contribute to the complexity (Cai & Li, 2018; Calza et al., 2017), and in order to manage the communication flows, digital means such as a shared platform or apps can be used to store and distribute ideas.
10
Third, since environmental innovations are more difficult to market, they require a more deep and detailed understanding of the market and its customers (Rennings & Rammer, 2009). Not all customers are willing to spend more on products that are better for the environment. To find the specific customers that are, data analytics is needed. In order to reach those specific customers, digital marketing strategies are crucial, for example, customer-specific advertisements via apps or websites. Dubey et al. (2017) tested that big data and predictive analytics are positively related to perceived environmental performance. These digital systems are needed to find, analyse and store the customer data about environmental innovation.
Fourth, environmental innovation implies the reduction of waste, water, CO2 etc., and in order to measure and control this, digital technologies need to be used. Green et al. (1994) found that the higher the drive to green the environment, the more firms were stimulated to re-examine their technologies which enhanced their R&D. This effect can be explained by Song, Fisher, and Kwoh (2018) who state the following: “Green innovation can promote the development and introduction of green technologies, and these green technologies can monitor, trace, control, and prevent pollution at the source while ensuring that the entire process of production, application, and consumption of end products has minimal environmental impact (p. 2).” Tariq et al. (2017) also suggested that in order to improve the production process efficiency of environmental innovations and thereby reducing emissions, greater technological and digital capabilities are needed.
Following the absorptive capacity literature, the higher need for digitalisation when pursuing environmental innovations can be explained since digital systems such as big data or predictive analytics can be used to acquire, assimilate, transform, and exploit environmental innovation knowledge (Gluch et al., 2009). The absorptive capacity will become greater with the use of IT and digitalisation (Joshi et al., 2010). Because of the above-mentioned arguments, environmental innovation will foster the need for digitalisation and therefore, the following hypothesis is proposed:
Hypothesis 1: An increase in environmental innovation is associated with an increase
11
The Moderating Roles of Internal and External Knowledge (Dis)similarity
This section will elaborate on the moderating roles of internal knowledge dissemination and external knowledge dissimilarity on the relationship between environmental innovation and the need for digitalisation. First, the differences between non-environmental innovations and non-environmental innovations will be touched upon which are then explained with the use of absorptive capacity theory. Second, the effect of internal knowledge dissemination on the main hypothesis will be clarified and third, the effect of external knowledge dissimilarity on the main hypothesis will be elaborated upon.
Following the article by Rennings and Rammer (2009), their empirical research on German companies shows that firms pursuing environmental innovations are perceiving more intense innovation barriers such as regulation, uncertain demand and insufficient cooperation partners. Therefore, these environmental innovating firms are more often introducing knowledge management systems to cope with the difficulties. Rennings and Rammer (2009) also found that firms pursuing environmental innovation have a twice as much higher share in sales on R&D compared to regular innovative firms. These firms also conduct significantly more R&D in-house and they rely more strongly on internal sources compared to external sources. The environmental innovation firms also cooperate more within their own enterprise group and with suppliers compared to non-environmental innovation firms. However, the share of environmental innovation firms that cooperate with universities, competitors or public research institutes is not higher compared to firms that pursue non-environmental innovations. Rennings and Rammer (2009) explain their findings by arguing that environmental innovations require more complex innovation activities and require knowledge from outside the organisation. However, they do not explain why environmental innovation firms cooperate more within their own enterprise group compared to non-environmental innovation firms. Yet, other studies have not found a significant influence of external knowledge acquisition on environmental innovation (Cuerva et al., 2014; Horbach et al., 2013). Thus, it is argued that environmental innovations are more complex and therefore are expected to rely more on external knowledge sources (Mothe & Nguyen-thi, 2017; Rennings & Rammer, 2009), however, no clear-cut results have been found. This inconclusiveness creates an interesting ground for research.
12
all external knowledge can be fully absorbed. The more diverse the knowledge is, the more complicated it is to integrate the knowledge and it might even lead to a random drift in the firm’s knowledge base (Cohen & Levinthal, 1990; Dong et al., 2017; Zahra & George, 2002). In the absorptive capacity literature, there is growing attention given to information technologies (Dong & Yang, 2015). Information technologies and digital systems are relevant for absorptive capacity since they can function as an organisational memory in the form of electronic repositories (Alavi & Leidner, 2001). Digitalisation also enhanced the distribution of knowledge and knowledge sharing throughout the company. It is found that knowledge dissemination in companies favours computer-mediated communication over co-location of employees (Song, Berends, Van der Bij, & Weggeman, 2007). Rennings and Rammer (2009) found that knowledge management systems are more often introduced in environmental innovation firms in order to overcome innovation barriers.
Dubey et al. (2017) addressed the questions of how and when large scale data can enhance environmental sustainability in supply chains since big data has been shown to have an environmental impact. They improve transparency and decision-making but also improve collaboration. Collaboration among partners within supply chains is used to meet environmental goals (Dubey et al., 2017). However, the acquired knowledge from collaboration is of limited use when it is not integrated within the company. Integrative capabilities that link to internal structures, processes and systems are a necessity (Verona, 1999). In order to adapt to environmental change and pressures, firms should be able to overcome internal barriers that hinder the integration and assimilation of new knowledge (Eggers & Park, 2018). Firms need to possess skills that combine new technologies and knowledge to encourage cross-functional integration and knowledge flows between employees, departments or subsidiaries (Dangelico, 2016).
13
is obtained through cooperation with universities since universities have the least similar knowledge in relation to the company (Giannopoulou et al., 2019).
Internal Knowledge Dissemination
Rennings and Rammer (2009) found that firms pursuing environmental innovation conduct significantly more R&D in-house and they rely more strongly on internal knowledge sources. Moreover, these firms more often introduce knowledge management systems and in order to overcome the challenges of cooperation within enterprise groups, a smooth and effective internal knowledge dissimilation is important. Thus, I propose that internal knowledge dissemination will strengthen the positive relationship between environmental innovation and the need for digitalisation for the following reasons.
First, environmental innovations are more complex than regular innovations because they are more difficult to bring to the market (Rennings & Rammer, 2009). Therefore, the market and its customers should be known in detail and this market knowledge needs to be disseminated through the company to create better environmental innovations. This knowledge dissemination is improved with better digital platforms or shared systems where knowledge can be effectively stored and managed (Song et al., 2007). Thus, higher knowledge dissemination will stimulate digitalisation use, which is needed for environmental innovations. Second, since environmental innovations are more complicated and involve more stakeholders, their knowledge should be assimilated through the company and effective digital communication systems are therefore needed. Cai and Li (2018) argue that the environmental information acquired from the stakeholders should be coordinated and integrated into environmental innovations. The coordination and integration of knowledge and the management of communication flows can be done through digital means such as a shared platform or other computer-mediated communication means (Song et al., 2007). Thus, better knowledge dissemination requires proper digital systems to communicate through, and this is needed to handle the complexity of environmental innovations.
14
Such a company culture can be created with a shared platform where information about sustainability is shared. For example by keeping employees up to date about how less water has been used this month or the decrease in CO2 in the production process. Such a culture will also use less paper and will need to improve their digital storage means (Li & Found, 2017). For example, printing files and store them in file boxes can be replaced by storing documents in the cloud. ERP systems such as Oracle also improve the cross-functional collaboration within the enterprise and can change awareness throughout the company (Oracle, 2019). The positive one-company culture towards environmental innovations improves the knowledge dissemination within the company, which leads to a higher need for digital knowledge management systems.
In summary, higher internal knowledge dissemination will strengthen the relationship between environmental innovation and the need for digitalisation because more communication and sharing of knowledge is needed to cope with the complexity of environmental innovations and knowledge dissemination favours digital systems. Therefore, the following hypothesis is proposed:
Hypothesis 2: Internal knowledge dissemination will positively moderate the
relationship between environmental innovation and the need for digitalisation.
External Knowledge Dissimilarity
Studies found that in the case of environmental innovations, in-house R&D is more often chosen compared to cooperation with companies or institutes outside the market (Rennings & Rammer, 2009). The acquisition of knowledge that is too dissimilar from the focal firm might not benefit environmental innovations (Ghisetti et al., 2015). Therefore, I propose that the relationship between environmental innovation and the need for digitalisation is weakened by cooperating with external partners that have very dissimilar knowledge for the following reasons.
15
the industry (Eisenhardt & Martin, 2000). External knowledge that is dissimilar from the focal firm is less detailed about the market but is more broad and general. This knowledge might be more tacit and difficult to store in ERP systems. Therefore, less digital systems are needed to manage the knowledge, because environmental innovations require more detailed market knowledge instead of broad and general knowledge.
Second, it is found to be problematic when companies or institutions with very dissimilar knowledge cooperate to create interorganisational digital technologies (Gama, Sjödin, & Frishammar, 2017). Both partners might lack sufficient understanding of each other’s knowledge, and terminologies used or decision-making controls can be incompatible. Gama et al. (2017) found that especially cooperating with universities to create digital technologies was complicated since they lacked insights into customers’ needs and misunderstanding was a key issue in the cooperation. Moreover, cooperation partners might also use different digital systems that are not compatible with each other. For example, when the company uses the SAP software as an enterprise resource planning (ERP) system and another company uses their in-house developed tailor-made system, both software are not compatible and information then must be exchanged by more old fashioned already existing digital means such as email. Thus, when companies want to cooperate with partners to create environmental innovations through digitalisation, but these partners do not use the same digital systems or lack understanding, there is less need for digitalisation because less different digital means are used.
Third, more external knowledge dissimilarity results in less need for digitalisation when pursuing environmental innovations because not all external knowledge can be fully absorbed according to the absorptive capacity theory. When firms obtain knowledge that is too far from the industry, it might even drift the company away from the actual market needs (Cohen & Levinthal, 1990; Dong et al., 2017; Zahra & George, 2002). State-of-the-art digital technologies that are required for environmental innovations need to be found within the industry instead of externally (Canhoto, Quinton, Jackson, & Dibb, 2016). Therefore, when obtaining too dissimilar external knowledge, this results in less digitalisation since the appropriate digital systems for environmental innovations need to be found within the industry.
16
to the market and digital technologies change rapidly, I expect that obtaining external knowledge that is highly dissimilar for the firm will weaken the relationship between environmental innovation and the need for digitalisation. Hence, I hypothesise the following:
Hypothesis 3: External knowledge dissimilarity will negatively moderate the
relationship between environmental innovation and the need for digitalisation.
Conceptual model
METHODOLOGY
The hypotheses are tested using data from German companies in 21 different industries. This empirical setting has been chosen because of the following reasons. First, incorporating multiple industries will allow for generalisation of the results and thus, the results will not be limited to a single industry. The survey allows projections for the German firm population as well as for individual industries and size classes (Gottschalk, 2017). Second, Germany is chosen because this country is currently ranked 13th out of 180 on the
Environmental Performance Index (EPI, 2018) and are ranked 14th on the Digital Economy and
17
Data Collection
The data used comes from two large scale surveys that are both commissioned by the German Federal Ministry of Education and Research (BMBF). Both the Mannheim Innovation Panel (MIP) 2015 and the MIP 2016 are conducted jointly by the Centre for European Economic Research (ZEW), the Fraunhofer-Institute System and Innovation Research (ISI), and the Institute for Applied Social Sciences (infas). The MIP is the German contribution to the Community Innovation Surveys (CIS) from the European Commission. The MIP 2015 survey was answered by 5,445 companies and the MIP 2016 was answered by 4,685. Both surveys were then merged and matched based on the company id and the sector they operate in, and this left us with 2,555 matched companies that filled in both surveys.
The two specific surveys are chosen since they include either information on the usage of digitalisation (MIP 2016) or on the introduction of innovations that had environmental benefits (MIP 2015). By using data from different points in time, the potential common method biases will be reduced. To avoid reverse causality, the time lag between both surveys allows making a comparison between the need for digitalisation and the environmental innovations because the dependent digitalisation variable is measured later in time than the independent environmental innovation variable.
Measurement
Independent variable
18
In the MIP 2015 survey, environmental innovation is defined as “a new or significantly improved product (good or service), process, organisational method or marketing method that creates environmental benefits compared to alternatives.” The survey questioned whether the companies introduced innovations from 2012 to 2014 with the use of nine sub-questions on what type of environmental benefit was addressed. The benefits addressed are whether the innovation led to a reduction of the following: energy use (a), material or water use (b), CO2 use (c), pollution of air (d), water (e) or noise (f), or whether the innovation led to an increased use of renewable energy sources (g), less hazardous substitutes (h), and recycled materials (i). Each sub-question could be answered with ‘no’, ‘yes insignificant’, and ‘yes significant’ where ‘no’ is indicated with a 0, ‘yes insignificant’ with a 1 and ‘yes significant’ with 2. This makes the data ordinal.
In order to create one variable out of the nine sub-questions, a factor analysis is used (Appendix A). Before running the factor analysis, Cronbach’s Alpha is calculated to control for reliability. Since the Cronbach’s Alpha is 0.866 and removing one variable results in a lower Alpha, all nine variables are included in the factor analysis. The Alpha is above the threshold of 0.7, which indicates that the values are reliable (Nunnally, 1978). All variables clearly load on one factor after running the factor analysis. One variable (the use of renewable energy sources) has a slightly lower factor loading (0.476) compared to the other variables which were all above the 0.5, however, according to Saunila, Ukko, & Rantala (2018) factor loadings of 0.4 are accepted. Moreover, when removing this variable, the Cronbach’s Alpha will decrease from 0.866 to 0.854, therefore the variable is included.
Dependent variable
Digitalisation is computed in a similar way as the independent variable. In MIP 2016, eleven questions are asked regarding the current usage of digitalisation. The questions asked about the digital interconnection, digital communication, e-commerce and information processing systems such as big data. Each question could be answered with ‘no’, ‘low’, ‘medium’, and ‘high’ where ‘no’ is indicated with 0, ‘low’ with 1, ‘medium’ with 2, and ‘high’ with 3 which makes the data ordinal.
19
variables will result in a lower alpha. Therefore, all eleven variables are included in the factor analysis.
Moderating variables
The two moderating variables are internal knowledge dissemination and external knowledge dissimilarity. Both variables are calculated with data from the MIP 2015 questionnaire that researched the different types of cooperation for innovation activities. From the survey, I use the question “cooperation with other enterprises within your enterprise group” to measure the internal knowledge dissemination, since the knowledge within the enterprise is most similar to the company. The question “cooperation with universities or other higher education institutions” is used to measure external knowledge dissimilarity, since this type of knowledge exchange is least similar to the company. Both variables internal knowledge dissemination and external knowledge dissimilarity were given a value of 1 if there was a respective type of cooperation and a value of 0 if there was not.
Control variables
To control for possible effects from other variables, I include the following control variables: R&D intensity, firm size, education level, marketing intensity and industries.
First, R&D intensity is controlled for since R&D will improve technological capabilities (Cuerva et al., 2014; Horbach, 2008). R&D intensity is measured as the R&D expenditures divided by the total sales.
Second, I control for the firm size which is measured as the natural logarithm of the number of employees to reduce the impact of outliers and normalise the data. The number of employees can influence the firm’s resource availability which in turn affects innovation activities (Laursen & Salter, 2006).
20
Finally, I include 20 industry dummies to control for the differences across industries (Laursen & Salter, 2006). Table 1 includes all 21 industries and shows how the firms are distributed across the industries.
Table 1: Classification of 21 economic sectors
Economic sectors Freq. Percent Cum.
1 Mining 112 4.38 4.38 2 Food/Tobacco 120 4.70 9.08 3 Textiles 89 3.48 12.56 4 Wood/Paper 72 2.82 15.38 5 Chemical 69 2.70 18.08 6 Plastics 73 2.86 20.94 7 Glass/Ceramics 56 2.19 23.13 8 Metals 177 6.93 30.06 9 Electrical equipment 176 6.89 36.95 10 Machinery 106 4.15 41.10 11 Retail/Automobile 51 2.00 43.09 12 Furniture/Toys/Medical technology 135 5.28 48.38 13 Energy/Water 198 7.75 56.13 14 Wholesale 111 4.34 60.47
15 Transport equipment/Postal Service 214 8.38 68.85
16 Media services 126 4.93 73.78
17 IT/Telecommunications 98 3.84 77.61
18 Banking/Insurance 101 3.95 81.57
19 Technical services/R&D services 180 7.05 88.61
20 Consulting/Advertisement 164 6.42 95.03
21 Firm-related services 127 4.97 100.00
Total 2,555 100.00 100.00
Analysis Strategy
21
regression was done. The first model will include the control variables, the second model will add the environmental innovation variable, the third model will add the internal knowledge dissemination variable, the fourth adds external knowledge dissimilarity which results in the full model. All tests are performed in STATA.
RESULTS
This section explains the output of the analysis described in the methodology. It includes a description of the statistics, correlations, and the main regression results.
Descriptive Statistics and Correlations
First, the mean, standard deviation and correlations between the variables are calculated and shown in table 2. In total, more than 2,100 observations are included and the correlation results show that there are no unexpected high correlations. R&D intensity shows a moderate correlation of 0.47 with external knowledge dissimilarity which is unexpectedly higher than the correlation with internal knowledge dissemination (0.20). This finding is unexpected since literature found that firms with higher R&D investment have stronger internal innovation benefits (Hensen & Dong, in press).
In order to fully exclude the presence of multicollinearity, a Variation Inflation Factor (VIF) is done. The VIF results show an average VIF value of 1.86 with the highest values of 2.81 for technical and R&D services industry. All values are well below the threshold of 10 and therefore, no multicollinearity is found in the data (O’Brien, 2007).
Table 2: Descriptive statistics and correlations
Obs. Mean SD (1) (2) (3) (4) (5) (6) (7)
(1) Digitalisation 2199 –0.04 0.94
(2) Environmental innovation 2148 –0.02 0.91 0.14
(3) Internal knowledge dissemination 2555 0.03 0.17 0.10 0.12
(4) External knowledge dissimilarity 2555 0.09 0.28 0.14 0.11 0.34
(5) R&D intensity 2413 0.01 0.03 0.20 0.07 0.20 0.47
(6) Firm size 2539 3.25 1.56 0.24 0.21 0.15 0.15 0.04
(7) Education level 2335 3.33 2.65 0.25 –0.11 0.10 0.21 0.30 –0.04
(8) Marketing 2109 0.01 0.02 0.17 0.02 0.02 0.03 0.17 –0.01 0.14
22
Regression Results
Table 3 shows the OLS regressions results for hypotheses testing. The results show an R-square ranging from 0.237 for the control model to an R-square of 0.261 for the full model. Model 1 only includes the control variables and model 2 adds the independent variable environmental innovation. The results show that environmental innovation has a highly significant positive effect on digitalisation (b = 0.172, p < 0.01), therefore supporting hypothesis 1. Thus, an increase in environmental innovation is associated with an increase in the need for digitalisation. In model 3, the interaction effect of internal knowledge dissemination and environmental innovation is tested. The results show a b = 0.192 slope with a weak significance of p < 0.1 indicating that internal knowledge dissemination positively moderates the relationship between environmental innovation and the need for digitalisation. Thus, hypothesis 2 is weakly supported. The 4th model adds the interaction effect of external
knowledge dissimilarity and environmental innovation. The results show that the moderation effect (H3) is supported (b = –0.161, p < 0.05) indicating that external knowledge dissimilarity negatively moderates the relationship between environmental innovation and the need for digitalisation. What is surprising is that external knowledge dissimilarity without the interaction effect is weakly but positively related to the need for digitalisation (b = 0.179, p < 0.1). This can suggest that external knowledge dissimilarity might lead to a higher need for digitalisation, however, in combination with environmental innovations, it does not.
23
Table 3: OLS Regression Results for the Need for Digitalisation
Notes: * p < 0.1; : ** p < 0.05; : *** p < 0.01. Standard errors are in parentheses. Dependent variable is the need for digitalisation.
(1) (2) (3) (4) R&D intensity 2.653*** 1.903** 1.935** 1.350 (0.725) (0.767) (0.776) (0.833) Firm size 0.167*** 0.152*** 0.151*** 0.149*** (0.0144) (0.0152) (0.0153) (0.0153) Education level 0.0455*** 0.0474*** 0.0459*** 0.0443*** (0.00988) (0.0102) (0.0102) (0.0102) Marketing 4.961*** 4.958*** 4.998*** 5.114*** -1.165 -1.259 -1.259 -1.259 Industry 1: Mining 0.0612 0.0498 0.0542 0.0560 (0.136) (0.144) (0.144) (0.143) Industry 2: Food/Tobacco -0.0787 -0.187 -0.192 -0.194 (0.137) (0.140) (0.140) (0.140) Industry 3: Textiles 0.255* 0.157 0.155 0.163 (0.147) (0.154) (0.154) (0.154) Industry 4: Wood/Paper 0.00259 -0.139 -0.132 -0.136 (0.156) (0.158) (0.158) (0.158) Industry 5: Chemical -0.0487 -0.169 -0.164 -0.169 (0.162) (0.170) (0.170) (0.170) Industry 6: Plastics 0.197 0.125 0.121 0.123 (0.155) (0.158) (0.158) (0.158) Industry 7: Glass/Ceramics 0.0121 -0.167 -0.157 -0.155 (0.171) (0.177) (0.177) (0.177) Industry 8: Metals 0.115 0.0286 0.0303 0.0296 (0.124) (0.128) (0.128) (0.128)
Industry 9: Electrical Equipment 0.262** 0.243* 0.242* 0.237*
(0.127) (0.131) (0.131) (0.131)
Industry 10: Machinery 0.355** 0.271* 0.257* 0.253*
(0.142) (0.146) (0.147) (0.147)
Industry 11: Retail/Automobile 0.157 0.121 0.112 0.105
(0.183) (0.191) (0.191) (0.191)
Industry 12: Furniture/Toys/Medical tech. -0.0130 -0.0333 -0.0305 -0.0260
(0.134) (0.139) (0.139) (0.139)
Industry 13: Energy/Water -0.0610 -0.151 -0.148 -0.146
(0.121) (0.128) (0.128) (0.128)
Industry 14: Wholesale 0.367*** 0.345** 0.347** 0.352**
(0.137) (0.141) (0.141) (0.141)
Industry 15: Transport equip./Postal service 0.193 0.167 0.170 0.171
(0.122) (0.128) (0.127) (0.127)
Industry 16: Media services 0.718*** 0.728*** 0.726*** 0.732***
(0.138) (0.142) (0.142) (0.142)
Industry 17: IT/Telecommunications 1.082*** 1.071*** 1.078*** 1.092***
(0.150) (0.157) (0.157) (0.157)
Industry 18: Banking/Insurance 0.840*** 0.849*** 0.843*** 0.851***
(0.151) (0.153) (0.153) (0.153)
Industry 19: Technical services/R&D services 0.234* 0.254* 0.261* 0.259*
(0.132) (0.135) (0.135) (0.135)
Industry 20: Consulting/Advertisement 0.593*** 0.578*** 0.578*** 0.586***
(0.131) (0.135) (0.135) (0.135)
Environmental innovation 0.172*** 0.161*** 0.174***
(0.0257) (0.0264) (0.0275)
Internal knowledge dissemination -0.00342 -0.0794
(0.147) (0.152)
Internal knowledge dissemination X Environmental innovation 0.192* 0.264**
(0.112) (0.117)
External knowledge dissimilarity 0.179*
(0.0937)
External knowledge dissimilarity X Environmental innovation -0.161**
24
DISCUSSION AND CONCLUSION Main Findings
This thesis examined how environmental innovation is associated with the need for digitalisation and how internal knowledge dissemination and external knowledge dissimilarity are influencing this relationship. By building on the absorptive capacity literature, I expected that environmental innovation would have a positive effect on digitalisation and that this relationship would be positively moderated by internal knowledge dissemination and negatively moderated by external knowledge dissimilarity. A regression analysis based two datasets with 2,555 matched German companies was conducted to answer the research question “How will environmental innovation drive the need for digitalisation and how will internal knowledge dissemination and external knowledge dissimilarity shape this relationship?” First, I found that environmental innovation indeed fosters the need for digitalisation which is consistent with prior literature that suggested there is a relationship between environmental innovation and digitalisation (Dubey et al., 2017; Li & Found, 2017).
The second main finding is that internal knowledge dissemination positively moderates the relationship between environmental innovation and digitalisation which is consistent with the theoretical arguments made in this thesis. Higher internal knowledge dissemination will strengthen the relationship between environmental innovation and the need for digitalisation because more communication and sharing of knowledge is needed to cope with the complexity of environmental innovations and this is done through digital systems. This is in line with past research that mentioned the important role of knowledge dissemination on environmental innovations (Cai & Li, 2018; Rennings & Rammer, 2009) and on digitalisation (Song et al., 2007).
25
These results offer important implications for both theoretical and managerial purposes. First, theoretical implications will be discussed, followed by managerial implications. Lastly, the limitations of this study and the potential future research will be mentioned.
Theoretical Implications
Prior literature has not examined whether an increase in environmental innovation is associated with an increase in the need for digitalisation. This study contributes to this gap by empirically examining this relationship and adds moderators to understand how internal knowledge dissemination and external knowledge dissimilarity influence the relationship. The findings of this study show several relevant implications for the existing literature.
First, this thesis departs from prior literature because this direction of the main relationship has not been addressed before. Literature has examined digitalisation as a driver of environmental innovation (Cai & Li, 2018; Frondel, Horbach, & Rennings, 2008; Li & Found, 2017; Melville, 2010), however, environmental innovation as a driver for digitalisation has not been studied until now. Although some caution is needed when interpreting a causal relationship, the empirical results show that environmental innovation is associated with the need for digitalisation. This is consistent with the examples from practice that many digital systems are introduced when firms pursue environmental innovations (Kuzmina et al., 2018; Li & Found, 2017). These findings suggest that environmental innovation and the need for digitalisation are strongly related. Future research should, therefore, study these concepts together to develop a rich and deeper understanding of the relationship.
26
internal knowledge dissemination might be even more important than external knowledge in relation to environmental innovation and digitalisation. Future research should extend this notion by deepening the understanding of how internal knowledge dissemination influences the development of environmental innovations through digitalisation.
Third, external knowledge dissimilarity was found to have a weakening effect on the relationship between environmental innovation and digitalisation. This adds empirically to existing literature that suggested an inverted U-shape of knowledge heterogeneity on environmental innovation (Ghisetti et al., 2015; Laursen & Salter, 2006; Mothe & Nguyen-thi, 2017) and a weakening effect on digitalisation (Canhoto et al., 2016; Gama et al., 2017). The finding is in line with the previously made assumptions. I assumed a weakening effect since environmental innovations are more complex, more stakeholders are involved, and more detailed market knowledge is needed. The more dissimilar the knowledge acquired, the more difficult to integrate this knowledge in digital data management systems. This is an important departure from the literature that mainly states external knowledge is beneficial for innovations (Mothe & Nguyen-thi, 2017; Rennings & Rammer, 2009). It appears that this does not hold when pursuing environmental innovations with digitalisation. These findings provide interesting grounds for future research.
Finally, the surprising positive effect of external knowledge dissimilarity (without the interaction with environmental innovation) on digitalisation hints to some important implications. It suggests that external knowledge dissimilarity might lead to a higher need for digitalisation, however, in combination with environmental innovations, it does not. Environmental innovation might therefore even be more complex and different from regular innovations than expected. Research should explore this implication in the future by thoroughly examining the difference between environmental innovations and regular innovations in relation to external knowledge dissimilarity. Moreover, the underlying reasons of why external knowledge dissimilarity is less beneficial can be examined, and how firms can improve this to enhance their environmental innovations.
Managerial Implications
27
strategies. The first important take away is that simply introducing environmental innovations are not sufficient and digital systems need to be introduced as well since they are strongly linked together. Therefore, managers should take into account to not only invest in environmental innovations but also re-evaluate their digital systems and perhaps introduce new knowledge management systems.
The second implication results from the finding that internal knowledge dissemination is important for environmental innovations and it will increase the need for digitalisation. When seeking to pursue environmental innovation through digitalisation, managers should enhance the internal knowledge dissemination with introducing digital communication systems. This will improve the environmental innovation process and the more complex innovation barriers can be more easily be overcome. Moreover, it is important to have a one-company culture where the environmental innovation strategy is represented. A shared platform where information can be exchanges benefits this culture and will enhance the environmental innovations. Firms should stimulate cross-functional knowledge sharing and the use of such platforms since this will not only benefit the environmental innovations but also strengthen the company culture.
Third, the results showed that it is less beneficial to cooperate with partners that have dissimilar knowledge since this will complicate the environmental innovation process and leads to a lower use of digitalisation. Although literature found it is not bad to cooperate and seek knowledge externally, this study shows that too dissimilar external knowledge will not be beneficial. Firms need to take into account that, especially with environmental innovations, it is critical to select the right cooperation partners. Managers should be careful with applying knowledge that is acquired far from the industry since it might lead them in the wrong direction or make the wrong implications.
28
Limitations and Future Research
This study has several limitations which can provide interesting ground for future research. First, this study uses the CIS data which includes some biases. The CIS data does not allow to specify what part of the introduced innovations are environmentally friendly in the case of companies that introduced multiple innovations during a three-year period (Mothe & Nguyen-thi, 2017). Moreover, the CIS data is self-reported which might be biased as well. For example, the current usage of digitalisation could be indicated with high, medium or low. High use for one company can be medium use for another. In the case of environmental innovations, there is no distinction made between intentional and unintentional environmental innovations. Future research might consider measuring environmental innovation and the usage of digitalisation based on a combination of surveys and patents. Patents have been used in previous research, however, patens alone might not be sufficient (Wagner, 2007).
Second, the industry inclusiveness of this study allows for generalisability, however, the distinction between industries is therefore difficult. The findings might vary depending on the specific industry. There is an opportunity for future research to distinguish among different industries or sectors in order to find results specific to a certain industry. Moreover, the findings might differ when studied in other countries. Therefore, future research should consider collecting data from less innovative or developed countries, to test whether the results would be the same.
29
the other stronger and whether there are contingencies other than internal and external knowledge that interfere.
Conclusion
30
References
Alavi, M., & Leidner, D. E. (2001). Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues. MIS Quarterly, 25(1), 107–136. Ar, I. M. (2012). The Impact of Green Product Innovation on Firm Performance and Competitive
Capability: The Moderating Role of Managerial Environmental Concern. Procedia - Social and
Behavioral Sciences, 62, 854–864. https://doi.org/10.1016/j.sbspro.2012.09.144
Belin, J., Horbach, J., & Oltra, V. (2011). Determinants and specificities of eco-innovations – an econometric analysis for the French and German industry based on the Community Innovation Survey. Cahiers Du GREThA, 17. https://doi.org/10.1080/13662716.2013.833375
Blomquist, T., & Sandström, J. (2004). From Issues to Checkpoints and Back: Managing Green Issues in R&D. Business Strategy and the Environment, 13, 363–373.
Cai, W., & Li, G. (2018). The drivers of eco-innovation and its impact on performance : Evidence from China. Journal of Cleaner Production, 176, 110–118.
https://doi.org/10.1016/j.jclepro.2017.12.109
Calza, F., Parmentola, A., & Tutore, I. (2017). Types of green innovations: Ways of implementation in a non-green industry. Sustainability (Switzerland), 9(8). https://doi.org/10.3390/su9081301 Canhoto, A. I., Quinton, S., Jackson, P., & Dibb, S. (2016). Industrial Marketing Management The
co-production of value in digital, university – industry R & D collaborative projects. Industrial
Marketing Management, 56, 86–96. https://doi.org/10.1016/j.indmarman.2016.03.010
Chen, Y.-S., Chang, C. H., & Wu, F. S. (2012). Origins of green innovations: the differences between proactive and reactive green innovations. Management Decision, 50(3), 368–398.
https://doi.org/10.1108/00251741211216197
Chen, Y., Chang, C.-H., & Lin, Y. (2014). The Determinants of Green Radical and Incremental Innovation Performance: Green Shared Vision, Green Absorptive Capacity, and Green
Organizational Ambidexterity. Sustainability, 6, 7787–7806. https://doi.org/10.3390/su6117787 Cohen, W. M., & Levinthal, D. A. (1990). Absorptive Capacity : A New Perspective on Learning and
Innovation. Administrative Science Quarterly, 35(1), 128–152.
Cooper, V. A., & Molla, A. (2012). Developing Green It Capability: An Absorptive Capacity Perspective. In PACIS 2012 proceedings (Vol. 46). Retrieved from http://aisel.aisnet.org/pacis2012/46 Cuerva, M. C., Triguero-cano, Á., & Córcoles, D. (2014). Drivers of green and non-green innovation:
empirical evidence in Low-Tech SMEs. Journal of Cleaner Production, 68, 104–113. https://doi.org/10.1016/j.jclepro.2013.10.049
Dangelico, R. M. (2016). Green Product Innovation: Where we are and Where we are Going. Business
Strategy and the Environment, 25, 560–576. https://doi.org/10.1002/bse.1886
De Marchi, V. (2012). Environmental innovation and R & D cooperation: Empirical evidence from Spanish manufacturing firms. Research Policy, 41(3), 614–623.
https://doi.org/10.1016/j.respol.2011.10.002
Dong, J. Q. (n.d.). Moving a mountain with a teaspoon: Toward a theory of digital entrepreneurship in
the regulatory environment. Technological Forecasting and Social Change. Elsevier.
https://doi.org/10.1016/j.techfore.2018.07.050
31
Product Innovation Management, 34(4), 526–542. https://doi.org/10.1111/jpim.12384
Dong, J. Q., & Yang, C. H. (2015). Information technology and organizational learning in knowledge alliances and networks: Evidence from U.S. pharmaceutical industry. Information and
Management, 52(1), 111–122. https://doi.org/10.1016/j.im.2014.10.010
Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., Luo, Z., Wamba, S. F., & Roubaud, D. (2017). Can big data and predictive analytics improve social and environmental sustainability?
Technological Forecasting and Social Change, (April), 0–1.
https://doi.org/10.1016/j.techfore.2017.06.020
Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic Capabilities: What are they? Strategic
Management Journal, 21, 1105–1121.
El-Kassar, A. N., & Singh, S. K. (2018). Green innovation and organizational performance: The influence of big data and the moderating role of management commitment and HR practices.
Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2017.12.016
EPI. (2018). Country Profile Germany. Retrieved from https://epi.envirocenter.yale.edu/epi-country-report/DEU
European Commission. (2018). Digital Economy and Society Index (DESI) 2018 Country Report
Germany. https://doi.org/10.2118/140253-MS
Federal Ministry of Education and Research. (2015). Community Innovation Survey 2015. Retrieved from http://ec.europa.eu/eurostat/web/microdata/community-innovation-survey
Federal Ministry of Education and Research. (2016). German Innovation Survey 2016.
Ferreira, J. J. M., Fernandes, C. I., & Ferreira, F. A. F. (2018). To be or not to be digital, that is the
question : Firm innovation and performance. Journal of Business Research (Vol. in press).
Elsevier. https://doi.org/10.1016/j.jbusres.2018.11.013
Frondel, M., Horbach, J., & Rennings, K. (2008). What triggers environmental management and innovation? Empirical evidence for Germany. Ecological Economics, 66(1), 153–160. https://doi.org/10.1016/j.ecolecon.2007.08.016
Gama, F., Sjödin, D. R., & Frishammar, J. (2017). Managing interorganizational technology development: Project management practices for market- and science-based partnerships.
Creativity and Innovation Management, 26(2), 115–127. https://doi.org/10.1111/caim.12207
Ghisetti, C., Marzucchi, A., & Montresor, S. (2015). The open eco-innovation mode. An empirical investigation of eleven European countries. Research Policy, 44(5), 1080–1093.
https://doi.org/10.1016/j.respol.2014.12.001
Giannopoulou, E., Barlatier, P., & Pénin, J. (2019). Same but different ? Research and technology organizations, universities and the innovation activities of firms. Research Policy, 48(May 2017), 223–233. https://doi.org/10.1016/j.respol.2018.08.008
Gluch, P., Gustafsson, M., & Thuvander, L. (2009). An absorptive capacity model for green innovation and performance in the construction industry. Construction Management and Economics, 27(5), 451–464. https://doi.org/10.1080/01446190902896645
Gottschalk, S. (2017). Guide to the survey’s datasets for external users, 1993 to 2017 surveys. The
Scientific-Use-Files of the Mannheim Innovation Panel.
https://doi.org/10.1016/0016-32 3287(94)90072-8
Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., & Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance.
Journal of Business Research, 70, 308–317. https://doi.org/10.1016/j.jbusres.2016.08.004
Hensen, A., & Dong, J. Q. (n.d.). Toward a Digital Innovation Value Chain: Hierarchical Benefits from
Information Technology for Innovation. Information and Management.
https://doi.org/10.1099/00207713-50-2-901
Horbach, J. (2008). Determinants of environmental innovation-New evidence from German panel data sources. Research Policy, 37(1), 163–173. https://doi.org/10.1016/j.respol.2007.08.006 Horbach, J., Belin, J., & Olta, V. (2013). Determinants of Eco-innovation from a European-wide
Perspective - an Analysis based on the Community Innovation Survey (CIS). Industry and
Innovation, 20(6), 523–543.
Joshi, K. D., Chi, L., Datta, A., & Han, S. (2010). Changing the Competitive Landscape : Continuous Innovation Through IT-Enabled Knowledge Capabilities. Information Systems Research, 21(3), 472–495. https://doi.org/10.1287/isre.1100.0298
Kemp, René. (2000). Technology and Environmental Policy — Innovation effects of past policies and suggestions for improvement. Paper for OECD Workshop on Innovation and Environment, 19
June 2000, Paris.
Kemp, Rene, Arundel, A., & Smith, K. (2001). Survey indicators for environmental innovation. Paper
Presented at the Conference Towards Environmental Innovation Systems in Garmisch-Partenkirchen.
Kuzmina, K., Prendeville, S., Walker, D., & Charnley, F. (2018). Future scenarios for Fast-Moving Consumer Goods in a circular economy. Futures, (April), 1–15.
https://doi.org/10.1016/j.futures.2018.12.001
Laursen, K., & Salter, A. (2006). Open for innovation: The role of openness in explaining innovation performance among U.K. manufacturing firms. Strategic Management Journal, 27(2), 131–150. https://doi.org/10.1002/smj.507
Lewin, A. Y., Massini, S., & Peeters, C. (2011). Microfoundations of Internal and External Absorptive Capacity Routines. Source: Organization Science, 22(1), 81–98. https://doi.org/10.1287/orsc Li, A.-Q., & Found, P. (2017). Towards Sustainability: PSS, Digital Technology and Value Co-creation.
Procedia CIRP, 64, 79–84. https://doi.org/10.1016/j.procir.2017.05.002
Li, Y. (2014). Environmental innovation practices and performance: moderating effect of resource commitment. Journal of Cleaner Production, 66, 450–458.
https://doi.org/10.1016/j.jclepro.2013.11.044
Melville, N. P. (2010). Information systems innovation for Environment Sustainability. MIS Quarterly,
34(1), 1–21. https://doi.org/10.1007/BF00847095
Mothe, C., & Nguyen-thi, U. T. (2017). Persistent openness and environmental innovation : An empirical analysis of French manufacturing firms. Journal of Cleaner Production, 162, S59–S69. https://doi.org/10.1016/j.jclepro.2016.12.063
O’Brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality and
Quantity, 41(5), 673–690. https://doi.org/10.1007/s11135-006-9018-6
33 using Total Interpretive Structural Modeling (TISM). Technological Forecasting and Social
Change, 118, 161–169. https://doi.org/10.1016/j.techfore.2017.02.017
Rennings, K., & Rammer, C. (2009). Increasing Energy and Resource Efficiency Through Innovation – An Explorative Analysis Using Innovation Survey Data Increasing Energy and Resource Efficiency Through Innovation – An Explorative Analysis Using Innovation Survey Data. Finance a Uver –
Czech Journal Of Economics and Finance, 59, 442–459.
Roberts, N., Galluch, P. S., Dinger, M., & Grover, V. (2012). Absorptive Capacity and Information Systems Research: Review, Synthesis, and Directions for Future Research. MIS Quarterly, 36(2), 625–648.
Roome, N., & Wijen, F. (2005). Stakeholder Power and Organizational Learning in Corporate Environmental Management. Organization Studies, 27(2), 235–263.
https://doi.org/10.1177/0170840605057669
Saunila, M., Ukko, J., & Rantala, T. (2018). Sustainability as a driver of green innovation investment and exploitation. Journal of Cleaner Production, 179, 631–641.
https://doi.org/10.1016/j.jclepro.2017.11.211
Song, Malin, Fisher, R., & Kwoh, Y. (2018). Technological challenges of green innovation and sustainable resource management with large scale data. Technological Forecasting and Social
Change. https://doi.org/10.1016/j.techfore.2018.07.055
Song, Michael, Berends, H., Van der Bij, H., & Weggeman, M. (2007). The Effect of IT and Co-location on Knowledge Dissemination. Journal of Product Innovation Management, (816), 52–68. Tariq, A., Badir, Y. F., Tariq, W., & Saeed, U. (2017). Technology in Society Drivers and consequences
of green product and process innovation : A systematic review, conceptual framework, and future outlook. Technology in Society, 51, 8–23. https://doi.org/10.1016/j.techsoc.2017.06.002 Teigland, R., Fey, C. F., & Birkinshaw, J. (2000). Knowledge Dissemination in Global R & D Operations :
An Empirical Study of Multinationals in the High Technology Electronics Industry. Management
International Review, 40(1), 49–77.
Verona, G. (1999). A Resource-based View of Product Development. The Academy of Management
Review, 24(1), 132–142.
Wagner, M. (2007). On the relationship between environmental management, environmental innovation and patenting: Evidence from German manufacturing firms. Research Policy, 36(10), 1587–1602. https://doi.org/10.1016/j.respol.2007.08.004
Zahra, S. A., & George, G. (2002). Absorptive Capacity: a review, reconceptualization, and extension.
Academy of Management Journal, 27(2), 185–203.
Zhou, Y., Hong, J., Zhu, K., Yang, Y., & Zhao, D. (2018). Dynamic capability matters: Uncovering its fundamental role in decision making of environmental innovation. Journal of Cleaner
Production, 177, 516–526. https://doi.org/10.1016/j.jclepro.2017.12.208
34
APPENDIXES
Appendix A: Factor loadings environmental innovation
Environmental Innovation Uniqueness
Reduced energy use 0.7067 0.5006
Reduced material use/use of water 0.7083 0.4983
Reduced CO₂ footprint 0.7496 0.4381
Reduced air pollution 0.7044 0.5038
Reduced water or soil pollution 0.6933 0.5194
Reduced noise pollution 0.6633 0.5601
Replaced fossil energy sources by renewable energy sources 0.4758 0.7737 Replaced materials by less hazardous substitutes 0.5601 0.6863
Recycled waste, water, or materials 0.6065 0.6321
Appendix B: Factor loadings environmental innovation
Need for Digitalisation Uniqueness Digital interconnection within production/provision of services 0.6990 0.5114 Digital interconnection between production/service provision and logistics 0.6697 0.5515
Digital interconnection with customers 0.6965 0.5149
Digital interconnection with suppliers 0.6380 0.5930
Teleworking 0.6743 0.5454
Software-based communication (Skype etc.) 0.7403 0.4519
Intranet-based platforms (Wikis etc.) 0.7121 0.4929
E-commerce 0.6692 0.5522
Social media (Facebook, Twitter etc.) 0.6127 0.6245
Cloud computing / cloud applications 0.6627 0.5608
