Organizational adoption behavior of CO2-saving power train technologies: An empirical study on the German heavy-duty vehicles market

Transportation Research Part A 80 (2015) 247–262

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Transportation Research Part A journal homepage: www.elsevier.com/locate/tra

Organizational adoption behavior of CO2-saving power train technologies: An empirical study on the German heavy-duty vehicles market Claudio S. Seitz a,b,⇑, Oliver Beuttenmüller b, Orestis Terzidis a a Karlsruhe Institute of Technology (KIT), Institute for Entrepreneurship, Technology Management and Innovation, Fritz-Erler-Str. 1-3, D-76133 Karlsruhe, Germany b Robert Bosch GmbH, Wernerstr. 51, D-70469 Stuttgart, Germany

a r t i c l e

i n f o

Article history: Received 21 August 2014 Received in revised form 29 June 2015 Accepted 5 August 2015

Keywords: Heavy-duty commercial vehicles Organizational adoption Alternative power train CO2 emission Energy efficiency Road freight

a b s t r a c t This study analyzes the preference structure of buyer groups that influences their willingness to select CO2-saving power train technologies for medium-duty and heavyduty vehicles (HDV). Based on the Technology–Organization–Environment framework for organizational adoption decision making and organizational buying criteria a theoretical construct was developed. Variables were validated in exploratory preliminary research and subsequently tested based on factor analysis using 27 survey items in a quantitative web-based study among 177 organizations operating HDV in Germany. Knowledge, experience, use and purchase consideration concerning alternative power train technologies and further measures to reduce fuel consumption were additionally queried. Based on a multiple linear regression analysis, key findings show that at the current stage of market maturity environmental attitude and corporate social responsibility exert the strongest significant influence on willingness to select CO2-saving power train technologies. A hierarchical cluster analysis revealed six customer groups in order to yield behavioral market segmentation. Hereby it is shown that the performed transportation tasks do not determine the preference structures. Early adopting organizations are larger than average and driven by non-economic aspects as image or corporate social responsibility, whereas the mass market awaits lower purchasing prices. Crossing this chasm will be a major challenge for policymaker and manufacturers. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction After carbon dioxide (CO2) limit values have already been implemented for passenger cars and vans (PC), the European Union is currently preparing a proposal targeting the greenhouse gas emissions for medium-duty and heavy-duty commercial vehicles (HDV).1 Actions and research activities of the commission focus on the supply side of the HDV market (European Commission, 2014). However, the demand side has a key role for future emission levels equally by adopting fuel saving technologies. Recent studies highlighted the efficiency practices among Nordic haulers (Liimatainen et al., 2013) and indicated that ⇑ Corresponding author at: Karlsruhe Institute of Technology (KIT), Institute for Entrepreneurship, Technology Management and Innovation, Fritz-Erler-Str. 1-3, D-76133 Karlsruhe, Germany. E-mail address: [email protected] (C.S. Seitz). 1 On-road commercial vehicles are commonly classified by their gross vehicle weight: Light-duty [3,5t; 6t); Med ium-duty [6t; 16t) and Heavy-duty [16t; 40t). http://dx.doi.org/10.1016/j.tra.2015.08.002 0965-8564/Ó 2015 Elsevier Ltd. All rights reserved.

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energy consumption and energy efficiency practices vary significantly in different freight operations (Liimatainen and Pöllänen, 2010). HDV are used in short and long distance freight and passenger transportation industries as well as other on-road applications (e.g. construction traffic or waste disposal services) and are therefore linked to numerous diverse body types and weight classes (Hill et al., 2011). Beside some studies of management consultancies or private research companies, there has been little research in the HDV industry to highlight its demand side structure and their operating organizations’ preferences for fuel saving measures. These studies postulate the predominant role of cost and quality related criteria in purchasing decisions for HDV (Oliver Wyman GmbH, 2010; KPMG AG, 2006; Bain and Company Inc., 2012). In contrast, a recent publication found indications that soft factors such as environmental concerns could potentially influence buying decisions in the supposedly rational freight transportation business (Liimatainen et al., 2012). On the private PC market, non-economic criteria have a key role for early adopters of alternative power trains, who value environment and new technologies (Plötz et al., 2014). These first consumers are decisive for a successful market penetration of innovations (Rogers, 2003). Hence, both vehicle manufacturer and policy makers are highly interested in the characteristics and behavior of the first large user groups of CO2-saving power train technologies (Plötz et al., 2014). On the PC market, numerous studies have thoroughly analyzed this adoption of environmentally friendly cars (Plötz et al., 2014; Lane and Potter, 2007; Jansson et al., 2010) as well as corresponding consumer preferences for alternative fuel vehicles (Golob et al., 1997; Brownstone et al., 2000; Hackbarth and Madlener, 2013). To the best of the authors’ knowledge, for the HDV market solely one scientific study exists which analyses the customer preferences in the niche of hydrogen street sweepers (Walter et al., 2012). A transfer of the customer insights from PC to HDV cannot be done, since a major distinction lies in the market and industry structure. The PC market follows predominantly business-to-customer (B2C) characteristics, whereas the HDV market underlies a business-to-business (B2B) market structure. This influences the buying and adoption process of emerging technologies on the HDV market significantly, since it is based on the setting of organizational rather than individual adoption. Adoption and buying decisions of HDV customers, in turn, determine the market penetration of technologies reducing the CO2 emissions on the market. Thus, barriers and drivers for adoption decisions of CO2-saving technologies in HDV and of non-private vehicle owners should be studied further (Liimatainen et al., 2012; Lane and Potter, 2007; Seitz and Terzidis, 2014). Against this background, this study aims to highlight the demand side for selected CO2-saving technologies of HDV applying organizational adoption theory. A primary market study shall be conducted to identify early adopting customer groups with respect to their characteristic organizational purchasing considerations for the currently most relevant alternative CO2-saving power train technologies, such as hybridization, liquefied (LNG) or compressed (CNG) natural gas engines, liquefied petroleum gas engines and battery electric power trains (Hill et al., 2011). The selection decision upon such innovative technologies is surveyed in context of the vehicle operators’ specific transportation task requirements, which significantly influences the relative advantage of CO2-saving technologies. The fuel reduction potential from such technologies alters greatly from urban to long-haul transportation (Hill et al., 2011; Law et al., 2011). Furthermore, other fuel and thus CO2-saving measures for improving vehicle aerodynamics, driver performance, or rolling resistance are included in the study as they are mostly linked to lower additional purchasing prices than new power train technologies (Law et al., 2011). Therefore, the research question about preferential structures of diverse customer groups on the HDV market with respect to their willingness to select CO2-saving power train technologies is developed. To answer this question a theoretical framework is developed to explain the organizational innovation decision making on this market. Section 2 reviews related theories in order to deduce variables as well as hypotheses to be tested. In Section 3 the theoretical construct is validated by an explorative preliminary study and the methodology of the quantitative empirical research part is provided. Results of the study are addressed in Section 4. Based on the analysis of the preceding part, Section 5 provides an analysis of the identified customer groups and discusses their influence on the market penetration of CO2-saving power train technologies for HDVs. Finally, limitations and implications are provided in Section 6. 2. Theory and hypotheses Commercial vehicles are – as the name implies – in general used for commercial purposes. Thus, the market on which HDV are supplied and demanded underlies the characteristics of a B2B industry. Purchasers of HDV are businesses that use HDV as production factors to provide products and services for their customers. Hence, this research is based on the setting of organizational adoption to study the buying process and the underlying preferential structures for CO2-saving power train technologies. To extend current research beyond scaled question of buying criteria the focus of this study, and thus the theory section, is based on the organizational behavior of adopting and buying new technologies. 2.1. Theoretical foundations 2.1.1. Organizational adoption of innovations A fundamental approach to innovation adoption behavior follows from the Theory of Reasoned Action and its advancement, the Theory of Planned Behavior (Fishbein and Ajzen, 1975; Ajzen, 1991). Both theories remain on an individual level opposed to an organizational one.

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To explain organizational innovativeness, the Diffusion of Innovations theory uses three independent variables: ‘individual (leader) characteristics’, ‘internal characteristics of organizational structure’, and ‘external characteristics of the organization’ (Rogers, 2003). The diffusion theory thus measures on a company level the organization’s innovation adoption behavior via management openness towards innovation and change, descriptive characteristics of an organization like size, slack, or degree of centralization, and the external system’s openness towards innovation. Hereby, it is quite similar to the Technology–Organization–Environment framework (TOE) Fishbein and Ajzen, 1975; Oliveira and Martins, 2011. Both models identify organizational characteristics as well as external influences on the organization as variables affecting innovation adoption decisions (Tornatzky and Fleischer, 1990). Those general findings are further distinguished, as Frambach and Schillewaert identified a set of factors that have been found to influence the acceptance of new products by organizations: The final adoption decision depends on the ‘perceived innovation characteristics’, the ‘adopter characteristics’ and ‘environmental influences’. ‘Perceived innovation characteristics’ in turn depends on an organization’s ‘social network’, ‘supplier marketing efforts’ and ‘environmental influences’ (Frambach and Schillewaert, 2002). So far, no validation for organizational adoption models on automotive markets can be found. The dominant application of the TOE is the diffusion of different kinds of information technology in organizations. Nevertheless, it provides a more general approach to explain technology diffusion and is thus said to be broadly applicable for different categories of technological innovations (Zhu and Kraemer, 2005). The framework has been used to explain organizational innovation decision making for various technological innovations in different industries and different cultural backgrounds over the years (Baker, 2012; David et al., 2010). Thus, it forms the foundation for the subsequent empirical research. 2.1.2. Organizational buying process For understanding adoption behavior of organizations the specific buying process of organizations should be taken into account as well. The most basic distinction between individual and organizational buying is characterized by the processorientation of organizations whereas consumers tend to buy more spontaneously and thus rather point in time-oriented. The result of such a process – which aims to solve a recognized organizational problem via a multi-personal, information-based and interactive procedure – is a list of individually specified purchasing criteria that will ultimately be manifested in a final selection decision (Hutt and Speh, 2013). Commercial vehicle purchases can be classified as a product business, characterizing standardized offers that are produced in great part before the event of demand from the purchasing organization occurs. This implies that budget and cost considerations will significantly influence selection decisions. Additionally, a vehicle has to fit the organization’s processes so that it can improve on its performance and productivity, without great efforts necessary to adapt to the newly acquired HDV (Ellis, 2011; Brassington and Pettitt, 2006). The process orientation of organizational buying supports the assumption of rationality of purchasing decisions at first glance. Nevertheless, empirical research on emotions involved in organizational buying also concludes that decisions are most commonly not made on the basis of purely rational and cost-benefit oriented criteria (Zehetner, 2011). B2B markets and especially transportation companies are mostly interpreted as driven by derived demand, indicating that they are still dependent on end-consumer preferences which are translated throughout the entire supply chain (Hesse and Rodrigue, 2004). Consequently, end-customer preferences will also impact organizational purchasing decisions. It is important to establish an external image, such as environmental protection, that convinces potential customers to be chosen as the preferred supplier for transportation services. Thus, image considerations will also affect buying decisions of organizations (Ellis, 2011; Brassington and Pettitt, 2006). 2.2. Variables and hypotheses For the research project at hand, representing solely the organizational context, the variables given in the TOE will be mainly adopted. An organization’s origin, size, and scope as well as its most relevant purchasing processes and related hierarchical structures will be examined to analyze its willingness to select CO2-saving power train technologies for HDV. Nevertheless, the framework will be complemented in order to measure different customer groups’ preferences more precisely. Decisive variables considered for power train selection decisions will be mapped as a consequence of the previously mentioned organizational buying and adoption characteristics. This means that the theoretical construct for this research will assume just an indirect effect of the organizational context on the dependent variable. It does not seem appropriate to directly link the variables of the organizational context with the adoption intentions. Instead we describe the organizational context by the corresponding organizational adoption and purchasing behavior. Based on the ensuing behavioral market segmentation we then discuss to which degree the organizational context decides upon the individual organizational preferences. This individual preference structure will subsequently be responsible for the degree of willingness to select CO2-saving power train technologies for HDV. The resulting construct of variables and corresponding hypotheses is illustrated in Fig. 1. 2.2.1. Dependent variable: Willingness to select CO2-saving power train technologies The dependent variable is operationalized as the persistent willingness of deciders within an organization to select CO2-saving power train technologies when making investment decisions for HDV.

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Organization

dependent variable

Independent variables TCO awareness and consideration

Scope and size

Purchasing price sensitivity

H1

Technology H2

Expected usefulness

H3

Resources available Expected ease of use

H4

Willingness to select CO2-saving power train technologies

Technological innovation decision making

H5 Processes and structures

Image factors environment & innovation

Environment H6

Environmental attitudes and CSR

Fig. 1. Variables and hypotheses.

The TOE framework illustrates the influence of an organization’s context on technology adoption decisions. In this context, these are innovative power trains for HDV which have not yet been adopted by most organizations. Consequently, a dichotomous measurement of the willingness to select does not seem reasonable. Purchase intention and consideration are therefore measured as a latent variable (Patterson and Spreng, 1997; Jarvenpaa et al., 1999; Grewal et al., 1998). The terminology used is willingness to select, summarizing interest in innovative power trains, attitude towards the assessed sustainability of conventional power trains, and general purchase intentions and considerations. The measurement shall be nominally validated retrieving information on previous purchases as well as short and mid-term purchase intentions and considerations (Tornatzky and Fleischer, 1990). 2.2.2. Independent variable: Total cost of ownership (TCO) awareness and consideration Rationality in buying decisions is stressed in almost all relevant literature related to B2B. Thus, the result of a considered purchase decision made by a buying center of an organization will be significantly influenced by the evaluation of the expected total cost impact. The relation between TCO calculations and a final considered purchase decision has been empirically confirmed for B2B businesses in general (TriComB2B, 2011) and the commercial vehicle industry in particular (KPMG AG, 2006). There is furthermore a clear distinction between purchasing price and TCO, although the latter includes the initial investment as amortization rates (Brassington and Pettitt, 2006). TCO awareness and consideration describes the degree of importance of TCO to an organization for future selections of innovative power train technologies. It comprises considerations of amortization periods and operating costs with focus on fuel consumption as the frequently most important single component of TCO. Studies of management consultancies on purchasing criteria of European truck operators provide further empirical support for the importance of TCO awareness and consideration (Oliver Wyman GmbH, 2010; Bain and Company Inc., 2012). Keeping in mind the direct correlation of fuel consumption and CO2 emission, and the substantial share of fuel costs in TCO for most HDV applications, the following is hypothesized: H1. There is a positive relationship between TCO awareness and consideration and willingness to select CO2-saving power train technologies for organizational investments in HDV.

2.2.3. Independent variable: Purchasing price sensitivity Although, the initial investment price influences the TCO via amortization rates, it is hypothesized to exert a separate influence on the selection of power train technologies. In part, this contradicts the assumption of rationality in organizational buying decisions. Organizations should not be sensitive towards purchasing prices when investing as long as a positive influence on TCO remains. The influence of the initial investment price is nevertheless academically supported (Sechtin, 2012; Bausback, 2007). Thus, there seems to be an influence of the mere initial investment necessary on the willingness to select CO2-saving power train technologies. Increasing purchasing price sensitivity among commercial vehicle operators in Europe was also empirically confirmed. Purchasing price has become a top three criterion for investment decisions. This can be traced back to increasing competition in the transportation industry and is assumed to be especially relevant for small and medium-sized enterprises (Bain and Company Inc., 2012). Since power trains account for a substantial share in total vehicle prices and alternative power train concepts are currently more expensive than conventional, the following is hypothesized:

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H2. There is a negative relationship between purchasing price sensitivity and willingness to select CO2-saving power train technologies for organizational investments in HDV. 2.2.4. Independent variable: Expected usefulness Usefulness of a technological innovation on an organizational level describes its potential to improve persistent processes and operations (Venkatesh et al., 2003; Davis, 1989). The operationalization as expected usefulness accounts for the perspective on future power train technologies that have not yet seen much adoption. Therefore, an organization can only assess expectations towards certain innovative technologies. Expected usefulness of alternative power trains for HDV comprises likely effects on general processes, transportation task performance, and productivity. Potential improvements of process performance were also highly ranked as a decisive purchase criterion in the TriComB2B study. Executives in B2B businesses evaluated the expected usefulness of considered purchase decisions even higher than their impact on operating costs (TriComB2B, 2011). The use of different power train technologies substantially impacts commercial vehicle operations. Apart from lowering CO2 emission, they differ from conventional diesel power trains in engine performance, sound emissions, or mileage per tank. Since these factors are assumed to significantly positively affect organizational selection decisions for CO2-saving power train technologies, the following is hypothesized: H3. There is a positive relationship between expected usefulness and willingness to select CO2-saving power train technologies for organizational investments in HDV. 2.2.5. Independent variable: Expected ease of use The variable ease of use measures ‘‘the degree to which a person believes that using a particular system would be free of effort” (Davis, 1989). As for expected usefulness, the operationalization expected ease of use accounts for the fact that future adoption behavior is researched. The recognition of importance of this variable can be derived from general organizational buying behavior. Johnston and Lewin subsume under decisive purchasing criteria the risk associated with the purchased item as well as its complexity which both are indicators of the expected ease of use (Davis, 1989). Ajzen identifies ‘perceived behavioral control’ – the expected ease or difficulty of using a technological innovation – as an influential factor on organizational innovation adoption (Liimatainen et al., 2013). For HDV operators, we assume several substantial impediments to select CO2-saving power train technologies related to process risk, increased complexity, and resulting efforts. The persistent refueling infrastructure for gas or battery-electric vehicles is widely insufficient. Service and maintenance efforts are likely to increase using alternative power train technologies. Additionally, there is a lack of experience in using new power trains, inducing an elevated level of uncertainty. Nevertheless, alternative power trains need not necessarily be associated with a reduced expected ease of use. Hybridization of power trains, for example, does not require additional refueling infrastructure. Since perceived facilitating factors on business operations related to the use of alternative power train technologies is expected to support their adoption, the following is hypothesized: H4. There is a positive relationship between expected ease of use and willingness to select CO2-saving power train technologies for organizational investments in HDV. 2.2.6. Independent variable: Image factors environment and innovation Striving for market share, organizations continuously need to identify ways to strengthen their competitive position. In principal, this can be achieved via cost reductions or value enhancement. The relevance of TCO for transportation service providers has already been discussed. At the same time, the external perception of an organization as being environmentally friendly and innovative – summarized as image factors environment and innovation – may help becoming a preferred service provider. Rogers mentions image as one core construct in his Innovation Diffusion Theory, similarly to Johnston and Lewin for organizational buying in general (Bain and Company Inc., 2012; Davis, 1989). Venkatesh et al. include image as an extrinsic motivator to achieve social desirability (Bausback, 2007). In addition, there is empirical confirmation of customer preference for ‘‘green” suppliers for light commercial vehicle applications which is assumed to play an increasingly important role also for operators of HDV (Liimatainen et al., 2013; Piecyk and McKinnon, 2010; Hunke and Prause, 2014). 80% of logistics companies see environmental concerns as a relevant decision criterion in 2020 (Johnston and Lewin, 1996). Consequently, the following is hypothesized: H5. There is a positive relationship between image factors environment and innovation and willingness to select CO2-saving power train technologies for organizational investments in HDV. 2.2.7. Independent variable: Environmental attitude and corporate social responsibility (CSR) In contrast to image as a rather extrinsic motivator for using CO2-saving power train technologies, environmental attitude and CSR shall account for an organization’s intrinsically or transcendently motivated moral obligation to contribute to

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climate protection. From an environmental perspective, it involves environmental care and climate friendliness as inherent obligations of an organization as part of society. Drumwright found empirical evidence for socially responsible organizational buying for noneconomic reasons. Environmental attitude and the perceived obligation to reduce the environmental impact of purchase decisions were identified to be particularly strong when rooted in the founder’s ideals (Drumwright, 1994). Organizations even assign a higher score to the moral aspect of socially responsible behavior than to the strategic or economically beneficial one (van de Ven and Graafland, 2006). Climate protection through reduction of greenhouse gas emissions is considered as an inherent part of environmental care, and consequently of CSR. Several examples addressing that issue for vehicle fleets in Europe can be found. Guidelines for purchasing climate-friendly commercial vehicle fleets are published by a number of initiatives or governmental institutions (Ministry of Housing, 2010). Since organizations can reduce carbon emissions through the adoption and subsequent use of CO2-saving power train technologies, the following is hypothesized: H6. There is a positive relationship between environmental attitude and CSR and willingness to select CO2-saving power train technologies for organizational investments in HDV.

3. Method The approach for the empirical research is twofold. First, a preliminary exploratory study shall validate and confirm the identified variables conducting expert interviews among experienced commercial vehicle dealers. Second, quantitative research shall provide insides into preferences affecting an organization’s willingness to select CO2-saving power train technologies and the corresponding buying process. 3.1. Exploratory preliminary study: Validation of selection criteria Expert interviews were chosen as the methodological approach to confirm the identified independent variables. Experienced sales consultants at commercial vehicle dealers of various brands were identified to suit the prerequisites for the study. Given its preliminary character, a convenience sample was chosen in southern Germany. The five interviewees were consultants for the major brands in the HDV industry. The interviews were conducted in order to obtain general information on purchasing preferences of organizations operating HDV, the general purchasing process, and participants in that process. Thereby, the independent variables influencing customers’ willingness to select CO2-saving power train technologies should be confirmed. The interviewees were unaidedly asked to report on purchasing processes and preferences regarding HDV in general and power trains in particular. Thus, it was avoided to only guide the experts into the direction of the previously researched variables. Furthermore, the interviewees were asked to comment on differences in purchasing preferences of different groups of customers. As being an exploratory study on a barely researched topic, the interviews were conducted using a written, unstructured guideline and an open interview structure. The interviews were recorded and subsequently transliterated and anonymized. For the transliterations, a pragmatic and selective approach was chosen, rephrasing and summarizing the main contents (Bogner et al., 2009). Every commercial vehicle sales consultant mentioned purchasing price sensitivity and expected ease of use as decisive purchasing criteria. The prior was confirmed to be independent of potential TCO advantages in most cases. Expected ease of use was mainly referred to mentioning the lack of refueling infrastructure, increased vulnerability to malfunctions, and higher associated risk. All these factors impede the adoption of alternative, CO2-saving power train technologies. Apart from one interviewee each, the influence of both TCO awareness and consideration as well as image factors environment and innovation was confirmed. Whether this is more likely to be true for smaller or larger organizations could not be resolved conclusively. Nevertheless, larger companies appear to be more cost sensitive due to more rational purchase decisions and clear budget fixations. Four of the interviewees furthermore considered potential improvements of the organization’s image as a major driver for the selection of CO2-saving power train technologies. This acknowledges extrinsic motivation as a definitional component of the variable image factors environment and innovation. Although two out of five experts did not unaidedly mention them as decisive selection criteria, expected usefulness and environmental attitude and CSR are also kept as independent variables since all experts stressed the relevance upon request. Besides the confirmation of the predefined independent variables, the interviewees all highlighted the considerable importance of both sales consultants and drivers on final selection decisions. Their influence will be reflected in the preference structures of the buying centers for the selection of CO2-saving power train technologies. Consequently, they do not constitute additional variables but need to be considered in the measurements of internal processes and structures. Furthermore, it was conclusively stated that the various purchasing methods – namely cash payment, financing, leasing, and rental – have significant influence on selection preferences and consequently the willingness to select CO2-saving power train technologies. These also form a part of an organization’s processes as well as resources available, and a corresponding measure will be included into the subsequent quantitative study.

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3.2. Empirical quantitative research: analyzing preference structures 3.2.1. Questionnaire design The questionnaire consisted of seven principal parts, each comprising a series of distinct questions of different types (cf. Table 1). The screening part was included to identify whether a potential respondent does actually form part of the target group. These are members of an organizational buying center which own or plan to purchase HDV within the upcoming 12 month. The subsequent process part aims to illustrate the respective organizational buying center and corresponding influence structures. In order to validate specific criteria relevant for the selection of a power train, respondents were asked to indicate the degree of importance per criterion on a Likert-type scale. To get an insight into the general attitude and application of a variety of measures to reduce fuel consumption, respondents were asked to indicate which measures they had already taken und which they would take for further reductions. As the selection of CO2-saving power train technologies as one measure to reduce fuel consumption is subject to the principal preference-part of the questionnaire, the lists in this part constitute another control instrument for validation of further responses. The main part of the questionnaire is the analysis of preference structures concerning the willingness to select CO2-saving power train technologies for HDV. The six independent variables as well as the dependent variable were operationalized as latent variables, measured by four to six statements on a 7-point Likert-type scale. Respondents were asked to indicate the degree of agreement from their respective organization’s point of view for each statement. The statements were presented back-to-back in three lists in a predefined random order. Items were formulated in both directions towards and against the construct to be measured in order to control for response biases (Bradburn et al., 2004). While preferences for the selection of CO2-saving power train technologies on a generic level were subject to the previous part, actual selection behavior, intention, and consideration shall be examined subsequently. Finally firmographics and other general data on the respective organization’s purchase behavior and use of HDV was gathered. Before the questionnaire was made available to the target population, it was extensively pretested. HDV market experts, logistics service providers and a relevant German transport and logistics association tested the questionnaire to validate principal contents and the overall understanding. In total, a sample of 24 respondents was yield for the pretest. 3.2.2. Sample and data collection The questionnaire was distributed by German transport and logistics associations among their members. Furthermore, a non-exhaustive list of municipalities in Germany as well as associated passenger transportation companies was created and contacted via a mass mail distributor. Two reminder mails were each sent approximately two weeks after the initial mail or the first reminder, respectively. Due to the dominant snowball sampling approach, it is unclear how many potential members of the target population were informed about the survey. Thus, the success of advertising the questionnaire, expressed through the a-selection rate, cannot be adequately determined. Of the 383 potential respondents in Germany who followed the hyperlink to the questionnaire, 377 actually started the survey (b-selection rate of 98.4%). Of these 377 potential respondents, 214 completed the survey entirely (c-selection rate of 56.8%). As not all of the respondents who completed the survey actually formed part of the target population, the analysis is based upon a sample of 177 respondents in Germany (c-selection rate of 46.9%). Fig. 2 gives an overview of the respondents buying center roles as well as the transportation task their organization is performing predominantly.

4. Results 4.1. Validity and reliability analysis For both the dependent variable as well as the six independent variables, factor analysis is conducted to establish discriminant validity and Cronbach’s a is calculated to assess the measuring instruments’ reliability. In accordance with the results, the variables are adjusted and reinterpreted, if necessary. Eight items were eliminated due to low factor loadings and increase of Cronbach’s a. Before the analysis is conducted, 14 items were recalculated to align their measuring scale towards the theoretical construct. The final explorative factor analysis revealed a Kaiser–Meyer–Olkin measure of 0.820, indicating a good adequacy for the factor analysis of the sample (Ferguson and Cox, 1993). All items had standardized factor loadings of 0.52 or higher (cf. Appendix A). 4.2. Multiple linear regression analysis In order to test the hypotheses specifying an expected relationship between the six independent variables and the dependent variable, a multiple linear regression analysis was performed. Five of the six identified independent variables were expected to exert a positive influence on the willingness to select CO2-saving power train technologies, while only purchasing price sensitivity was hypothesized to negatively influence the dependent variable.

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Table 1 Questionnaire design. Part of the questionnaire

No. of questions

Level of measurement

Principal purpose

Screening Buying process Power train criteria

3 2 2 (1 matrix)

Nominal dichotomous Nominal/ordinal Ordinal (Likert-type scale)

Fuel-saving measures Power train selection preferences Selection behavior, intention, and consideration Organizational details/firmographics

2 (matrices) 3 (35 items/ statements) 2 (matrices)

Nominal Ordinal (Likert-type scales), treated as metric Nominal

Identify respondents as target group members Identify buying center composition and respondents’ roles within it Evaluate the perceived importance of most relevant purchasing criteria for power trains Identify former and intentional taking of fuel-saving measures for HDV Measure organizational preference structures for the selection of CO2saving power train technologies via latent variables Evaluate knowledge about selected power train technologies as well as selection behavior, intention, and consideration

14

Nominal/metric

Gather information on purchasing methods, use of HDV, and firmographics

Table 2 Multiple linear regression analysis results. Model

Unstandardized coefficients

(Constant) TCO awareness and consideration Purchasing price sensitivity Expected usefulness Expected ease of use Image factors environment and innovation Environmental attitude and CSR

Standardized coefficients

B

Std. error

Beta

0.003 0.132 0.204 0.426 0.320 0.193 0.511

0.060 0.061 0.058 0.058 0.056 0.059 0.066

0.128 0.211 0.434 0.337 0.192 0.461

t

Sig.

0.058 2.162 3.528 7.329 5.696 3.247 7.752

0.954 0.033 0.001 0.000 0.000 0.002 0.000

ANOVA regression: F = 30.116, Sig. = 0.000, n = 117.

Roles in Buying Center

Transportation Task 14

4,5%

35

8,5%

Decider User (driver) Consultant

122

29

Buyer 15,8%

1,7%

54,2%

20

Not involved Not specified

15,3%

34 78 n=177

Goods traffic -long haul Goods traffic regional distribution Goods traffic -urban distribution Passenger transportation Waste disposal services Construction traffic Not specified / others

Fig. 2. Sample description based on respondents buying center roles and their transportation task.

The R2 of 0.630 indicates, that 63.0% of the variance in the dependent variable are explained by the variance in the six independent variables. Calculating the empirical F-value (30.12) using SPSS and comparing it with the theoretical value (3.28), reveals that the model is highly significant. The influence of all independent variables on the dependent variable is significant (Sig. < 0.05). Environmental attitude and CSR exerts the strongest positive influence with a standardized beta coefficient of 0.461, followed by expected usefulness with 0.434, expected ease of use with 0.337, image factors environment and innovation with 0.192, and TCO awareness and consideration with 0.128. Purchasing price sensitivity exerts a significant negative influence of 0.211 on the measuring value of the dependent variable for every one unit increase in its own measuring value. Due to the significant influence of each independent variable on the dependent variable, all hypotheses H1–6 are retained (cf. Table 2).

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4.3. Cluster analysis Using a hierarchical cluster analysis, the respondents are classified into intra-homogeneous and inter-heterogeneous groups on the basis of the extracted factor values relating to the six independent variables. Due to the pairwise exclusion of cases for the exploratory factor analysis, factor values could only be saved via regression for 117 responses. Thus, 60 (33.9%) cases would not be included into the cluster analysis. Therefore, it was decided to reconduct the exploratory factor analysis, replacing missing values per item by the respective mean value. The factor solution is identical to the one yield before. Factor loadings only marginally vary from the solution specifying pairwise exclusion of cases (Kaiser–Meyer–Olkin of 0.828). Factor values were saved for all 177 responses, specifying regression. In order to validate the selected approach, a hierarchical cluster analysis using Ward’s method (Burns and Burns, 2008) measuring squared Euclidean distances was conducted for both sets of factor values (including 117 and 177 responses, respectively). Based on the visualized dendogram and the change in the coefficients given in the agglomeration schedule, a six cluster solution was chosen for both. In order to assess whether there is a relationship between the six-cluster solution for the 117 responses with the one for all 177 responses, the clusters were saved as variables and compared in a contingency table. The revealed v2 of 240.184 indicates that there is a relationship between both cluster solutions. Cramer’s V of 0.641 is highly significant and indicates a strong relationship between the two nominal variables. Consequently, the six cluster solution including all 177 responses is kept for further analyses. Cross tabulation revealed frequencies and shares of the various principal transportation tasks in each cluster. Cramer’s V was calculated to assess whether there is a relationship between an organization’s principal transportation task and its belonging to a certain cluster. Its significant value of 0.245 indicates a weak relationship. All clusters are rather heterogeneous (cf. Fig. 3). Summarizing, it is concluded that a HDV-operating organization’s principal business is not necessarily decisive for its preference structure affecting its willingness to select CO2-saving power train technologies. 5. Discussion From the results of the multiple linear regression analysis, it can be concluded that at the current stage of market maturity the willingness to select an alternative power train technology is most strongly driven by environmental attitude and CSR. Organizations which consider climate-friendliness as an inherent part of their philosophy and which have special interest in alternative power train technologies for reasons of climate protection are most likely to be willing to select such technologies. This strongly explains previous weak indications of altruistic objectives for energy efficiency actions in the logistic industry (Liimatainen et al., 2012). The willingness to select also increases when it is expected to improve persistent business operations and processes, and consequently leads to increasing customer value and returns. It is furthermore of importance that the selection of CO2-saving power trains is not expected to compromise the performance of transportation tasks, and does not lead to elevated risk, incalculable complicacies, vulnerability to malfunctions, or extra work for an organization. Less but still significant positive influence on the willingness to select alternative power train technologies is exerted by image factors environment and innovation. Additionally, the willingness to select is increased if fuel consumption data, the requirement of a positive total cost balance, and expectations on payback time and amortization of additional costs are considered important for selecting an alternative power train. The low importance of TCO for the willingness to select is aligned with the fact that in most cases HDV equipped with alternative power train concepts have a negative business case compared to conventional ones(Law et al., 2011). In contrast, the willingness to select CO2-saving power train technologies decreases with an elevated general preference for lower-priced power trains, the relative importance of purchasing prices for selection decisions, and the attitude that higher purchasing prices render alternative power train technologies unattractive. The results contradict common industry assumptions of purely cost related buying criteria and economically rational behavior. However, the phenomenon that non-economic buying criteria lever the attractiveness of innovative technologies is widely proved for B2C car markets (Plötz et al., 2014) and few B2B studies (Beatty et al., 2001). Therefore, we assume the same effect of organizational Innovators and Early Adopters (Rogers, 2003) in the HDV market as well. Hence, we derived a behavioral segmentation of the German HDV market based on the cluster analysis of the organizations preferential structures and current awareness, consideration and adoption of CO2-saving technologies. Additionally, we analyze whether organizational characteristics in these customer groups influence the willingness to select alternative power train technologies to conclude directly from organizational context to adoption intentions. 5.1. Customer groups of the German HDV market The cluster analysis revealed six customer groups of organizations on the basis of their distinct preferences influencing their willingness to select CO2-saving power train technologies for their HDV. These are mapped with the different adopters in the Diffusion of Innovations theory (Rogers, 2003). Cluster 1 comprises the Technical Doubters of alternative power trains, which are mainly small and medium sized enterprises (SME) with less than 250 employees. The expected TCO over useful life as well as fuel consumption together with a power train’s reliability are the highest ranked purchasing criteria for power trains. Consequently, TCO and low price

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Cluster 1 (n=32)

Cluster 2 (n=37)

1 1

2

5

11

1

2

2

4 10

3

1 8

4

Cluster 5 (n=25)

Cluster 6 (n=13) 1

2

4

9

26

Waste disposal services

10

Construction traffic

2

2 2

Passenger transportation - short-distance traffic

4

2

5

1

2

1

3

1

Goods traffic - heavy load

7

Cluster 4 (n=44)

Goods traffic - regional distribution Goods traffic - urban distribution

19

1

Goods traffic - long haul

2

2

3

3

Cluster 3 (n=26)

4

Others

4

Not specified

Fig. 3. Principal transportation tasks per cluster.

Fig. 4. Customer groups of HDV market.

sensitivity are drivers for the adoption of CO2-saving power train technologies. On the other hand, there seems to be an expectation of elevated risk and vulnerability to malfunctions caused by eventual adoption, indicated by a comparatively very low value of the expected ease of use and usefulness. These organizations hardly expect positive effects of the technologies in question on the performance of their transportation tasks. Cluster 2 organizations show the lowest sensitivity to elevated purchasing prices. Additionally, these organizations seem to be aware of the actual TCO disadvantage of alternative power train technologies. Hence, TCO hinders the willingness to select significantly. Furthermore, the rationality is supported by the fact that image or CSR are considered insignificant. Moreover, no barrier exists due to expected complicacies caused by the use of CO2-saving power train technologies. Consequently, the Pragmatic Mainstream only shows moderate interest in them. Organizations comprised in cluster 3, the Green Innovators, tend to value the positive effect of innovative CO2-saving power train technologies on their image towards customers decisively higher than the remaining customer groups.

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Additionally, their willingness to select such technologies is also driven by environmental attitude and CSR. It becomes apparent that these organizations confirm typical behavior of Early Adopters. Consequently, this group shows the highest current adoption of alternative power trains. At the same time, they consider the ease of use as the most relevant barrier for their willingness to select compared to other clusters. 42.3% of these organizations operate their HDV mainly in municipal transportation tasks – passenger transportation and waste disposal. Cluster 4 is predominantly characterized by SME conducting different kinds of goods traffic (81.8% of its organizations). Cluster 4 organizations are least willing to select CO2-saving power train technologies for their HDV. This is mainly caused by the lowest value for environmental attitude and CSR and the highest purchasing price sensitivity. Besides aerodynamic optimization and low rolling resistance tires, this cluster shows also the least adoption for all other CO2-saving technologies. Additionally, they have the lowest experience and knowledge about alternative power trains. Summarizing, this cluster comprises the Conservative Laggards (Rogers, 2003). A comparatively high share of organizations in Cluster 5 is conducting their transportation task within the construction and disposal business. Despite a high CSR, the fairly low expected potential for process and operation improvements and low valuation of alternative power trains as image enhancing factors towards customers render these technologies relatively uninteresting for them. Consequently, the willingness and thus experience as well as knowledge remain limited. These organizations form the group of Cautious Followers. Cluster 6 shows the highest willingness to select CO2-saving power train technologies for HDV. This is mainly driven by a far above average expected usefulness of alternative power train technologies and an elevated environmental attitude and importance of CSR. Consequently, organizations summarized in that cluster expect to be able to improve their services and yield higher returns due to the use of alternative power trains. Furthermore, they regard climate-friendliness as an inherent part of their respective organization’s philosophy and social responsibility. This is consistent with the criteria considered most important when selecting an alternative power train by these organizations. CSR and customer preferences rank upon the most important ones relative to the other five clusters. This small group of Early Green Adopters is predominantly characterized by a comparatively high share of large companies (cf. Fig. 4). 5.2. Buying process for HDV The preliminary study indicated a relationship between the purchasing habits and the willingness to select CO2-saving power train technologies. Although both Conservative Laggards and Early Green Adopters show a comparatively elevated preference for leasing their HDV, there cannot be determined a clear trend for the different clusters concerning the purchasing method chosen for the vehicles forming their current fleet (cf. Fig. 5). Organizations that would choose a different purchasing method for HDV with non-conventional power trains hardly tend towards cash payments. It involves the highest risk with respect to liquidity and liability. In contrast, leasing and rental as the preferred purchasing methods in sum account for 58.1% of all organizations that would not prefer to maintain their current methods of purchasing. The single most preferred mode still is financing with a share of 34.9%. No differences between the identified clusters were revealed by the analysis of the importance of different positions in the buying center. In all clusters, the respective organizations’ entrepreneurs or top management, respectively, rank first, followed by the drivers. The next three ranks are always occupied either by the responsible fleet manager, the purchasing manager, or the organization’s commercial vehicle dealer. Thus, a distinguishable buying process for each cluster could not be identified through the applied measure in the questionnaire. On a cluster solution basis, no significant differences concerning major purchasing premises could be identified. Neither usage nor amortization periods of HDV vary significantly among the clusters. However, survey results show organizations plan usage and amortization periods of HDV that are to be purchased based on the intended transportation task. The usage periods vary between 4.5 years for long-haul trucks up to 13 years for city busses. This supports existing assumptions in publications (Humphrey et al., 2007) and open market information. Summarizing the analysis of the customer groups and their underlying buying process, we can conclude: no differences between the clusters were revealed concerning an organization’s scope (transportation task), resources available, or structure and processes (buying center, purchasing methods, type of enterprise). However, the size of an organization influences positively the willingness-to select alternative power train technologies. We observed a higher willingness to select alternative power trains by customer groups which comprise organizations operating larger fleets and employing more workers compared to the industry majority. This might explain the fact, why larger transportation companies tend to be more active in energy efficiency issues than smaller ones (Liimatainen et al., 2012). Therefore, the organizational context only describes to a very limited degree the individual organizational preference structures for alternative power train technology adoption. 5.3. Awareness, consideration and adoption of CO2-saving technologies Up to date, alternative power trains have been the most rarely taken measure to reduce fuel consumption of HDV in Germany (cf. Fig. 6). This is congruent with surveys in Scandinavia (Liimatainen et al., 2013). However, about 30% of Green Innovators in Germany have already adopted alternative power train technologies. In contrast, among the measures considered for further reductions, alternative power trains rank first or second for every cluster among all measures. Especially Early Green Adopters and Cautious Followers in majority consider this measure. Given the already elevated adoption rate

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Technical Doubters Pragmatic Mainstream Cash Financing Leasing Rental

Green Innovators Conservative Laggards Cautious Follower Early Green Adopter

n=177

0%

20%

40%

60%

80%

100%

Fig. 5. Current purchasing methods per adopter group.

Alternative power trains Vehicle measures Aerodynamically optimized driver's cab Low rolling-resistance tires Driver training Driver assistance systems for fuel reduction

Technical Doubters Pragmatic Mainstream Green Innovators

Power train reconfiguration

Conservative Laggards Cautious Follower

None

Early Green Adopter

n=177

0%

20% 40% 60% 80% 100% 0%

20% 40% 60% 80% 100%

Fig. 6. Measures to reduce fuel consumption.

for alternative power trains of Green Innovators, the third rank in future considerations supports their statistically high willingness to select CO2-saving power train technologies. The cumulatively previously most selected measures are driver trainings and the use of low rolling-resistance tires, which is in line with previous research (Liimatainen et al., 2012; Léonardi and Baumgartner, 2004). Both measures neither involve high investments nor unknown and thus risky technologies. The drivers’ important role for possible reductions of fuel consumption was also stressed by all commercial vehicle dealers during the preliminary study. Further measures are chosen in similar frequencies. Two aspects of information gained from the analysis of previously adopted and for the future considered measures shall be summarized separately: First, alternative power trains appear to play an important role in future efforts to reduce fuel consumption of HDV. This differs significantly from other northern European countries, where 80% of companies are not familiar or won’t consider hybrid vehicles (Liimatainen et al., 2012). Second, for further reductions of fuel consumption in the future, the share of organizations that indicated to select none of the above mentioned measures highly increases in comparison to measures already taken. This might be caused by saturation due to previously taken measures or that potentials for further reduction of fuel consumption are unknown or not considered being worth additional efforts. It is furthermore noticeable that there is still little experience with alternative power train technologies. Green Innovators have already tested power trains powered by CNG, electric hybrids (both 26.9%), and purely battery electric power trains (23.1%). The latter was also previously tested by 23.1% of Early Green Adopters. The share of organizations having already experienced the remaining power train technologies hardly reaches 10%. Especially hydraulic hybrids and LNG trucks have hardly been tested and are the most unknown power trains as these are the most recent and immature technologies of the suggested set. With regards to the power train technologies that are known or have already been tested by the responding organizations, there is little clear intention to purchase a certain power train with the next HDV. Nevertheless, some technologies prove strong according to the adoption behavior if they have been previously tested. Of all organizations having already experienced CNG trucks previously, 68.8% have such trucks in current use. Considering only clusters 1, 2 and 3, these shares amount to 83.3%. Also for electric hybrids, the adoption rate amounts to 66.7% for organizations that have already tested

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such a power train technology. Apart from the three gas-fueled power train technologies, the remaining organizations within Early Green Adopters show very high rates of purchase intention or consideration towards alternative power train technologies. The rate amounts to 100% for hydraulic and electric hybrids, and 75% for battery electric HDV. The lowest share of use, purchase intention, or consideration is revealed for Technical Doubters and Conservative Laggards concerning battery electric vehicles. Given their clearly above average awareness and consideration of TCO and their comparatively low willingness to select CO2-saving technologies in general. The current use, purchase consideration, and knowledge about alternative power trains is further evidence for the customer groups on the basis of innovativeness (Bain and Company Inc., 2012). 6. Conclusion Our results show that at the current stage of market maturity CO2-saving power train technologies tend to be adopted or considered for environmental reasons. Although it is evident that they offer potential for operating cost savings, this does not appear to be a predominant argument favoring their adoption, currently. Their impact on the performance of transportation tasks and productivity as well as the associated additional efforts related to the adoption exert also a higher influence. These findings are fostered on a customer group perspective. Mapping the customer groups against the descriptive organizational characteristics, no significant correlation with the willingness to select CO2-saving power train technologies could be identified except for an organization’s size. There is a clear tendency towards an elevated willingness shown by organizations operating larger fleets and employing more workers compared to the industry majority. Such organizations rather tend to establish corporate guidelines than smaller ones and are thus more likely to include an environmental attitude and CSR into their general business philosophy. Also, a high degree of public presence and exposure to society like in short-distance bus traffic or waste disposal services, may positively affect an organization’s willingness to early adopt HDV equipped with alternative power trains. This is supported by the comparatively large share of these transportation tasks within the Green Innovators (45.9%). Nevertheless, a significant high relationship between transportation task and the preference structure affecting an organization’s willingness to select CO2-saving power train technologies could not be determined for other transportation tasks. Summarizing, this study contradicts the existing assumption of purely rational and cost-oriented buying behavior in the transportation sector. Though this phenomena doesn’t exists for the customer majority but it is especially valid for those few early adopting organizations which currently boost the market penetration of innovative power train technologies. The mass market is still highly cost-sensitive, whereas CSR and an external image are decisive for Innovators preceding adoption. Hence, the HDV industry may currently be facing the very important challenge of crossing the chasm (Moore, 2006) towards successful diffusion of alternative power trains. Furthermore, we showed also the existence of these early adopting organizations on HDV market. The first time customer groups on the HDV market were identified based on a behavioral market segmentation. The description of these and the energy efficiency practices in Europe’s most relevant HDV market, Germany, help both practitioners and researchers in this early stage of market development to lever their understanding of the diffusion of alternative power trains in HDV. 6.1. Limitations and further research The study has several limitations and various needs for further research were identified. First, the sample for both studies was non-probabilistic and mainly convenience, consequently a random sample would be more appropriate for further research. Second, as the present study has shown, non-rational aspects significantly influence organizational buying behavior and variations between the different countries are probable. To gain insights on a European level, further lead markets should be included for further research. Third, the use of an online survey biased the collected sample towards those organizations that are experienced in the use of information technology and internet applications. Furthermore, exploratory factor analysis was reconducted for the hierarchical cluster analysis, and missing values were replaced by means per item. This naturally biases the sample to a certain degree. The variance in the data is underestimated, and the mean value is overrepresented. Furthermore, it leads to a certain understatement of covariances and correlations between the variables (Enders, 2010; Jamshidian, 2004). A clear-cut picture, providing precise externally distinguishable characteristics for the six identified clusters could not be drawn. With the gained findings, further research can be effectively designed more focused and thus yield more precise and significant characterizations of different customer groups. The identified variables affecting an organization’s willingness to select CO2-saving technologies should thereby be kept as their significant impact on behavior was statistically validated. We suggest to study especially the effect of public presence in urban use cases and to extend this study to organizational fleets on the PC market. The results highlight an elevated acceptance of innovative power train technologies in urban use cases. Thus, these use cases can be a driver for the diffusion of CO2-saving power train technologies. Hence, we suggest an analysis of the future market penetration of such technologies in different transportation tasks and customer groups, respectively. To map the defined customer groups more precisely against the various selected power train technologies the specific manifestations for different transportation tasks of the independent variables influencing an organization’s willingness should be researched more thoroughly. To conduct detailed research a conjoint analysis is suggested.

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6.2. Managerial and political implications For marketers of CO2-saving power train technologies in the HDV industry the main implication of the study is that economically irrational aspects significantly influence organizational buying behavior. From a marketing communications perspective, perceived eco-credential fosters the probability of such technologies to be chosen by early adopting customers. An active communication and visible brandings of alternative power train concepts could lever their sales by responding to customer needs for CSR compliant power trains. At the same time targeting Innovators can be a more cost-effective promotional strategy during the initial stages of market development. Beyond top-management decision makers, drivers and sales consultants take a decisive role within a buying center of HDV-operating organizations. For manufacturer of HDV the results reveal that alternative power trains are most considered for future fuel consumption reduction. CO2-saving power train technologies should thus be offered in manufacturer’s model portfolio. However, the majority of organizations operating HDV perceive their current attractiveness as not competitive caused by high purchasing prices. For governmental policymakers devised to decrease carbon footprint of freight and public passenger transportation, the findings imply that incentives are needed for crossing the chasm so that CO2-saving power train technologies are also selected due to advantageous TCO and usefulness. These factors are among the highest ranked investment criteria in the buying process and could contribute significantly to convert the comparatively high confidence in CO2-saving power train technologies for further fuel reduction into an increased willingness to select them. Acknowledgements We thank three anonymous reviewers for their very helpful comments and the German logistic industry associations for distributing the survey among their members. The project was funded by the Robert Bosch GmbH. Furthermore the authors would like to thank Ellen Mailänder for her valuable input and proof reading.

Appendix A See Table A1.

Table A1 Factor analysis results. Factor

Survey items

Standardized Factor loading

Willingness to select

Cronbach‘s

a 0.819

Alternative power trains are interesting for our organization Within the next 10 years, it will be necessary for our organization to purchase commercial vehicles with innovative power train technologies. As long as possible, our organization will continue purchasing commercial vehicles with conventional diesel power trains There is a high willingness to purchase commercial vehicles with alternative power trains in our organization

0.859 0.852 0.803 0.698

TCO awareness and consideration

0.732 Fuel consumption data are decisive for the selection of an alternative power train. The total cost balance of an alternative power train must be positive The expected payback time is decisive for the selection of an alternative power train Extra costs for an alternative power train compared to a conventional one have to pay for itself

0.769 0.718 0.696 0.528

Purchasing price sensitivity

0.703 The price is decisive for which power train will be chosen A lower-priced power train is generally preferable High purchasing prices render alternative power trains unattractive

0.854 0.652 0.605

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Survey items

Standardized Factor loading

Expected usefulness

Cronbach‘s

a 0.866

The use of alternative power trains augments our productivity The use of alternative power trains improves business operations. Our organization can improve its services by using alternative power trains Through the use of alternative power trains, we can strengthen our image compared to the competition Climate-friendly power train technologies in our commercial vehicles make our organization more attractive to potential customers

0.857 0.770 0.696 0.690

The use of alternative power trains compromises the performance of our transportation tasks The use of alternative power trains entails disadvantages for performing our transportation tasks. The use of alternative power trains entails incalculable complicacies The vulnerability to malfunctions of our commercial vehicles increases through the use of alternative power trains The use of alternative power trains creates extra work for our organization

0.794

0.671

Expected ease of use

0.855

0.771 0.720 0.696 0.692 0.702

Image factors environment and innovation It is irrelevant to our customers whether or not our commercial vehicles are always equipped with state-of-the-art technology For decisions that we make as an organization, environmental aspects are irrelevant A ‘‘green image” of our organization is unimportant to our customers

0.711 0.643 0.634

Environmental attitudes and CSR

0.891 Climate-friendliness is an inherent part of our organization’s philosophy It is part of our responsibility as an organization to use climate-friendly technologies Innovative low-emission power train technologies are, for reasons of climate protection, of special interest for our organization

0.858 0.816 0.814

References Ajzen, I., 1991. The theory of planned behavior. Organ. Behav. Hum. Dec. Process. 50, 179–211. Bain & Company Inc., 2012. Winning in Europe: Truck Strategies for the Next Decade: Lessons from Our Customer Loyalty Study, München. Baker, J., 2012. The technology–organization–environment framework. In: Dwivedi, Y.K., Wade, M.R., Schneberger, S.L. (Eds.), Information Systems Theory. Springer New York, New York, NY, pp. 231–245. Bausback, N., 2007. Positionierung von Business-to-Business-Marken: Konzeption und empirische Analyse zur Rolle von Rationalität und Emotionalität. DUV, Wiesbaden. Beatty, R.C. et al, 2001. Factors influencing corporate web site adoption: a time-based assessment. Inform. Manage. 38, 337–354. Bogner, A. et al. (Eds.), 2009. Interviewing Experts. Palgrave Macmillan, Houndmills. Bradburn, N.M. et al, 2004. Asking Questions: The Definitive Guide to Questionnaire Design – For Market Research, Political Polls, and Social and Health Questionnaires. John Wiley & Sons Inc., Hoboken. Brassington, F., Pettitt, S., 2006. Principles of Marketing, fourth ed. Financial Times/Prentice Hall, Harlow. Brownstone, D. et al, 2000. Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles. Transport. Res. Part B: Methodol. 34, 315–338. Burns, R.B., Burns, R.A., 2008. Business Research Methods and Statistics Using SPSS. SAGE, Los Angeles. David, D. et al, 2010. Factors influencing the adoption of technologies in developing countries: an empirical study. J. Acad. Bus. Econ. 10. Davis, F.D., 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quart. 13, 319–340. Drumwright, M.E., 1994. Socially responsible organizational buying: environmental concern as a noneconomic buying criterion. J. Market. 58, 1. Ellis, N., 2011. Business-to-Business Marketing: Relationships Networks & Strategies. Oxford Univ. Press, Oxford. Enders, C.K., 2010. Applied Missing Data Analysis. Guilford Press, New York. European Commission, Strategy for Reducing Heavy-Duty Vehicles’ Fuel Consumption and CO2 Emissions, Brussels, 2014. Ferguson, E., Cox, T., 1993. Exploratory factor analysis: a users’ guide. Int. J. Select. Assess. 1, 84–94. Fishbein, M., Ajzen, I., 1975. Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison Wesley, Reading, Mass. Frambach, R.T., Schillewaert, N., 2002. Organizational innovation adoption: a multi-level framework of determinants and opportunities for future research. Market. Theory Next Millennium 55, 163–176. Golob, T.F. et al, 1997. Commercial fleet demand for alternative-fuel vehicles in California. Transport. Res. Part A: Policy Practice 31, 219–233. Grewal, D. et al, 1998. The effects of price-comparison advertising on buyers’ perceptions of acquisition value, and behavioral intentions. J. Market. 62, 46–59. Hackbarth, A., Madlener, R., 2013. Consumer preferences for alternative fuel vehicles: a discrete choice analysis. Transport. Res. Part D: Transport Environ. 25, 5–17. Hesse, M., Rodrigue, J., 2004. The transport geography of logistics and freight distribution. J. Transport Geogr. 12, 171–184. Hill, N., et al., 2011. Reduction and Testing of Greenhouse Gas (GHG) Emissions from Heavy Duty Vehicles: Lot 1: Strategy. Final Report to the European Commission – DG Climate Action. Humphrey, A.S. et al, 2007. Evaluating the efficiency of trucking operations with weekend freight leveling. Int. J. Phys. Distrib. Log. Manage. 5, 360–374. Hunke, K., Prause, G., 2014. Sustainable supply chain management in german automotive industry: experiences and success factors. JSSI 3, 15–22. Hutt, M.D., Speh, T.W., 2013. Business Marketing Management: B2B, 11th ed. South-Western Cengage Learning, Mason, Ohio.

262

C.S. Seitz et al. / Transportation Research Part A 80 (2015) 247–262

Jamshidian, M., 2004. Strategies for analysis of incomplete data. In: Hardy, M., Bryman, A. (Eds.), Handbook of Data Analysis. SAGE Publications, London, pp. 113–130. Jansson, J. et al, 2010. Green consumer behavior: determinants of curtailment and eco-innovation adoption. J. Consumer Market. 27, 358–370. Jarvenpaa, S.L. et al, 1999. Consumer trust in an internet store: a cross-cultural validation. J. Comput.-Med. Commun. 5. Johnston, W.J., Lewin, J.E., 1996. Organizational buying behavior: toward an integrative framework. J. Bus. Res. 35, 1–15. KPMG AG, 2006. Die europäische Nutzfahrzeugindustrie im Zeichen der Globalisierung. Lane, B., Potter, S., 2007. The adoption of cleaner vehicles in the UK: exploring the consumer attitude–action gap. J. Clean. Product. 15, 1085–1092. Law, K., Jackson, M., Chain, M., 2011. European Union Greenhouse Gas Reduction Potential for Heavy-Duty Vehicles. TIAX, Cupertino. Léonardi, J., Baumgartner, M., 2004. CO2 efficiency in road freight transportation: status quo, measures and potential. Transport. Res. Part D Transport Environ. 9, 451–464. Liimatainen, H., Pöllänen, M., 2010. Trends of energy efficiency in Finnish road freight transport 1995-2009 and forecast to 2016, Special Section: Carbon Reduction at Community Scale, vol. 38, pp. 7676–7686. Liimatainen, H. et al, 2012. Energy efficiency practices among road freight hauliers. Energy Policy 50, 833–842. Liimatainen, H. et al, 2013. Energy efficiency of road freight hauliers—a Nordic comparison. Energy Policy, 378–387. Ministry of Housing, 2010. Spatial Planning and the Environment, Criteria for the Sustainable Public Procurement of Heavy-Duty Motor Vehicles, The Hague. Moore, G.A., 2006. Crossing the Chasm: Marketing and Selling Disruptive Products to Mainstream Customers. HarperCollins Publ, New York. Oliveira, T., Martins, M.F., 2011. Literature review of information technology adoption models at firm level. Electron. J. Inform. Syst. Eval. 14, 110–121. Oliver Wyman GmbH, 2011. European Truck Customer 2010: Customer expectations in the commercial vehicle industry, München. Patterson, P.G., Spreng, R.A., 1997. Modelling the relationship between perceived value, satisfaction and repurchase intentions in a business-to-business, services context: an empirical examination. Int. J. Serv. Ind. Manage. 8, 414–434. Piecyk, M.I., McKinnon, A.C., 2010. Forecasting the carbon footprint of road freight transport in 2020. Int. Global Supply Chain 128, 31–42. Plötz, P. et al, 2014. Who will buy electric vehicles? Identifying early adopters in Germany. Transport. Res. Part A: Policy Practice 67, 96–109. Rogers, E.M., 2003. Diffusion of Innovations, fifth ed. Free Press, New York, NY. Sechtin, R., 2012. Emotional differentiation for influencing purchase decisions in industrial markets, Dissertation, Univ. Stuttgart. Seitz, C., Terzidis, O., 2014. Market penetration of alternative powertrain concepts in heavy commercial vehicles: a system dynamics approach. In: Proceedings of the 2014 International Conference of the System Dynamics Society, vol. 32. Tornatzky, L.G., Fleischer, M., 1990. The Processes of Technological Innovation, fourth ed. Lexington Books, Lexington, Mass. TriComB2B, 2011. The Considered Purchase Decision: What Matters, What Doesn’t And What It Means For B2B Marketing and Sales, Dayton. van de Ven, B., Graafland, J.J., 2006. Strategic and Moral Motivation for Corporate Social Responsibility. University Library of Munich, Germany. Venkatesh, V. et al, 2003. User acceptance of information technology: toward a unified view. MIS Quart. 27, 425–478. Walter, S. et al, 2012. Assessing customer preferences for hydrogen-powered street sweepers: a choice experiment. Int. J. Hydrogen Energy 37, 12003– 12014. Zehetner, A., 2011. Emotions in organisational buying behaviour: a qualitative empirical investigation in Austria. Model. Value 1, 345–368. Zhu, K., Kraemer, K.L., 2005. Post-adoption variations in usage and value of E-business by organizations: cross-country evidence from the retail industry. Inform. Syst. Res. 16, 61–84.

Glossary B2B: business-to-business B2C: business-to-consumers CNG: compressed natural gas CO2: carbon dioxide CSR: corporate social responsibility HDV: medium-duty and heavy-duty vehicles LNG: liquefied natural gas PC: passenger cars and vans SME: small and medium sized enterprises TOE: Technology–Organization–Environment framework TCO: total cost of ownership