How Organizational Structure Affects Ecological Responsiveness

Abstract

In this article, we analyze the relationship between the main dimensions of organizational structure (i.e., formalization, centralization, and complexity) and ecological responsiveness in a sample of 109 firms in the European air passenger transport industry. Broadly confirming our hypotheses, the results show that high formalization of routine tasks favors ecological responsiveness. Structures characterized by high decentralization and low complexity also favor ecological responsiveness. Furthermore, decentralization has a significant, positive relationship with ecological responsiveness among firms with low vertical complexity. Overall, the results indicate that organizational structure plays a major role in ecological responsiveness.

Keywords

aviation industry environmental responsiveness organizational structure structural complexity

In response to increasing demands imposed by their stakeholders and society, most firms have adopted different initiatives oriented toward reducing the environmental impact of their activities. Bansal and Roth (2000) defined corporate ecological responsiveness as

a set of corporate initiatives with the aim of mitigating a firm’s impact on the natural environment. These initiatives can include changes to the firm’s products, processes, and policies, such as reducing energy consumption and waste generation, using ecologically sustainable resources, and implementing an environmental management system. (p. 717)

The concept of ecological responsiveness refers to the initiatives that firms actually implement to reduce their ecological footprint, rather than the initiatives that should be taken (Bansal & Roth, 2000; Colwell & Joshi, 2013; Jiang & Bansal, 2003; Martínez-del-Río & Céspedes-Lorente, 2014). These initiatives are frequently complex and require the integration of specific information and knowledge dispersed among different units (Russo & Fouts, 1997).

A firm’s organizational structure determines how employees from different levels interact, exchange information, and participate in decision making (Fredrickson, 1986). However, the complexity of the study of organizational structure has driven this field to be “unduly neglected” (Greenwood & Miller, 2010, p. 78). To overcome this complexity, we focus on ecological responsiveness, which, although narrows the generalization of the results, might be helpful to understand the relationship between structure and organizational processes (Greenwood & Miller, 2010). The notion that a firm’s organizational structure may potentially foster or impede its ecological responsiveness has been implicit in seminal works on corporate sustainability over the last two decades (Aragón-Correa, 1998; Aragón-Correa & Sharma, 2003; Judge & Douglas, 1998; Russo & Fouts, 1997; Russo & Harrison, 2005). However, only a modest number of works have addressed this topic explicitly (but see Rivera-Torres, Garcés-Ayerbe, Scarpellini, & Valero-Gil, 2015). These studies suggest that ecological responsiveness is associated with (a) some features of the organizational structure, such as the inclusion of market-driven control practices within hierarchies (Pérez-Valls, Céspedes-Lorente, & Moreno-Garcia, 2016), the power of some functional departments within the organization (Delmas & Toffel, 2008; Walker, Ni, &, Dyck, 2015), or levels of cross-functional integration (Huang & Jim Wu, 2010); (b) the inclusion of an environmental management functional area in the overall strategy of the firm (Hahn, Pinkse, Preuss, & Figge, 2016; Russo & Harrison, 2005); or (c) changes in the organizational design (Rivera-Torres et al., 2015).

Therefore, although the extant literature suggests the existence of an association, we still lack fine-grained knowledge about the nature of the relationship between organizational structure and ecological responsiveness. Moreover, there seems to be a theoretical tension in the prior literature regarding what type of organizational structure fits better with ecological responsiveness. On one hand, ecological responsiveness aims to achieve resource efficiency and environmental risk minimization through the use of environmental management systems (EMS), written procedures, and formal evaluations (Arimura, Darnall, Ganguli, & Katayama, 2016; Heras-Saizarbitoria & Boiral, 2013). This approach seems to suggest that higher levels of formalization and functional specialization would favor ecological responsiveness. In addition, the high costs of environmental damage and the complexity of environmental issues have promoted firm centralization of relevant information and decisions in specialized departments (Aragón-Correa, Martín-Tapia, & Hurtado-Torres, 2013). On the other hand, environmental practices also require high cross-functional employee involvement and knowledge integration (Huang & Jim Wu, 2010; Russo & Fouts, 1997; Russo & Harrison, 2005) and creative problem solving and process innovation (Aragón-Correa, 1998; Aragón-Correa & Sharma, 2003), which are typically associated with free flows of information and low formalized, decentralized, and cross-functional structures.

We contend that this apparent paradox can be overcome by introducing dimensions of organizational structure and distinguishing among them. The literature emphasizes that organizational structure can be considered a multidimensional construct that is formed by a wide range of variables. Although previous studies’ approaches to the dimensions of organizational structure vary, a consensus exists regarding three key dimensions: complexity, centralization, and formalization (Chen & Huang, 2007; Fredrickson, 1986). The previous literature has shown that formalization, complexity, and centralization exert specific effects on different competitive advantages, such as innovation (Damanpour & Gopalakrishnan, 1998), exporting capabilities (Beamish, Karavis, Goerzen, & Lane, 1999), marketing (Lee, Kozlenkova, & Palmatier, 2015), and knowledge management (Pertusa-Ortega, Zaragoza-Sáez, & Claver-Cortés, 2010). These studies illustrate that the effect of formalization, complexity, and centralization is specific to a competitive advantage. The existing literature on ecological responsiveness has not sufficiently incorporated this view and has not clearly distinguished between different dimensions of organizational structure and how they affect ecological responsiveness.

In this article, we aim to fill this gap by exploring whether formalization, complexity, and centralization facilitate or impede a firm’s ecological responsiveness. Drawing on the literature on organizational structure and organizational sustainability, we contribute to clarifying the role of structure by—first—differentiating the effects of the formalization of routine and nonroutine tasks. We contend that the formalization of routine tasks—associated with operational efficiency—favors ecological responsiveness. However, nonroutine tasks—associated with innovation, stakeholder integration, and continuous improvement—exert a greater effect on ecological responsiveness when the level of formalization is low. Second, we theorize that ecological responsiveness benefits from structures characterized by hierarchical simplicity (both vertically and horizontally) because such a structure facilitates the effective use of explicit technical knowledge dispersed among functional areas and hierarchical levels. Third, we propose that decentralized structures foster a greater number of employee initiatives and higher levels of participation in decision making, which are very important for ecological responsiveness (Norton, Parker, Zacher, & Ashkanasy, 2015).

To address this question empirically, we gathered primary and secondary data from the European air passenger transport industry. The overall results show that structure plays a major role in ecological responsiveness. In particular, high formalization of routine tasks and structures characterized by high decentralization and low horizontal complexity favor ecological responsiveness. Furthermore, decentralization has a significant positive relationship with ecological responsiveness among firms with low organizational vertical complexity.

Theoretical Framework

How Can the Organizational Structure of a Firm Influence Its Ecological Responsiveness?

The organizational structure of a firm may affect its levels of ecological responsiveness for at least five reasons. First, ecological responsiveness initiatives are typically complex and multifaceted, and they often require the formal integration of technical skills and explicit knowledge from different functional areas (Russo & Fouts, 1997) such as the quality, operations, and legal departments. In a large organization, it is impossible for a single person to collect and comprehend all the relevant skills and knowledge needed to manage the company’s ecological footprint. The organizational structure may enable—or impede—the efficient transmission of information, the utilization of technical expertise, and the creation of new knowledge (Pertusa-Ortega et al., 2010) and, in turn, the extent to which the firm learns about environmental issues. When management chooses a particular structure, it provides the channels along which information and knowledge will be shared and functional areas will be interconnected and coordinated (Fredrickson, 1986). Thus, a relationship might exist between structure and the knowledge incorporated in environmental initiatives.

Second, some previous studies emphasize that environmental strategies and practices may constitute critical competitive advantages and contribute to decisively develop other strategic resources and capabilities (Aragón-Correa & Sharma, 2003; Hart, 1995; Russo & Fouts, 1997). There might be synergies and complementarities between competitive strategies creating value for the shareholders of the company and strategies and practices aiming at minimizing the environmental impact of the organization. For example, the problems of fuel consumption, waste, and emissions associated with airline operations present an opportunity for firms to lower cost and risk through the development of skills and capabilities in pollution prevention and eco-efficiency (Hart & Milstein, 2003). However, as Greenwood and Miller (2010) note, “the critical axis (resources) must be identified and then reinforced by the full range of organizational design elements” (p. 85). Developing competitive resources and capabilities requires appropriate organizational designs that nurture and reinforce a firm’s best practices and microfoundations. Therefore, developing ecological responsiveness requires an organizational design that fits, complements, and reinforces environmental initiatives (Rivera-Torres et al., 2015).

Third, there is a recent but growing stream of literature underscoring the role of employee involvement and participation in ecological responsiveness (Aragón-Correa et al., 2013; Boiral & Paillé, 2012; Jenkin, McShane, & Webster, 2011; Kim, Kim, Han, Jackson, & Ployhart, 2014; Martínez-del-Río, Céspedes-Lorente, & Carmona-Moreno, 2012; Paillé, Chen, Boiral, & Jin, 2014; Shen & Benson, 2016; Wolf, 2013). A firm’s organizational structure determines how employees with a wide range of expertise from different levels interact, exchange information, and participate in decision making (Fredrickson, 1986). Structures that facilitate delegation and the sharing of information about environmental issues may have two reinforcing effects: (a) they contribute to greater levels of employee involvement in environmental and social issues (Aragón-Correa et al., 2013; Rothenberg, Hull, & Tang, 2015), and (b) they improve the consideration of employees’ knowledge and ideas regarding complex environmental initiatives. In other words, a firm’s organizational structure may enable or obfuscate the incorporation of human capital into ecological responsiveness.

Fourth, previous studies also emphasize the importance of formal reporting relationships in the development of ecological responsiveness (Hahn et al., 2016; Henriques & Sadorsky, 1999). Formal reporting relationships determine employees’ behavior and define the information flows within an organization. Furthermore, formal reporting may give environmental issues greater weight in the corporate agenda, support smoother formal communication, and offer ideas about environmental issues greater leverage (Russo & Harrison, 2005).

Finally, organizational structure can be viewed as the context in which decisions are made and information is perceived (Hall & Saias, 1980; Mintzberg, 1990). Structure imposes “boundaries of rationality” (March & Simon, 1958), framing the way in which environmental issues and priorities are interpreted by top management. Ultimately, structure influences “how environmental issues are perceived and dealt with, i.e., it has the capacity to shape environmental strategy and its implementation and possibly even its conception” (Atkinson, Schaefer, & Viney, 2000, p. 119). For example, long-established routines and organizational systems tend to protect the status quo (Schön, 1971) and usually perceive environmental issues as threats to the established ways of working (Griffiths & Petrick, 2001). Flexible structures (i.e., low formalization, low complexity, and centralization) tend to increase managers’ ability to perceive the urgency of environmental problems and, therefore, to provide responses to environmental issues.

In the following section, we integrate findings from the literature on organizational structure and corporate environmental management to create a framework (shown in Figure 1) and formulate hypotheses describing how the dimensions of organizational structure may enable ecological responsiveness.

Figure 1.

Theoretical model.

Hypotheses

Formalization and ecological responsiveness

Formalization refers to the extent to which organizational activities are prescribed in written procedures and instructions (Claver-Cortés, Pertusa-Ortega, & Molina-Azorín, 2012; Khandwalla, 1977). While higher levels of formalization have the benefit of eliminating role ambiguity, they also limit members’ autonomy and discretion (Fredrickson, 1986).

The role of formalization in the development of ecological responsiveness appears to be unclear, and conflicting arguments seem to apply. On one hand, written procedures summarizing the organization’s best practices allow employees to more effectively address issues with environmental implications because they enable employees to access and apply specific knowledge to specific problems (P. S. Adler & Borys, 1996). EMS such as International Organization for Standardization (ISO) 14001 or European Union (EU) regulation Eco-Management and Audit Scheme (EMAS) adopt this logic and structure their approaches around written procedures, evaluations, and goal setting, which favor the integration of environmental issues in daily operations (Heras-Saizarbitoria & Boiral, 2013; López-Fernández & Serrano-Bedia, 2007). On the other hand, rules and bureaucracy may limit the creation of knowledge because they may limit the search for alternative courses of action and inhibit the interaction among organizational members and new idea creation (P. S. Adler, 1999; Von Krogh, 1998).

We contend that the traditional distinction between formalization in routine tasks and nonroutine tasks presented in seminal works on structure (P. S. Adler, 1999; P. S. Adler & Borys, 1996; Hofstede, 1978) may help to clarify this apparent conflict.

Higher levels of formalization in routine tasks are frequently associated with operational efficiency (P. S. Adler & Borys, 1996; Hofstede, 1978; Lillrank, 2003). However, lower levels of formalization in routine tasks frequently imply suboptimal operational processes and resource waste. Formalization of routines facilitates the dissemination of existing organizational knowledge among employees by means of written rules and norms (Cordón-Pozo, Garcia-Morales, & Aragon-Correa, 2006). Routines are repetitively executed, and formalization ensures that employees are not continually reinventing the wheel (P. S. Adler, 1999). Specifically, transferring knowledge related to environmental issues may be useful because addressing environmental issues typically requires technical knowledge that is not always possessed by all members of the organization. For example, a frontline employee might not know how to handle a type of environmentally damaging waste. A written procedure about how to manage this type of waste might thus anticipate possible mistakes and improve environmental outputs.

Formalization of routine tasks also eases the control of environmental activities. Establishing formal and explicit control and reporting procedures may prevent middle managers and employees from departing from behaviors aligned with organizational goals (Tosi, Katz, & Gomez-Mejia, 1997). Control and evaluation play a fundamental role in the development of environmentally responsive strategies (Henriques & Sadorsky, 1999; Russo & Harrison, 2005) because measuring environmental impact is a required step for environmental goal setting and continuous improvement. For example, an essential part of ISO 14001 is the requirement of formal planning and control of the firm’s most relevant environmental impacts (López-Fernández & Serrano-Bedia, 2007).

Thus, we propose the following hypothesis:

Hypothesis 1a: A higher level of formalization in routine tasks is positively related to the development of ecological responsiveness.

Several ecological responsiveness practices are nonroutine and idiosyncratic. Incorporating ecologically responsive initiatives requires effectively implementing new operating procedures that improve the existing ones (Aragón-Correa & Sharma, 2003; Bansal & Roth, 2000). In addition, ecological responsiveness also requires companies to adapt to shifting regulations and specific stakeholder demands. To achieve greater effectiveness with respect to these nonroutine initiatives, organizational structures that facilitate flexibility, creative problem solving, and change are more appropriate (Aragón-Correa, 1998; Russo & Fouts, 1997; Vidal-Salazar, Cordón-Pozo, & Ferrón-Vilchez, 2012).

Strict, formal rules in nonroutine tasks restrict creativity in solving nonrecurrent problems (P. S. Adler, 1999; P. S. Adler & Borys, 1996). Rules are useful for finding a solution to a structured problem or executing operations efficiently, but they usually do not provide the logic underlying this course of action (Pertusa-Ortega et al., 2010). Consequently, when managers and employees find a new and different environmental problem, the knowledge available in rules and procedures provides limited assistance.

When performing nonroutine tasks, objectives are often missing, unclear, or shifting, the level of accomplishment is more difficult to measure (or impossible to measure directly), and previous feedback information is frequently difficult to apply to current problems (Hofstede, 1978). In these situations, lower levels of formalization are more suitable because they do not limit the search for nonevident possible courses of action (Lee & Choi, 2003) and because they favor interaction and communication among organizational members (Chen & Huang, 2007; López, Peón, & Ordás, 2006).

Hypothesis 1b: A higher level of formalization in nonroutine tasks is negatively related to the development of ecological responsiveness.

Structural complexity and ecological responsiveness

The notion of structural complexity refers to the extent to which firms comprise many interrelated parts (Fredrickson, 1986; Lawrence & Lorsch, 1967). Complex structures are more appropriate for firms that compete in highly differentiated markets. However, higher levels of complexity make it more difficult to coordinate and control decision activities (Lawrence & Lorsch, 1967).

Vertical complexity refers to the number and coordination of hierarchical levels in the organization. The distribution of knowledge within the organization and the ability to transfer this knowledge internally are critical for organizations to expand and refine their environmental practices (Lenox & King, 2004). This knowledge is often widespread in relatively independent locations and organizational units. Therefore, the role of top management as collectors and subsequent providers of information related to environmental issues is of paramount importance for organizations to reach greater levels of ecological responsiveness (Lenox & King, 2004; Sharma, Pablo, & Vredenburg, 1999). For example, this information may refer to regulations or new, less-polluting technologies (Russo & Harrison, 2005). The greater the vertical simplicity of the organization is, the easier and less expensive it will be to develop and maintain routines related to environmentally relevant information collection and processing and to subsequently disseminate this information within the organization. Conversely, a greater number of loosely coordinated hierarchical levels (i.e., vertical complexity) can impede communication related to environmental decision making and the participation of the organization’s members, which is necessary to improve environmentally responsive practices (Hart, 1995).

Moreover, because information circulates more quickly when the number of levels is lower, flatter structures also favor strategic flexibility and change adaptation (Khandwalla, 1977; Quinn, 1985; Tushman & Anderson, 1997). This is relevant for the implementation of ecological responsiveness because some initiatives that companies implement to lower their environmental impact require flexibility and change adaptation, such as those related to pollution prevention, continuous improvement (e.g., EMS), and product and process redesign (Aragón-Correa, 1998; Russo & Fouts, 1997). Thus,

Hypothesis 2a: Greater levels of vertical complexity will be negatively related to the development of ecological responsiveness.

The horizontal dimension of complexity refers to the degree to which tasks are divided among departmental units or functions and to the level of cross-functional coordination and integration (Fredrickson, 1986; Pertusa-Ortega et al., 2010). Therefore, a firm has a high level of horizontal complexity when tasks are divided among multiple specialized units and when these units are loosely coordinated. By contrast, firms hold greater horizontality (i.e., horizontal simplicity) when they are divided into a limited number of units and when these units are highly integrated.

Higher levels of strategic coordination and integration among departments favor the inclusion of environmental issues in the strategic agenda (Hoffman, 2001; Judge & Douglas, 1998). Functional integration and simplicity (i.e., low horizontal complexity) increase the cohesiveness of the organizational behavior and the levels of information sharing. However, functional departments may differ in their exposure to external pressures toward environmental conformity. Greater integration may facilitate the diffusion of these pressures within the organization. For example, Delmas and Toffel (2008) found that the extent to which the corporate marketing and legal affairs departments influenced organizational decisions significantly affected top management’s perceptions about environmental pressures coming from the institutional framework. For example, in European aviation companies, legal affairs departments were the first to perceive the implications of EU directive 2008/101/EC, which includes aviation activities in the greenhouse gas emission allowance trading. As the integration of this department with other functional areas increased, the ability of companies to convey the importance of this regulatory change and to include CO₂ emissions in their environmental approaches increased.

Environmental issues are frequently complex and multifaceted. Therefore, they often require a combination of specific technical knowledge dispersed among different units (Russo & Fouts, 1997). Environmental decision making improves when quality managers, engineers, and production personnel effectively provide relevant information (Russo & Harrison, 2005). Consequently, managers make more-informed decisions with respect to environmental issues when (a) cross-functional information flows are abundant, and (b) when specific knowledge is laterally integrated (Hart, 1995; Sharma et al., 1999), that is, in firms with lower horizontal complexity.

In addition, achieving greater levels of ecological responsiveness is a human-capital-intensive process (del Brío & Junquera, 2003; Hart, 1995). Lateral coordination and integration play a role not only at a formal level but also at an informal level (Russo & Harrison, 2005). Increased informal communication between engineers and employees with environmentally relevant technical knowledge creates iterative problem-solving systems (Boiral, 2002; King, 1999) that also contribute to helping managers make more-informed decisions about environmental issues (Sharma et al., 1999). Horizontally simple and highly coordinated structures favor informal communication among employees and the feeling of being “on the same page.” However, the division of a company into multiple functional areas loosely coordinated with few formal and informal communication channels increases the likelihood of conflicts between functional areas and strategic dissonance.

For example, an aviation company might consider reducing the amount of aircraft surface painted to reduce maintenance costs and the weight of the aircraft (oil spending and CO₂ emissions). However, this practice might entail trade-offs with the firm’s marketing function. Increased coordination and a shared vision between the marketing, maintenance, and environmental personnel of the firm might ease the assumption of this trade-off.

Hypothesis 2b: Greater levels of horizontal complexity are negatively related to the development of ecological responsiveness.

Centralization and ecological responsiveness

The centralization-decentralization dimension refers to the degree of dissemination in decision making and control. A structure is centralized when the right to make decisions and evaluate activities is concentrated in one or very few individuals. By contrast, a decentralized structure occurs when decision making is shared by several individuals (Fredrickson, 1986; Mintzberg, 1979).

Highly centralized structures place significant cognitive demands on those managers who retain authority. Therefore, such structures are more susceptible to decision makers’ cognitive limitations. By contrast, decentralized structures accommodate possible knowledge limitations by sharing decision process responsibilities (Fredrickson, 1986). Therefore, decentralization facilitates the exploitation of firstline and middle managers’ technical knowledge (Baum & Wally, 2003), which is important because ecological responsiveness typically demands high levels of employee technical knowledge and participation (Hart, 1995). Numerous practices that are often linked to ecological responsiveness strategies, such as the implementation of an EMS, the redesign of environmental processes, or the search for technical solutions to environmental problems, are typically complex and require individuals with command of varied technical skills (Martínez-del-Río et al., 2012).

Moreover, centralization frequently entails bureaucratic processes and may delay a particular initiative until the stimulus is perceived and interpreted by top management. Managers often take significant time to perceive and address environmental initiatives because they often consider environmental issues to be too scientific, complex, and encrypted in technical knowledge and, therefore, too difficult to understand (Anderson & Bateman, 2000; Shrivastava, 1995). Thus, decentralization allows not only more knowledgeable but also quicker responses to contingencies that may result in pollution minimization. For example, in the aviation industry, profound technical knowledge is required to decide whether and how a particular route should be shortened or enlarged to reduce fuel expenditure. Pilots and firstline managers are more likely to be able to make these kinds of real-time decisions effectively.

Decentralization implies a greater number of individuals in the process of strategic reflection (Hall & Saias, 1980; Pertusa-Ortega et al., 2010) and greater levels of personal involvement in corporate initiatives (Chen & Huang, 2007). This finding is consistent with the previous literature on organizations and the natural environment, which emphasizes that environmental initiatives require employees’ involvement and commitment (Kim et al., 2014; Norton et al., 2015). Middle and frontline managers are usually closer to the environmental impact and/or source of environmental pollution, which makes them more aware of the environmental consequences of the firm’s operations (Atkinson et al., 2000). Consequently, ecological responsiveness finds a better fit with structures that allow middle managers and employees to play an active role and provide a source of initiative in the configuration of the overall corporate strategy.

Conversely, in highly centralized structures, relevant initiatives will more likely be initiated only by the dominant few (Fredrickson, 1986), and levels of social interaction and interpersonal exchange are lower (Chen & Huang, 2007). Consequently, with such a structure, environmental problems and opportunities are more likely to go unrecognized and ignored until they become apparent to the top management. In addition, the existence of longer and time-consuming bureaucratic communication channels along the chain of command inhibits idea circulation and the suggestion of new environmental initiatives by employees (Child & McGrath, 2001). Therefore, the following hypothesis is proposed:

Hypothesis 3: Greater levels of operational decentralization (i.e., lower centralization) are positively related to the development of ecological responsiveness.

We contend that the effect of complexity and centralization on ecological responsiveness is more intricate than the previously hypothesized direct effects. More specifically, we posit that these effects are interrelated so that higher levels of complexity may obfuscate the positive effect of decentralization.

Decision making might be widespread among a great number of people, but the positive effect of decentralization on ecological responsiveness could be lost if these people are poorly coordinated and dispersed among a large number of loosely coordinated hierarchical levels (i.e., vertical complexity) or functional areas (i.e., horizontal complexity). When decision making is decentralized in middle managers and frontline employees, environmental initiatives will be favored if the organization is vertically and horizontally simple because such a structure ensures that knowledge and information are integrated and coordinated not only at the top, but throughout the organization (Chen & Huang, 2007). These circumstances are favorable for the generation of new ideas, knowledge, and skills. In other words, decentralized and simple organizations tend to place both decision making and integrated knowledge in the hands of employees and middle-level managers.

However, even if the organization is decentralized and employee decision making is enhanced, environmental initiatives and decisions—which are typically complex and multifaceted—will not be informed to the same extent by technical and integrated knowledge coming from other functional areas or levels of the organization. These decisions will be informed only by the circumstantial knowledge at hand in the specific functional area or hierarchical level in which the decisions are being made. Under these circumstances, it is highly probable that narrower, disparate, and uncoordinated environmental initiatives and decisions take place, causing a weaker and less effective approach to ecological responsiveness. Thus, we posit the following:

Hypothesis 4a: Vertical complexity negatively moderates the relationship between decentralization (i.e., lower centralization) and ecological responsiveness so that the positive effect of decentralization on ecological responsiveness will be greater with lower levels of vertical complexity.

Hypothesis 4b: Horizontal complexity negatively moderates the relationship between decentralization (i.e., lower centralization) and ecological responsiveness so that the positive effect of decentralization on ecological responsiveness will be greater with lower levels of horizontal complexity.

Method

Sample

To test the abovementioned hypotheses, we developed a sample of companies belonging to the airline industry in Europe. Aviation is an essential sector in Europe, and it provides a significant contribution to the EU’s overall economy and employment. It generates more than 5.1 million jobs and adds €365 billion, or 2.4%, to the European GDP (International Air Transport Association [IATA], 2014). However, transport in general and aviation in particular are also a source of pollution. They involve major infrastructure requirements and are fundamentally reliant on fossil fuels. The main environmental effects of aviation are those of aircraft noise and emissions. The former largely affects areas and communities at and around airports, while the latter can have both local effects on air quality and global effects on climate.

We focus on the airline industry for a variety of reasons. First, the airline industry is considered one of the most dynamic and competitive sectors (N. Adler & Gellman, 2012), promoting the emergence of organizational innovations (Galunic & Eisenhardt, 2001). Second, aviation has a significant environmental impact in terms of CO₂ emissions—which have a relevant effect on climate change—as well as noise, land use, and other pollutant emissions (Hooper & Greenall, 2002). Moreover, aviation emissions have grown rapidly over recent decades (by 42% during the 1990-2005 period; Rhoades, 2014). Third, the organizational structure of the companies in this sector is also relevant because companies show a high level of growth and change, require substantial investments in infrastructure and operations, and frequently are large in size. Finally, companies in this industry operate using a variety of business models, strategies, and organizational designs that provide sufficient variability in our independent variables. For example, our sample includes low cost, regional, and flag carrier companies. Thus, the air transport industry has frequently been used in organizational research (Ashkenas, Ulrich, Jick, & Kerr, 2015; Jensen & Meckling, 1992; Kelly & Amburgey, 1991)

The sample comprised 578 firms located in the 27 EU countries and Norway and Switzerland. We combined objective data with self-reported data on the dependent variable of our study (i.e., ecological responsiveness) while also using self-reported data on the independent variables included in the study. The survey was sent to two key respondents from each organization (the CEO, the chief operating officer [COO], or other equivalent operations executive, which might have a different title depending on the organization). These individuals were chosen because of their position inside the organization and their knowledge on the main topics of this study. In 12 out of the 109 cases we tracked, the survey was completed by a different person designated by our targeted executive. In all these cases, the respondents had managerial responsibilities, ensuring the validity of their responses. To increase the response rate, firms were contacted by not only email but also telephone. During each telephone call, the respondent’s identity was confirmed, and we indicated the study’s main goals. The data collection process was carried out in three mailing waves between September 2009 and June 2010. Once we received a valid questionnaire from either of the two possible respondents, we stopped sending additional mailings to that company. We eventually obtained 109 valid surveys, which implies a response rate of 18.9%. Although this rate is low, it is above the 10% to 12% rate usually obtained for studies interviewing American top executives (Hambrick, Geletkanycz, & Fredrickson, 1993). An analysis was performed to verify the existence of relevant differences between those organizations that answered the survey and those that did not. We obtained data on the number of employees, total assets, and return on assets (ROA) from the Amadeus database. The t tests revealed no significant differences (none of them was significant at a 5% level) in the mean number of employees (t = 1.584), the mean total assets (t = −0.649), and the mean ROA (t = −0.788) between the respondents and the nonrespondents.

We also conducted tests to identify differences between responses in the first and third mailing waves, and we found no significant differences with regard to the variables’ means between the waves. For example, Environmental Certification (t = 0.256), Environmental Impact Measurement (t = 0.420), Number of Employees (t = −1.215), Assets (t = −1.249), Number of Hierarchical Levels (t = 0.513), and Horizontal Connections (t = 0.583). None of them was significant at 5%. A full report of the results is available upon request.

The database was complemented with objective information from the Amadeus database, which provided information about the number of employees, the amount of assets, and the financial performance of the firms studied. We also codified information from the airlines’ annual reports, including financial, strategic, and environmental information (when available).

The air passenger company data used in this study presented problems in terms of interpreting similar scores for different business models (i.e., (a) flag carriers, (b) regional carriers, (c) low cost companies, and (d) charter and general aviation companies). We standardized the data in our sample using the average and standard deviation of companies with similar business models to overcome these difficulties and avoid biases caused by a different average situation for companies with the same business model. In this manner, the standardized scores were more comparable among firms with different business models.

In doing so, we also account for the effect of different regulatory pressures. Aviation in Europe is characterized by a largely homogeneous regulatory environment, with few variations across European nations. Thus, if there are any differences in regulatory pressures, they arise because of the characteristics of the airlines’ operations (e.g., long haul and transoceanic flights might have different regulations than short haul or domestic flights). By normalizing the data based on the business model, we account for that possible effect in our analysis.

Measures

Dependent variables

Prior to the development of the empirical part of this research, we conducted in-depth interviews with two managers to obtain qualitative information on our hypotheses and to pretest the questionnaire. Feedback from these executives, along with comments and suggestions from two industry experts and several colleagues knowledgeable in survey design, were incorporated into a revised version of the survey instrument.

Ecological responsiveness is a set of initiatives or best practices that aim to mitigate a firm’s overall impact on the natural environment (Bansal & Roth, 2000; Hart, 1995). These initiatives may include improvements in products and processes to reduce pollution and waste, using resources more sustainably and implementing an EMS. We used a multi-item scale to appropriately address the extent to which firms introduced initiatives to reduce their environmental impact.

First, to assess the level of implementation of more efficient technologies in air transport services to reduce energy consumption and waste generation, we took into account airplanes’ average age. We gathered this information from the companies’ annual reports. Usually, new airplanes include new technologies that respond to a tendency toward environmental awareness, and they typically have more efficient fuel consumption. Therefore, the average fleet age reflects the level of fossil fuel consumption and other ecological impacts (e.g., noise) of the airline. The selection of one airplane model (e.g., new) or another (e.g., used) typically affects several functional areas of the company, such as top management, operations and supply chain management, and legal.

Second, we asked the managers about the extent to which the companies gathered information about the environmental impact of their operations based on a set of environmental impacts proposed by the IATA (Hooper & Greenall, 2002). These impacts are in line with those proposed by the OECD’s Environment Directorate in 2003, and they include the use of energy and natural resources, wastewater effluent, solid waste, local air pollution, global pollutants (e.g., greenhouse gases), and aesthetic effects (including noise, smell, and landscape). This variable was finally coded as the number of areas in which firms were gathering information about their impact and thus ranged from 0 to 7.

Finally, we asked airlines whether they publish any type of environmental report and whether they hold any type of EMS certification. These answers were coded using two dichotomous variables. Table 1 provides details on the measurement instruments used in the study.

Table 1.

Measurement Instruments.

Construct/ variable	No. of items	Cronbach’s alpha	Composite reliability	β	Description
Environmental responsiveness	4	.761	.846	.823***	Which of the following environmental performance measures does your company regularly monitor? ΣNo. of areas in which the firm answers affirmatively:
					Use of natural resources (energy, fuel, etc.)
					Solid waste generation
					Wastewater effluent
					Local or regional air pollution
					Global pollutants (e.g., greenhouses gases)
					Aesthetic effects (noise, smell, landscapes)
					Other environmental performance measures (please specify)
				.711***	Fleet’s average age
				.683***	Does your organization elaborate any kind of environmental report? (Y/N)
				.819***	Does your organization hold any environmental certification (e.g., ISO 14001, EMAS)? (Y/N)
Formalization of routine tasks	2	.686	.861		Consider the difference between routine and nonroutine tasks in your firm. Routine tasks repeat continuously, and nonroutine tasks are unusual. Please indicate the level of agreement with the following sentences (1 = I do not agree at all, 5 = I completely agree):Source: Authors’ elaboration based on Baum and Wally (2003)
				.915***	Our SOP manual helps us address routine problems
				.822***	Our firm has highly formalized channels of communication for routine process and practices
Formalization of nonroutine tasks^a	2	.685	.842		Consider the difference between routine and nonroutine tasks in your firm. Routine tasks repeat continuously, and nonroutine tasks are unusual. Please indicate the level of agreement with the following sentences (1 = I do not agree at all, 5 = I completely agree):Source: Authors’ elaboration based on Baum and Wally (2003)
				.729***	I can get the information that I need when I face unusual problems without going through channels
				.964***	There are no written instructions to carry out nonroutine tasks
Vertical complexity^a	1	—	—	—	Source: Pettigrew, Massini, and Numagami (2000) and Whittington, Pettigrew, Peck, Fenton, and Conyon (1999)
Vertical complexity^a	1	—	—	—	How many organizational levels are there between the manager with the lowest level of responsibility and the manager with the highest level? (Please consider the longest hierarchical line). Absolute value weighted according to organization’s size
Horizontal complexity^a	4	.927	.949		Degree of linkage among subunits/departments with the purpose of (5-point scale—1 = very low, 5 = very high):Source: Pettigrew et al. (2000) and Whittington et al. (1999)
				.997***	Providing a service
				.846***	Sharing information
				.881***	Coordinating joint purchasing
				.902***	Sharing commercial information
Operational decentralization	3	.546	.746		Please indicate the level of agreement with the following sentences (5-point scale—1 = I do not agree at all, 5 = I completely agree):Source: Authors’ elaboration based on Baum and Wally (2003)
				.706***	Our frontline employees would say that they are free to change things to provide a better service to customers
				.755***	People involved in strategic planning make day-to-day decisions about frontline operations^a
				.647***	We give frontline employees freedom to make operational decisions about service and customer-oriented problems
Controls
Ecological values	1	—	—	—	Source: European Values Study (EVS; 2011). Do you belong to associations or movements related to conservation, the environment, ecology, or animal rights?
ROA	1	—	—	—	Source: Amadeus databaseReturn on assets
Age	1	—	—	—	Source: Amadeus databaseNumber of years since foundation
Alliance	1	—	—	—	Source: Author elaborationOrganization belongs to an alliance
Size	2	.903	.952		Source: Amadeus database
				.937***	Number of employees
				.969***	Total assets

Note. EMAS = Eco-Management and Audit Scheme; SOP = standard operating procedures; ROA = return on assets.

Reverse coded.

***

Significant at .001.

We conducted an exploratory factor analysis with the four items in our measure. We obtained only one component with an eigenvalue higher than 1.0 that explained 59% of the variance. Then, we conducted a confirmatory factor analysis (CFA) to assess the convergent validity of the measure. The results of the CFA indicated a good fit (comparative fit index [CFI] = 0.985; root mean square error of approximation [RMSEA] = 0.101; Hu & Bentler 1999), and the loads for all of the indicators were significant (p > .01).

Independent variables

Complexity

Organizational complexity refers to the division of work into tasks and responsibilities. Under the term “complexity,” concepts such as specialization or departmentalization, hierarchical levels, and span of control are analyzed (Hage & Aiken, 1967; Khandwalla, 1977). Complexity refers then to the degree of differentiation that exists within one organization (Robbins, 1990), which might be reflected in the number of and connections between organizational units (horizontal complexity) and in the number of responsibility levels (vertical complexity).

To capture vertical complexity, which refers to the number of organizational layers, we introduced a variable called FLAT, which captures the number of hierarchical levels. This variable was balanced by firm size to facilitate comparability and reverse coded so that a higher value in this item meant an organization with fewer organizational levels.

The horizontal dimension of complexity refers to the degree to which tasks are divided among departmental units or functions and to the level of cross-functional coordination and integration (Fredrickson, 1986; Pertusa-Ortega et al., 2010). We captured this dimension using a variable measuring the intensity presented in horizontal connections among different units or departments (Horizontal Complexity). This second-order construct was finally depicted using four indicators for the degree of linkage among subunits/departments with different purposes (one item for each purpose; please see Table 1 for a full description). We inversely coded these variables so that a higher value in this case meant a simpler and less horizontally differentiated organization.

Decentralization

This variable refers to the place or level of decision making in an organization. The centralization and decentralization processes have been studied by classical authors in organizational structure works (Child, 1974; Hage & Aiken, 1969; Mintzberg, 1979; Van de Ven, 1976). The measures applied in this study have been adapted to the decentralized operation measures presented by Baum and Wally (2003), who made their proposal by building upon Khandwalla’s (1977) seminal work. In this case, decentralization was estimated by using three items related to the freedom of frontline employees to make decisions on operation-related issues and the distance to those kinds of decisions kept by people involved in strategic planning.

Formalization

Formalization is also a traditional variable in measures of structural characteristics (Khandwalla, 1977; Pugh, Hickson, Hinings, & Turner, 1968). It shows the degree to which formal rules, policies, and standards are used in decision-making processes and working relationships (Fredrickson, 1986). Following the same scheme used for the previous variable, in this case, the scale proposed by Baum and Wally (2003) was preferred.

In addition, we considered the importance that the distinction between routine and nonroutine tasks implies for the management of resources and information flows to which formalization refers. We consider routine tasks as those that repeat continuously, and nonroutine tasks are those that happen unusually. To capture this distinction, two items were included to depict the formalization of both routine and nonroutine tasks. These items referred to the existence of operating manuals or other clear written instructions to perform tasks. Please refer to Table 1 for a full description of the items. The questions addressing the formalization of nonroutine tasks were designed in an inverse manner to avoid possible respondent bias; thus, we reverse coded them so that a higher value in these items represents a higher degree of formalization.

Control variables

Size

To account for possible alternative explanations, we controlled for firm size. Size was measured using a latent variable with two different indicators, namely, the number of employees and total assets at the moment of data collection, as reported in the Amadeus database.

Firm’s age

Firms’ organizational structure might evolve over time to become more hierarchical, complex, and centralized as organizations grow older. We compiled the number of years since the company was founded from a secondary data source (the Amadeus database).

Airline alliance

To control for the extent to which external normative or industry influences may account for alternative explanations of the results obtained, we introduced the variable airline alliance (dichotomous, 1 = yes, 0 = no) in the model.

Performance

Economic performance was measured by using firms’ ROA from the same year in which the data were gathered. We also compiled this information from the Amadeus database.

Ecological values

To control for possible institutional effects, we introduced a variable capturing societal values regarding ecology and social activism aimed at environment protection. This information was extracted from the European Values Survey 2008 (the closest available), which provides country-specific information on the moral, societal, political, work, and family values of Europeans. Although these data are from 1 year prior to the data collection, social values are not expected to dramatically change in such a short period of time. To introduce the relevance of ecological values in our study, we used the degree of social activism on environmental issues of the citizens of the home country of each airline. This dimension was captured using a variable measuring the proportion of the population of each country that was member or belonged to associations or movements related to conservation, the environment, ecology, or animal rights.

Method and Results

Data analysis was constructed using partial least squares (PLS), a modeling technique based on structural equations that use a system of estimation grounded on principal components (Chin, 1998). The software used was SmartPls 2.0 M3 (Ringle, Wende, & Will, 2005).

The use of PLS has certain advantages (Barroso, Cepeda, & Roldán, 2010): (a) It does not suffer from indeterminacy problems like other causal structural equation modeling techniques using programs such as EQS or LISREL; (b) it is a nonparametric technique and, therefore, does not assume normality of the data; (c) it does not require sample sizes as large as those in other causal modeling techniques; (d) it often allows researchers to work with more complex models than those in other causal modeling techniques; and (e) it allows work with any type of variable (ordinal, categorical, or dichotomous variables; Falk & Miller, 1992; Fornell & Bookstein, 1982).

With the aim of verifying the hypothesis, PLS offers diverse alternatives, although Chin (1998) recommends the process termed “bootstrapping.” Following Barclay, Higgins, and Thompson (1995), we analyzed and interpreted the results sequentially in two stages: (a) we assessed the reliability and validity of the measurement model, and (b) we assessed the structural model.

Discriminant Validity and Reliability

Common practice recommends an assessment of the model through an examination of the correlations between constructs, constructs and items; Cronbach’s alpha for different scales; and the composite reliability and the average variance extracted (AVE) for each construct (Carrion, 2006). In PLS, reflective indicators are determined by the constructs; thus, they are covariates of the level of the aforementioned construct (Hulland, 1999). In our model, all the applied scales make use of reflective items.

We assessed items’ individual reliability through PLS by examining measures’ charges on their respective construct. In the final model, all initial items have been kept because of the potential loss of information, as their removal did not justify the model’s improvement in terms of reliability. Construct reliability was evaluated by applying two internal consistency measures: Cronbach’s alpha and composite reliability. Interpretation of both values is similar, although composite reliability is a more accurate measure, as it does not assume an equal distribution of items’ weight (Barclay et al., 1995). Nunnally (1978) suggested .70 as the level of comparative basis for “modest” reliability, attributable to research development’s initial stages, and a .80 as a more “strict” reliability level, applicable to basic research. As Table 1 shows, although Cronbach’s alpha does not reach the established limit in some cases, composite reliability surpasses the .70 level in all cases.

Discriminant validity represents the extent to which measures of a specific construct differ from those of other constructs included in the same model. Discriminant validity has been assessed by two different means (Chin, 1998). First, we compared the AVE’s square root (shown in the diagonal of Table 2) with the correlations between different constructs (represented by the data among the variables’ intersections). Given that the AVE is an indicator of the variance obtained from the construct in relation to the total variance of the measure’s error, the AVE value must exceed the 0.5 level (Barclay et al., 1995). Table 2 shows that AVE’s square root is higher than the correlation between the constructs, which suggests that, on average, each construct is increasingly more related to its measure than to the other constructs offered in the model.

Table 2.

Intercorrelations.

Multi-item constructs	ECO_RESP	FORM_RUT	FORM_NON_RUT	HORIZ	DEC
ECO_RESP	.762
FORM_RUT	.200	.870
FORM_NON_RUT	.185	−.219	.855
HORIZ	.386	.101	.289	.908
DEC	.372	.088	.414	.370	.704

Note. ECO_RESP = ecological responsiveness; FORM_RUT = formalization of routine tasks; FORM_NON_RUT = formalization of nonroutine tasks; HORIZ = horizontal complexity; DEC = decentralization.

The second method used to assess discriminant validity consists of an evaluation of how each item is related to the constructs included in the model. For this purpose, we examined items’ weight on their constructs and on the cross loadings of the rest of the model’s constructs. All items’ weights proved to be higher for their own constructs than for other constructs. Moreover, all constructs shared more variance with their constructs than with others; thus, we can conclude that these results collectively provide support for the convergent and discriminant validity of the scales applied.

Structural Model

The main purpose of PLS is to minimize errors or, equivalently, the explained variance in all endogenous constructs. The extent to which a particular PLS model accomplishes this objective can be determined by examining the R² levels of endogenous constructs. In addition, relationships between variables can be assessed based on coefficients’ values and statistical worth.

We estimated three models. Model 1, the base model, included all of the main effects. Model 2 included all of the main effects and the vertical complexity—decentralization interaction term. Model 3 included all of the main effects and the horizontal complexity—decentralization interaction term. The path coefficient corresponding to this interaction term was found to be nonsignificant, and thus, this model was not considered in the subsequent analysis. Figure 2 shows Model 2. The sign and significance of the path coefficients were similar in Models 1 and 2.

Figure 2.

Structural model.

The results obtained indicate the competence of this model’s predictive capability. The dependent variable’s R² value surpasses the level of .1 proposed by Falk and Miller (1992). Wold (1982) suggested the general use of the Stone-Geisser test applied to the assessment of a construct’s predictive relevance. This test’s application follows a “blindfolding” process that enables the construction of a Q² indicator (1-SSE-SSO). The interpretation of this value takes 0 as a reference level. In this way, the model has a predictive value when the indicator is positive (Sellin, 1995). The results derived from the analysis of the model present a Q² indicator’s value for the endogenous variable of 0.174, which indicates the proposed model’s predictive viability.

The second part of the comments regarding the structural model’s assessment analyzes the predictive intensity of the endogenous variable grounded on the exogenous variables. Evaluating the model relationships’ direction and significance allows us to contrast the formulated hypotheses.

When we addressed the effect of formalization, we distinguished between the effect of formalization on routine (Hypothesis 1a) and nonroutine (Hypothesis 1b) tasks. We proposed that formalization would favor ecological responsiveness in the first case, but not in the second case. We found support for Hypothesis 1a in our model (β = .19, p < .01). For Hypothesis 1b, the link representing the relationship between the formalization of nonroutine tasks and ecological responsiveness was found to be nonsignificant (β = .06, p > .1). Therefore, we did not find support for Hypothesis 1b in our data.

Hypothesis 2a suggested a direct and negative relationship between vertical complexity and ecological responsiveness. We inversely coded the items measuring complexity to show a positive value when the levels of complexity were at the target and a negative value when the levels of complexity were high. The results showed that the relationship between vertical complexity (the number of hierarchical levels, inverted) and ecological responsiveness was not significant (β = .07, p > .1). Therefore, we found no support for Hypothesis 2a. However, Hypothesis 2b posed that greater levels of horizontal complexity are negatively related to the development of ecological responsiveness. In our model, the link between the horizontal complexity variable, which takes higher values for lower horizontal complexity, and ecological responsiveness was found to be significant (β = .31, p < .001). Consequently, Hypothesis 2b is supported by our data.

Hypothesis 3 proposed that a higher level of operational decentralization was directly and positively linked with the development of a higher degree of ecological responsiveness. The path of this relationship in our model was found to be significant (β = .18, p < .05). Thus, we found support for Hypothesis 3 in our data.

Hypotheses 4a and 4b posit that the levels of vertical and horizontal complexity moderate the relationship between organizational structure decentralization and a firm’s ecological responsiveness. The moderating effects were tested as part of the structural model. Two moderating variables were created by cross-multiplying the standardized items for each construct, the interaction term between vertical complexity and decentralization, and the interaction term between horizontal complexity and decentralization. We estimated two structural models including the main effects and each one of these interaction terms. The moderating effect between vertical complexity and decentralization was significant (β = .23, p < .05, ΔR² = 4.7%), supporting Hypothesis 4a. The moderating effect between horizontal complexity and decentralization was not significant (β = .07, p > .10). Consequently, Hypothesis 4b is not supported by our data.

To analyze the nature of the moderating role of vertical complexity between decentralization and ecological responsiveness, the effects of decentralization on the dependent variable for values of vertical integration one standard deviation above and below the mean were plotted, as suggested by Cohen and Cohen (1983). The path coefficients of the structural model shown in Figure 2 were used to quantify these effects. Figure 3 shows this plot. It reveals that decentralization had a significant positive relationship with ecological responsiveness among firms with low organizational vertical complexity (few organizational levels between the manager with the lowest level of responsibility and the manager with the highest level, weighted by the organization’s size). Among firms with high vertical complexity, decentralization was unrelated to firm environmental responsiveness.

Figure 3.

Moderating effect of vertical complexity on the relationship between decentralization and ecological responsiveness.

Discussion of Results and Conclusion

This study draws on the current organizational structure and ecological responsiveness literatures to examine how the dimensions of a firm’s organizational structure (i.e., formalization, complexity, and centralization) affect its ecological responsiveness. Unveiling the effect of structural antecedents of ecological responsiveness is important because the organizational structure constitutes the framework in which managerial and employees’ knowledge and participation in environmental issues are enhanced or impeded (e.g., Boiral, 2002; Martínez-del-Río et al., 2012; Russo & Harrison, 2005).

We opened the article by addressing the paradox existing in the extant literature regarding the most suitable organizational structure for ecological responsiveness. The relevance of formal procedures, formal reporting, and the complexity of environmental threads (Jiang & Bansal, 2003; Russo & Fouts, 1997) may drive one to the conclusion that higher levels of formalization and functional specialization favor ecological responsiveness. However, it could also be suggested that ecological responsiveness benefits from free flows of information and low levels of formalized, decentralized, and cross-functional structures because these favor employee involvement, knowledge integration, creative problem solving and process innovation (Aguilera-Caracuel & Ortiz-de-Mandojana, 2013; Aragón-Correa & Sharma, 2003; Russo & Harrison, 2005).

We have clarified this paradox by introducing the dimensions of organizational structure and distinguishing their differentiated effects on ecological responsiveness. Using data from the passenger aviation industry, we found general evidence that a firm’s organizational structure affects its level of ecological responsiveness. However, the magnitude and sign of the effect depends on the specific dimension of the organizational structure. Specifically, we found that the formalization of routine tasks, horizontal simplicity, and decentralization enable ecological responsiveness although we could not find significant support for a negative effect of the formalization of nonroutine tasks and vertical complexity on ecological responsiveness.

In addition, the study of the interaction of vertical and horizontal complexity with decentralization allowed us to refine our analysis of these effects. First, we looked at the moderation effect of vertical complexity on decentralization. The results show that the flatter the structure (i.e., lower vertical complexity) is, the greater the effect is of decentralization on ecological responsiveness. Interestingly, we found that when organizations are very tall (i.e., vertically complex), the effect of decentralization is very limited. However, when organizations are flat (i.e., vertically simple), the effect of decentralization is particularly significant. Finally, we found no support for the proposed moderating effect of horizontal complexity in the relationship between decentralization and ecological responsiveness.

Therefore, ecological responsiveness may not be achieved by simply running away from the traditional bureaucratic approach to a different organizational structure, but rather by acknowledging the complexities among the differentiated effects of the dimensions of organizational structure and embracing the possible interactions and tensions among them. In this sense, this article adds to the growing literature suggesting that challenging environmental issues may be effectively addressed through paradoxical approaches and the acceptance rather than the denial of complexities and tensions (e.g., Hahn et al., 2016).

As one of its contributions, this work provides theoretical reasoning for the notion that the formalization of routine tasks is beneficial, while the formalization of nonroutine tasks is detrimental to ecological responsiveness. Some environmental practices such as formal goal setting, written procedures to minimize waste and increase operational efficiency, or environmental training are eminently formal. We found support in our data that greater formalization of these tasks favors ecological responsiveness. In addition, some other environmental practices related to pollution prevention, environmental leadership, and continuous improvement have been shown to be related to innovation (Aragón-Correa, 1998). Accordingly, we expected that those structures that favor innovation also favor ecologically responsive practices. However, we did not find support for Hypothesis 1b in our results. This result might be attributed to the routine and reactive character of most ecological responsiveness practices, such as EMS or environmental damages tracking and reporting. It could be the case that more proactive approaches to environmental management might be more dependent of nonroutine tasks and, therefore, the nonformalization of nonroutine tasks might be more significant in those cases.

This study also contributes to the recent but growing literature emphasizing the role of employee involvement and participation in ecological responsiveness and corporate social responsibility (CSR) initiatives (Aragón-Correa et al., 2013; Boiral & Paillé, 2012; Chen, Tang, Jin, Li, & Paillé, 2015; Paillé et al., 2014; Wolf, 2013). First, our article provides additional evidence that employee information sharing and coordination favors the implementation of ecological practices (Anderson & Bateman, 2000; Aragón-Correa et al., 2013; Ramus & Steger, 2000). In addition, our article advances the current knowledge by pointing to the catalyst role of organizational structure in engaging employees on environmental issues. This work contributes by revealing how the organizational structure may give relevance to employees with regard to ecological responsiveness. Specifically, decentralized and (vertically and horizontally) simple organizations are more likely to achieve greater levels of employee participation, which might lead to higher levels of ecological responsiveness. In decentralized organizations, decisions are made at lower levels of the hierarchical chain, which must be paired with a good degree of integration and communication between the different levels of the organization to assure the correct flow of environmental information. If decentralization is increased in a vertically complex organization, then decisions can hardly be made based on the knowledge base generated by the employees, which in turn will negatively affect ecological responsiveness. Overall, our article suggests a novel approach to this research stream that studies the organizational conditions that facilitate employee participation in environmental issues.

Limitations and Extensions

Implications previously exposed must be interpreted considering the limitations of this study. First, our study has been conducted in the context of the European airline industry. Consequently, the potential generalization of the results to other industries should be considered with taking the organizational background into account—for instance, organizational structures tend to be in accordance with contextual variables (Walker et al., 2015). However, the variety of business models and external contexts (i.e., countries) present in the sample provide some reasonable basis for generalization.

Second, the dependent variable in this study was composed of items derived from secondary sources (i.e., fleet average age) and self-reported data obtained from the same questionnaire as the independent variables. Although the psychographic features (Cronbach’s alpha, internal correlation of items, and charges of measured items on the latent variable) of the measure are good and we checked for common method variance problems, we cannot completely rule out common method variance from the data.

In addition, reverse causality cannot be conclusively ruled out because the relationships between the different dimensions of organizational structure and ecological responsiveness are based on cross-sectional data. However, the direction proposed is in line with previous findings in the extant literature that generally support the notion that structure has the potential to foster or impede ecological responsiveness rather than ecological responsiveness preceding organizational structure (Hahn et al., 2016). Nonetheless, future research based on longitudinal designs would be valuable to further assess causality.

Seminal studies on organizational design (Child, 1974; Mintzberg, 1979) emphasize that external contingencies (i.e., complexity, uncertainty, and munificence) moderate the relationship between the dimensions of organizational structure and firm performance. In addition, some previous works on ecological responsiveness also address the effect of these contingencies as moderators of the internal antecedents of ecological responsiveness (Aragón-Correa & Sharma, 2003). In that sense, an opportunity for further research consists of the study of external contingencies (i.e., complexity, uncertainty, and munificence) as moderators of the effects of formalization, centralization, and complexity on ecological responsiveness. Moreover, because some relevant previous studies address the suitability of a functional area that is specifically devoted to environmental management (Hahn et al., 2016; Russo & Harrison, 2005), it would also be interesting to explore whether the existence of such a functional area constitutes an internal contingency that affects (moderates) the relationships developed here.

Finally, this work contributes to the extension of previous studies revealing how structure may give relevance to employees with regard to ecological responsiveness. Recent works emphasize the role of structural configurations in the environmental strategy of the firm (Walker et al., 2015). Future research may explore whether specific combinations of organizational variables, rather than isolated structural dimensions, favor the development of ecological responses. A potential outcome of this research endeavor would be to identify and specific configurations of structural dimensions that favor the environmental strategy of the firm and to test—depending on external and internal circumstances—which configuration is better than others (and to what extent). Nevertheless, we believe that having a clear notion of the direct effect and relevance of key organizational dimensions is a necessary step that precedes the exploration of these combinations. We hope that our work presents this notion and serves as a basis to develop further research in this stream.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors acknowledge financial support from the Spanish Ministry of Economics and Competitiveness and Fondo Europeo de Desarrollo Regional, FEDER (Project ECO2015-66504P) and Junta de Andalucia (Project P10-SEJ-05827).

Author Biographies

Miguel Pérez-Valls (PhD, University of Almería, Spain) is assistant professor at the University of Almeria. His research interests are particularly concerned with the organizational dynamics of managerial innovations and organizational design and how this relates to environmental management. He has published in journals such as Business Strategy and the Environment, European Management Journal, and Technovation.

José Céspedes-Lorente (PhD, National Distance Education University [UNED], Spain) is professor at the University of Almeria. His research interests are focused on strategic management, environmental management, and human resource management. He has published articles on those topics in journals such as Environmental Economics, Human Resource Management, Journal of Business Ethics, Omega, and Organization Science.

Javier Martínez-del-Río (PhD, University of Almería, Spain) is assistant professor at the University of Almeria. His research interests are focused on corporate environmental strategy, green human resource management (HRM) practices, and environmental entrepreneurship. He has published articles on those topics in journals such as European Journal of Management, Human Resource Management, Journal of Business Ethics, Journal of Environmental Management, Organization & Environment, and Technovation.

Raquel Antolín-López (PhD, University of Almería, Spain) is assistant professor at the University of Almería, Spain. She has been visiting scholar at Indiana University, University of Colorado at Boulder, and Loyola Marymount University. Her research interests include sustainability, entrepreneurship, and institutions and sustainability. The quality of her research has been recognized with several international awards. She has articles published in Journal of Cleaner Production, Organization & Environment, Plos One, and Technovation.

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