Environmental standard adoption in Marinas: A spatiotemporal analysis of a special form of maritime transportation hubs

Environmental standard adoption in Marinas: A spatiotemporal analysis of a special form of maritime transportation hubs

Transportation Research Part D 55 (2017) 1–11 Contents lists available at ScienceDirect Transportation Research Part D journal homepage: www.elsevie...

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Transportation Research Part D 55 (2017) 1–11

Contents lists available at ScienceDirect

Transportation Research Part D journal homepage: www.elsevier.com/locate/trd

Environmental standard adoption in Marinas: A spatiotemporal analysis of a special form of maritime transportation hubs William J. Ritchie a, Steve A. Melnyk b, John Z. Ni c,⇑ a

College of Business, James Madison University, MSC 205, Harrisonburg, VA 22807, United States Eli Broad College of Business, Michigan State University, N327 Business College Complex, East Lansing, MI 48824, United States c Farmer School of Business, Miami University, 800 E. High Street, Oxford, OH 45056, United States b

a r t i c l e

i n f o

Article history:

Keywords: Environmental standard Maritime transportation hubs Spatiotemporal analysis Geographical information systems

a b s t r a c t The growth of both commercial and recreational boating has posed significant environmental challenges to waterways. As an effort by the U.S. government and other public service organizations to prevent and mitigate the environmental impact, Clean Marina Programs (CMP) have been developed to encourage marina owners and operators to meet environmental standards and become better stewards of the environment. This study examines the impact of geospatial proximity on the adoption timing and diffusion of a CMP in marinas, a special form of a maritime transportation hub. Drawing upon case study methodology and literature on geography and organizational clusters, we find that the adoption timing of an environmental standard varies with the density of the market within which it is promoted. These results lend support to the notion that firms in close proximity can accelerate standard adoption, hastening information flow about environmental standards through local labor pools, customer interactions, and resources. Ó 2017 Published by Elsevier Ltd.

1. Introduction An established stream of transportation research centers on ocean-going vessels and related environmental issues (Acciaro, 2014; Ölçer and Ballini, 2015; Chang and Jhang, 2016). The vast majority of this literature addresses critical questions related to Business-to-Business (B2B) relationships such as costs-benefit scenarios with processes that minimize firms’ ecological footprints. However, another significant yet overlooked segment in the maritime transportation context is that of marinas. Marinas serve as a special form of transportation hub for commercial fishing vessels and recreational boating. In recent years commercial and recreational maritime transportation across coastal regions has increased dramatically in the United States. Currently there are more than 67,000 commercial fishing businesses in the United States, comprising $6 billion in annual revenues (D’Costa, 2016: 3). Recreational maritime activities (e.g. wakeboarding, waterskiing, and yachting) entertain millions of people every year. According to the National Marine Manufacturers Association (NMMA, 2015), 35.7% of the U.S. adult population, some 87.3 million Americans, participated in recreational boating at least once in 2014. The NMMA estimates that in 2014, there were 16 million registered boats in the United States, representing more than 33,000 businesses in the marine industry and employing more than 500,000 workers. The growth of both commercial and recreational boating has posed significant environmental challenges for waterways. In addition to the apparent environmental threats associated with litter entering waterways from yachts and recreational ⇑ Corresponding author. E-mail addresses: [email protected] (W.J. Ritchie), [email protected] (S.A. Melnyk), [email protected] (J.Z. Ni). http://dx.doi.org/10.1016/j.trd.2017.06.013 1361-9209/Ó 2017 Published by Elsevier Ltd.

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moorings, the maintenance, operation, and storage of these vessels in marinas pose significant risks to both marina owner/operators and adjacent waters. The greatest threats for marinas regarding environmental hazard management are re-fueling activities, discharge from bilges, dust from hull maintenance operations, solvents from engine repair shops, sewage discharges, and heavy metals from antifouling paints. These pollutants may be deposited directly into waterways or transported by storm-water runoff from boatyards. An excess of nutrients, primarily from sewage and discarded fish parts can also result in algal blooms and low-dissolved oxygen in nearby waterways. Toxicants, such as those from paints, can kill life at the bottom of the marine food chain. According to the California Air Resource Board fact sheet, a typical personal watercraft with a two-stroke engine generates more smog-producing emissions in seven hours of operation than a 1998 passenger car driven 100, 000 miles. By an expert estimate, the gasoline, diesel fuel, and oil typically spilled out of recreational boats each year equal the amount of at least 15 Exxon Valdez oil spills in U.S. waterways (Fields, 2003). The number of facilities in a marina (e.g. boat slips, mooring pins, launching ramps, gas docks, sewage pumpout stations, boating supply stores, and boatyards), make it extremely difficult to estimate the amount of pollution a marina generates. Effective management of these issues through self-regulation is complicated further for two key reasons. First, these transportation hubs have an extremely broad geographic distribution along secluded inlets, islands, and waterways. Second, 70% of marinas in the United States are privately owned, and incentives to document events that compromise environmental sustainability have traditionally been limited or nonexistent. In 1992, the U.S. Congress passed the Clean Vessel Act (CVA) to help reduce pollution from vessel sewage discharge. In 2000, the Clean Marina Program (CMP) was developed by the Florida Department of Environmental Protection’s Division of Law Enforcement to complement and enhance the CVA Grant Program. The CMP is a voluntary pollution prevention program that encourages marinas and boatyards to meet environmental standards and become environmental stewards. A Clean Marina designation signifies that businesses meet or exceed program criteria, which includes specific environmental measures and Best Management Practices (BMPs). For marinas, this certification provides a number of benefits. First, the CMP provides a template for best practices and can be used to reduce waste. Second, by participating in the Clean Marina Program, marinas can signal that they are making a significant commitment to satisfying regulatory requirements, thus reducing legal liabilities. Third, through public display of special burgees and signs, a clean marina demonstrates its environmental stewardship. Fourth, a Clean Marina designation confers a substantial marketing advantage to adopting marinas. According to the National Oceanic and Atmospheric Administration’s (NOAA) Office of Ocean and Coastal Resource Management, clean marinas can charge slightly higher slip fees and have fewer vacancies (FDEP, 2016). While previous studies have examined factors that influence the standard adoption decision (e.g. ISO 9000, ISO 14000) in a manufacturing context (Terlaak and King, 2006; Benner and Veloso, 2008; Kennedy and Fiss, 2009), certifications related to transportation hubs such as marinas have never been examined. Due to the relative newness of the CMP, little is known about how the standard diffuse across marinas. New product innovation literature suggests that when a new standard’s adoption has reached a critical mass, its perceived risk decreases and thus, most firms will choose to adopt it (Albuquerque et al., 2007). To examine CMP adoption patterns, we posit that geographic proximity to past adopters affects the decision to adopt the new standard. Geographic proximity has been linked to knowledge spillovers and innovation (Audretsch and Feldman, 1996; Glaeser et al., 1992), thereby facilitating the transmission of new ideas and imitation. Also, geographic clusters increase the pressure of standard adoption through social contacts and the localized competitive environment. The purpose of this study is to apply a geospatial analysis to the CMP certification adoption to better understand adoption patterns among marinas. To study geospatial influences on adoption, we examine all clean marinas on the east coast of Florida using the Getis-Ord Gi clustering statistic. We then employ the average nearest neighbor calculation to demonstrate that the concentration of these transportation hubs influences the timing of Clean Marina certification adoption. Our research context focuses on the state of Florida. Florida has been described as the world’s largest water park (State of Florida, 2004) with an estimated 80,000 km of streams, 7770 square kilometers of lakes, and 10,359 square kilometers of estuaries (FDEP, 2002). There are nearly 2000 marinas operating in Florida today and hundreds of thousands of vessels use Florida’s waterways daily (FDEP, 2002). According to the Marine Industries Association of Florida, boating in Florida is a $10.2 billion water intensive industry that includes marinas, boatyards, and commercial and recreational boaters (Murray, Thomas & Associates, 2005). The effects of year-round boating and boat traffic and their related pollutants contribute to the constant, growing pressure on the state’s fragile aquatic and marine ecosystems. Both the vast bodies of water and the number of marinas in the state can benefit from the CMP certification, as the program encourages marina owners and operators to consider best practices related to preserving the aquatic environment and protecting it from vessels’ damaging anthropogenic discharge.

2. Literature review 2.1. Certified management standards Since the 1980s, certified management standards, including Lean/Just-in-Time Systems, ISO 9001, ISO 14001, Total Quality Management (TQM), and Environmental Management System (EMS), have transformed the way firms create and manage their resources. Initially implemented by Japanese manufacturers, JIT focuses on minimizing waste in the production system through a set of ten programs. TQM has been defined as an integrative management philosophy aimed at continuously

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improving the quality of products and processes to achieve customer satisfaction and improved business performance (Ahire et al., 1995). The ISO 9000 series of quality management systems standards is a widely diffused management practice. Its premise is that well-defined and documented procedures improve consistency of output (Corbett et al., 2005). Certified management standards (CMS) are process guidelines that affect both the structure and administrative mechanisms of organizations (Damanpour and Evan, 1984; Nutt, 1989; Naveh et al., 2006). These process guidelines provide participating firms with strategic benefits, such as more economical processes (Benner and Veloso, 2008), reduced asymmetries of information (Chatterji and Toffel, 2010; Montiel et al., 2012), and manifested legitimacy and reputation (Suchman, 1995; King et al., 2005; Graffin and Ward, 2010). Despite significant implementation costs (Teece et al., 1997), CMS also allows organizations to communicate to stakeholders their conformity to specific industry norms such as environmental standards (Terlaak, 2007). In summary, standards help businesses to formulate plans for quality processes, deploy such plans, and evaluate the impact and outcomes of deployment. All management standards possess a temporal adoption continuum that includes both early adopters and those that lag behind. We also know that early adopters and laggards have differing motivations for adoption (Benner and Veloso, 2008; Terlaak and King, 2006; Ritchie and Melnyk, 2012), depending upon social factors (Terlaak and King, 2006), institutional pressures (Terlaak, 2007; Ingram and Silverman, 2002), the ‘‘newness” of the innovation (Moore, 2014), and economic considerations (Benner and Veloso, 2008). However, the majority of adoption research has been viewed at a very high conceptual level of analysis. To date, research suggests either that tangible organizational characteristics are the primary drivers of adoption timing (e.g. small firms lead and large firms follow (Terlaak and King, 2006)) or that large firms lead and small firms follow (Melnyk et al., 2013). However, there are much more complex social mechanisms that influence CMS adoption timing behavior, and these forces often drive adoption timing more powerfully than economic analyses (Kennedy and Fiss, 2009; Melnyk et al., 2013). The importance of understanding these forces cannot be overstated, as governments have also begun authoring and promoting voluntary CMS. These new standards address the important social issues of sustainability, security, and governance. The Florida Clean Marina Program (CMP) is but one of many voluntary initiatives created by the state (though other similar initiatives nationwide are not always authored by the state). Importantly, scant initial evidence of concrete economic benefits exists for early adopting companies, yet widespread adoption of this type of CMS in various industries is staggering, driving researchers to better understand the motivations that explain adoption timing (Melnyk et al., 2013). 2.2. Research on greening ports Ports operate at the nexus of the world economy. They are vital nodes in the countries and regions in which they are located, facilitating and enabling flows of information, materials, resources and peoples within countries, between countries, and across seas and oceans (Fenton, 2017). The past years have seen increasing concerns on the environmental impact of port operations on natural ecosystems. It is well documented that the environmental impact includes emissions to air, soil and sediments, discharges to water, noise, waste production, changes in terrestrial habitats and marine ecosystems, odor, resource consumption and port development on land or sea (Dinwoodie et al., 2012; Lam and Notteboom, 2014). As such, environmental sustainability in the port industry is of growing concern for port authorities, policy makers, port users and local communities (Acciaro et al., 2014a, 2014b). Evidenced by the increasing number of published articles since 2006, green ports and maritime logistics have become an important area of sustainable supply chain management research (Davarzani et al., 2016). Most of the existing literature on green ports primarily focuses on ecological issues, such as emission, eco-efficiency, fuel consumption, and port development (Davarzani et al., 2016; Denktas-Sakar and Karatas-Cetin, 2012). It is only recently that researchers have started examining strategic issues faced by the ports. For example, Acciaro et al. (2014a, 2014b) investigated successful innovations that improve the environmental sustainability of seaports, proposing a ranking system to assess the success of innovation types in relation to predefined green objectives. Lam and Notteboom (2014) presented an analytical framework that structures port management tools into three policy-oriented categories (i.e. pricing, monitoring, and measuring, as well as market access control and environmental standard regulation). Santos et al. (2016) analyzed European ports’ online sustainability communication practices. Fenton (2017) studied the role of transnational municipal networks (TMNs) on reducing the climate and environmental impact of port operations and international shipping. Several studies examine the process and underlying mechanisms involved in the adoption of environmental certifications and standards among ports. Acciaro (2013) developed a framework exploring how coercive, mimetic, normative and competitive structures influence the environmental strategies of ports. Giuliano and Linder (2013) found evidence that social legitimacy, social pressures, and regulatory threats contribute to the ports’ decision to establish a voluntary program to achieve emission reduction. Our study extends this stream of research by exploring the impact of geospatial proximity on environmental standard adoption in a unique form of port, the marina. 2.3. Geographical proximity in standards adoption The geographic proximity of organizations to each other impacts the inputs that organizations use (e.g. human resource acquisition and mobility), the signals they send to their stakeholders (e.g. local or distant market segments), and how they are viewed in the marketplace (e.g. perceptions of stakeholders differ with location). Throughout history, the concept of

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proximity has manifested itself in business clusters (Porter, 1998: 78). A business cluster is a geographic concentration of interconnected businesses, suppliers, and associated institutions in a specific industry. Such clustering often significantly impacts competitive advantage (Porter, 2000). Geographic clusters of organizations result in productivity increases due to readily available information and pools of technology where market, technical, and specialized information can be accessed more efficiently and less expensively (Tallman et al., 2004). The combination of collaboration and competition incentivizes firms to operate at higher levels of innovation and productivity, and also leads to the formation of new businesses (Rivera et al., 2016). An examination of the nature of firm clustering reveals that technology, information, and customer needs that ordinarily may appear disparate actually coalesce across firms and industries (Porter, 2000). Taken in aggregate, the literature points to the presence of positive externalities for those in close proximity (Audretsch and Feldman, 1996) whereby there is a spillover of benefits that accrue to neighboring firms. Similar externalities associated with clusters have been highlighted in research related to employment (Angel, 1989) and acquisitions (Chakrabarti and Mitchell, 2015; Baum et al., 2000). Most notably, clusters have been found to greatly impact knowledge diffusion and generation, research and development, and innovation (Jaffe et al., 1993; Brown, 1975; Brown and Cox, 1971; Bania et al., 1992; Anderson, 2006; Arikan, 2009; Audretsch and Feldman, 1996; Engel and del-Palacio, 2011; Zhang and Li, 2010; Funk, 2014). 2.4. Environmental standards in the maritime industry – Clean Marina Program In 2000, the Florida Department of Environmental Protection (DEP) developed the Clean Marina Program. This voluntary initiative certified Florida marinas as a ‘‘Clean Marina” if they demonstrated compliance with CMP guidelines. A special flag signifying certification is issued to the marina by the certifying organization. Flown over the marina, these flags communicate to all stakeholders the certification achievement. Since then, similar certification programs have emerged in 23 others states, including California, Michigan, Texas, South Carolina, and Virginia. The Clean Marina CMS is a voluntary program that ‘‘stresses environmental and managerial best management practices that exceed regulatory requirements” (Association of Marina Industries, 2016). The objectives of the Clean Marina program are to promote environmentally clean marinas and to protect the states’ coastal and inland waters from pollution by encouraging the implementation of best management practices. To accomplish this, the Clean Marina program has instituted a series of comprehensive regulations that must be complied with for a marina to achieve and retain certification. There were 325 certified marinas in the state of Florida when this analysis was conducted. Clean marina guidelines require that marinas must be prepared for hazardous spills, whether large or small, by having enough floating boom to encircle their largest vessel. Marinas must also have a well-articulated plan for spill cleanup as well as employee training programs in place to deal with such an eventuality. The marinas are also required to provide environmental training programs for their boating clientele. Certification requests are rejected when marinas fail to demonstrate that they are leaders in their communities by going ‘‘above and beyond” the Clean Marina certification standards. As with the adoption and implementation of any quality standard, the degree of difficulty associated with achieving the Clean Marina certification is largely a function of the commitment from both the owners and the managers of a given marina. For example, many marina dock masters try to implement the environmental guidelines but may not have the support of the owner, making it more difficult to achieve compliance. Overall, adopting marinas take the Clean Marina designation very seriously and are truly environmental stewards and leaders in their communities. These facilities work towards ‘‘continual improvement” and they incorporate new best management practices as they learn of them and as the marina can fund improvements/changes. It is noteworthy that the environmental benefits of the Clean Marina designation extend beyond the immediate boundaries of the marina. For example, while the Florida CMP does not designate a given port as ‘‘clean”, many marinas are part of ‘‘Working Waterfronts” which include ports. Thus, ports are becoming more important, since many ports are working waterfronts that draw the public to these areas for recreation, entertainment, and housing. We argue that this Florida DEP-administered CMS is of interest to logistics and transportation researchers since marinas are actively engaged in distribution activities on three key fronts. First, marinas often serve as the transportation hub and distribution center for commercial fishing. This activity exposes marina owners and operators to a variety of potential environmental threats related to large-scale vessel maintenance and biomass from fish cleaning. Clean Marina addresses this by encouraging marina facilities to install fish cleaning tables for their patrons and to dispose of carcasses in ways that do not include merely tossing them into the marina basin. Second, many marinas are waste collection facilities, and thus responsible for the collection, storage, and conveyance of waste byproducts to processors. This often involves overseeing logistics associated with transporting toxic and hazardous waste, such as contaminated fuels and sewage. Third, marinas are often a retail hub for both new and used watercrafts. As such, these organizations are responsible for both inbound and outbound logistics as they serve as brokers for maritime vessels. 3. Methods and results 3.1. The research context We employed a case study methodology (Yin, 1994; Churchill, 1999) to examine the timing of marina adoption of the Clean Marina standard and potential spatiotemporal relationships. This approach was deemed particularly appropriate given

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that this study used multiple data sources to better understand the dynamics of environmental standard adoption (Eisenhardt, 1989). One of the key benefits of this methodology is its identification of subtle similarities and differences in a research context that might otherwise be overlooked (Eisenhardt, 1989). To this end, our study draws on secondary data generated from the implementation of the Clean Marina Program in Florida, one of the first states in the United States to implement a Clean Marina program. Thus, the unit of analysis is the individual marina and their adoption timing of this environmental standard. Data related to adoption timing (measured by the number of elapsed days since the inception of the CMP in 2000) and location (measured in terms of digital latitude and longitude) was gathered from all 154 CMP certified marinas on the east coast of Florida. The east coast marinas were selected in this analysis to control for spurious effects of the large land mass in the central part of the state. As of this writing, the entire population of certified clean marinas in the state of Florida had an average adoption date of six years following the inception of the CMP. The average marina size was 97 boats slips, and 65% offered pumpout station services. This research context proved to be an effective case study for three reasons. First, the Clean Marina program is a voluntary CMS, embodying many of the characteristics of other quality standards, such as ISO 9001, in the marketplace. This enhances the generalizability of the findings. Second, the study data is longitudinal and spans a 13year period where adoptions have steadily increased, similar to ISO 9001 and other mainstream quality standards. Third, as an environmental sustainability initiative, the Clean Marina certification is a comparable model for other environmental CMS’s since process guidelines and documentation in the Clean Marina Program are reproducible and readily implemented in other contexts, including internationally (e.g. Australia’s Marina Industries Association International Clean Marina Program) (Marina Industries Association, 2017). 3.2. Topological analysis The primary purpose of this research design was to discover geospatial patterns of adoption timing. We used the ArcGIS software platform (ESRI, 2014) to analyze the data using spatial analyst tools, including ‘‘average nearest neighbor” to initially screen geospatial data and determine regional concentrations, and ‘‘optimized hot spot analysis” to identify statistically significant geographic clusters (ESRI, 2014). ESRI’s mapping software has been used extensively by cartographers and statisticians for more than four decades to analyze virtually all manner of relationships in geospatial data ranging from flood plain analyses to company location optimization (ESRI, 2014). 3.2.1. Optimized hot spot analysis We first subjected the adoption-timing variable (i.e. number of elapsed days between CMP inception and marina certification) to ‘‘optimized hot spot analysis” (ESRI, 2014) using the Getis-Ord Gi statistic (Getis and Ord, 1992; Ord and Getis, 1995). The Getis-Ord Gi Statistic formula is as follows:

Pn Pn j¼1 wi;j xj  X j¼1 wi Gi ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi   2  Pn 2 Pn n

S

j¼1

wi;j 

j¼1

wi;j

n1

where Xj is the value of the attribute for the feature being measured (j) and Wi,j is the spatial weight between feature i and j, and n is the equal to the total number of features and:

Pn X¼



j¼1 xj

n sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn 2 j¼1 xj n

 ðXÞ

2

This methodology provided a plot of statistically significant clusters of high values (number of days of adoption labeled as ‘‘hot” spots) and statistically significant clusters of low values (number of days of adoption labeled ‘‘cold” spots) (Mitchell, 2005). Associated adoption timing z-scores, p-values, and a confidence level for membership in a given geographic cluster of organizations is calculated for each feature based upon an observed versus expected distribution (see Getis and Ord, 1992; Ord and Getis, 1995; and Mitchell, 2005, for an elaboration on calculations). Results of the ‘‘optimized hot spot analysis” (see Table 1)revealed two statistically significant hot spots on the east coast of Florida, each with a markedly different temporal adoption signature. The first group, the late adopter cluster, was in the north and included marinas with a mean certification date of 8.3 years (3051 days, p < 0:05)) following CMP’s inception (see Fig. 1). In this cluster, five entities were commercial marinas. The total number of slips ranged from five to 550 slips, with an average of 97 slips. Six marinas offered pumpout facilities, whereby boaters can discard waste from vessels. The second group, the early adopter cluster, included marinas with a mean certification date of 4.8 years (1747 days, p < 0:05)) following CMP’s inception (see Fig. 2). In this cluster, nine entities were commercial marinas. The total number of slips for this clus-

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Table 1 Optimized hot spot analysis results.

Late Adopters Early Adopters

N

Mean Adoption Days

Std. Deviation

Std. Error Mean

18 24

3051 Days 1747 Days

1267.683 1248.526

298.796 254.854

T Tests Mean Difference Significant p < 0.05.

Fig. 1. Late adoption cluster with certification lag date. Days since standard creation. Significant at p < 0.05.

ter ranged from 12 to 342 slips, with an average of 79 slips. The number of wetslips (slips in the water) ranged from two to 343 slips, with an average of 77 slips. Ten marinas in the cluster offered pumpout facilities. These results reveal that the timing of administrative innovation adoption is subject to unique proximal attributes. Literature suggests that the combination of shared employee backgrounds, social networks, and complementary resources associated with a given locale enhance diffusion of information about administrative innovations (Bell and Zaheer, 2007). In the case of information spreading regarding CMP, organizations in close proximity may be more likely to share information and adopt certain shared standards. Similarly, information will diffuse among laggard firms as well, setting up potential clustering based upon early or later adoption timing clusters.

3.2.2. Average nearest neighbor analysis In the second phase of the analysis, we employed the ‘‘average nearest neighbor” function in ArcGIS 10.3 (ESRI, 2014) to examine marina concentrations associated with the early and late adoption clusters. The average nearest neighbor analysis provides four relevant metrics: the observed mean distance between entities in our study, the expected mean distance between entities, a nearest neighbor ratio, and associated z-score and p-values. In general, the observed mean distance of the entities in this analysis is compared with the average distance of a hypothetical random distribution. If the observed

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Fig. 2. Early adoption cluster with certification lag date. Days since standard creation. Significant at p < 0.05.

mean distance is less than the expected mean distance, then clustering might be evident. Similarly, if the observed mean distance is greater than the expected mean difference, the data manifests more dispersion (Mitchell, 2005). The calculation for the nearest neighbor ratio is as follows:

ANN ¼

Do DE

where Do is the observed mean distance between each entity and D E is the mean distance for entities in a random pattern.

Pn

Do ¼

i¼1 di

n

:5 DE ¼ pffiffiffiffiffiffiffiffiffi n=A The z score and SE can be calculated with the formulas:

zz ¼

Do  DE SE

0:26136 SE ¼ pffiffiffiffiffiffiffiffiffiffiffi n2 =A

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This analysis revealed that there was a marked difference in firm concentrations for each of the adoption timing clusters. Specifically, the northern region certification cluster of laggards had a low concentration of firms, with an average nearest neighbor distance of 1036.7951 m. By contrast, the southern region cluster of early adopters had a high concentration of firms, with an average nearest neighbor distance of 520.2294 m (see Table 2). The notable difference in average distance among early and late adoptions of CMP begs an additional question. Why are early adopters more closely clustered than laggards? This finding points to the impact of geographical proximity on a standard’s diffusion. First, the actors in geographically proximate organizations share common backgrounds (Paniccia, 1998), thus providing a sense of connectedness and a social platform for relationship building. Second, firms in close proximity benefit from social networks, associations with local communities, and chance meetings (Bell and Zaheer, 2007). A significant part of these local communities are the vessel owners who purpose to patronize designated clean facilities as they travel. Third, more proximate firms often have access to specialized inputs such as employees/employer interactions and complementary resources. Furthermore, individuals partial to a specific geographic proximity will likely remain in that location and engage in homogeneous practices (Bell and Zaheer, 2007), accelerating information sharing (Pouder and St. John, 1996). Financial and organizational statistics are considered to be ‘‘hard” information easily transmitted across great distances with little distortion (Borgatti and Cross, 2003; Chakrabarti and Mitchell, 2015). However, in the case of an environmental standard (and the timing of adoption in particular), managers are most interested in whether the standard has met the adopting organization’s goals and expectations. This type of information is considered ‘‘soft,” and is not readily quantifiable or conveyed objectively across great distances (Chakrabarti and Mitchell, 2015). Thus, ceteris paribas, soft information and opinions generated by an early adopting firm regarding process nuances will diffuse more quickly in a concentrated field of firms. In other words, the close proximity of organizations facilitates constant comparison regarding adoption outcomes and accessible information about the standard (its success, failure, costs, and benefits).

4. Discussion and conclusions Evidenced by the increase of relevant published articles since 2006, green ports and maritime logistics have become important research areas and represent a critical branch of sustainable supply chain management research. The focus on sustainable port operations found in the Clean Marina standard is not unique to the United States. Globally, major ports such as Rotterdam have joined and formed the World Port Climate Initiative (WPCI). These developments have been discussed by Acciaro et al. (2014a, 2014b) and Acciaro (2013). WPCI involves ports coming together and sharing best practice. However, what makes this study so unique is not its focus on sustainability but rather its interest in the impact of geography on the adoption and diffusion of a standard. This article examined how geospatial factors impacted environmental standard adoption surrounding a special form of maritime transportation hub. We found evidence that standards adoption timing depends upon the proximal nature of the marinas in our study. Regarding CMP adoption timing, organizations located in more concentrated markets manifested early adoption clusters while those in less concentrated markets lagged in applying new standards. These results support prior evidence that firms in close proximity enhance the diffusion of certain innovations. These findings can now be extended to certified management standards as applied to maritime transportation hubs, such as marinas. We believe that similar to the processes of traditional innovation acceleration, the spread of certification adoption is enhanced through local labor pools, customer interactions, and local resources. In contrast, greater spatial distances between firms slowed innovation diffusion, as represented by the study’s cluster of adoption laggards. These findings suggest opportunities for additional analyses of conditions related to other environmentally-focused transportation standards. This study’s findings also highlight critical issues regarding bandwagon evolution in standards. Traditional explanations of bandwagon evolution center on economic or social theories (Benner and Veloso, 2008; Abrahamson and Rosenkopf, 1993; Terlaak and King, 2006). However, the geographic clusters this study identified provide evidence that the drivers of adoption, amid a bandwagon, are multifaceted and potentially involve significant geospatial forces. Existing research on standard adoption bandwagons has largely focused on two extremes: either high-level effects that involve socially constructed pressures or micro-level effects such as firm activities and demographics. Both of these approaches overlook a critical mezzolevel effect, that of geographical proximity. When studying bandwagon behavior, both temporal and geospatial levels should be examined. Some interesting patterns emerge from this case study as a result of evaluating bandwagons in terms of proximal factors. Specifically, traditional long-term adoption graph slopes of a standard such as ISO 9001 reveal marked differences between countries (Albuquerque et al., 2007). Our analysis reveals that such differences also occur on the local level and are likely related to firms’ proximal locations. We describe this phenomenon as a micro-bandwagon, whereby proximally specific

Table 2 Average nearest neighbor summary.

Late Adopters Early Adopters

Observed Mean Distance

Expected Mean Distance

Nearest Neighbor Ratio

z-score

p-value

1036.7951 m 520.2294 m

3731.4901 m 1278.3540 m

0.2779 0.4070

19.1429 16.5970

0.000 0.000

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Fig. 3. Cumulative adoption time continuum of Clean Marina Program.

Fig. 4. Adoption time continuum (Early vs. Late Adopters) of Clean Marina Program. North County and South County Clusters.

adoption clusters exist amid a broader bandwagon backdrop. Thus, when considered in aggregate, the cumulative adoption time continuum is a relatively smooth line with a constant increase (see Fig. 3). However, when looked at through the lens of geographic clusters, unique adoption signatures are evident (see Fig. 4). The merit of placing geospatial factors at the forefront of bandwagon literature is that we address a simple yet profound condition in the practice of strategy: that the proximal nature of firms significantly influences resource dependencies (Salancik and Pfeffer, 1978) largely due to its impact on institutional ties, knowledge transfer, trust, and resources. In essence, firms in close proximity share information from many local sources, such as the customers and employees, regarding adoptiontiming decisions for standards. Future research should explore these relationships in greater detail. For example, it would be interesting to explore whether marinas in heavily populated areas adopt the CMP due to social pressures from local communities. In practice, these findings may also suggest that the authoring agencies of standards be more attentive to geographic areas with lower institutional concentrations and more vigorously promote the standards among these entities to overcome the liabilities of spatial distance between them. That is, the results presented in this paper would argue that geographic proximity is a ‘‘force multiplier” – it encourages the adoption of standards such as the one explored in this study. Sponsoring agencies are interested in seeing the rapid and widespread acceptance of a standard such as the Clean Marina. After all, when a standard becomes widespread and accepted, it becomes viewed as being ‘‘taken for granted.” When it is taken for granted, then it is assumed that organizations such as marinas and ports will normally pursue certification. However, any sponsoring

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agency is faced with the reality of fixed and finite resources. What this study suggests is that such agencies may want to adjust their investments – spending relatively more in areas that cannot benefit from the multiplier effects of geography and spending less in those areas where geographic proximity is more pronounced. Currently, Florida offers a ten percent discount on submerged land leases for those marinas with clean designations and many marina holding companies require their marinas to obtain certification. Such practices might be targeted and promoted to specific geographic regions, in much the same manner that states offer investment tax credits for real estate investments in targeted geographic areas. Finally, our findings also highlight the fact that researchers must consider their geospatial methods to ensure adequate rigor and robust findings. Geographic characteristics can readily confound even the most well-designed study. 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