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Ensuring returns management software effectiveness through joint development orientation.


Returns management has gained managerial and academic attention in recent years, but it still remains a significant challenge for many companies due to its unpredictable and irregular nature. The current study contributes to existing literature by focusing on the effectiveness of externally developed customizable software. Drawing upon the service dominant (S-D) logic and using empirical survey data, this study not only confirmed the positive impacts of returns management software effectiveness on firm market performance but also identified a joint development orientation as crucial to ensuring returns management software effectiveness.


Product returns, returns management software, joint development orientation, software effectiveness, firm market performance


Reverse logistics has been defined as "the process of planning, implementing, and controlling the efficient, cost effective flow of raw materials, in-process inventory, finished goods, and related information from the point of consumption to the point of origin for the purpose of recapturing value or proper disposal" (Rogers and Tibben-Lembke 1999). As Stock (1998) notes, from a business perspective the term refers to the role of logistics in "product returns, source reduction, recycling, materials substitution, reuse of materials, waste disposal, and refurbishing, repair, and manufacturing" (2). Our research focuses on one aspect--the returns management (product returns) component of reverse logistics. Returns management is examined because of the magnitude of returns and the impact that returns have on profitability and customer service (Stock and Mulki 2009). Handling product returns is particularly important in the age of e-commerce. While consumers in general can be demanding, it has been suggested that on-line shoppers are even more demanding (Griffis et al. 2012).

In spite of its importance, many companies still face significant challenges in improving their returns management operations. Software solutions offer a means of addressing the challenges. Effective software can enhance reverse logistics and returns management by supporting the development of formalized processes and procedures and by providing crucial information support (Richey et al. 2005). Today's logistics management, including returns and reverse logistics management, is dependent upon appropriate technologies to provide the information to make effective decisions (Hazen et al. 2014; Warth, Kaiser, and Kugler 2011). The primary reason is the data-driven nature of the tasks involved. Returns management is complicated and requires the capture of numerous data points (Horowitz 2010). This process becomes increasingly complex as retailers operate in multiple channels (Tabin 2013). Whether utilizing traditional retailing (brick-and-mortar), catalog sales, on-line, or some type of omni-channel combination of those approaches, information support for returns management is critical. For example, it has been reported that monitoring returns can cut credit issuances by as much as 30 percent, adding directly to the bottom line (Horowitz 2010). Returns-related information support is also required to meet tighter governmental regulations (Terry 2014).

Although software solutions have important strategic implications, to the best of our knowledge there have been no empirical studies published on returns management software. We address this topic with the current study. Companies can develop the software in-house, buy off-the-shelf software with no customization, or purchase externally developed customizable software. Ideally, software capability should accommodate requirements as diverse as returns management authorization (RMA), handling of noncongruent inventory (items a customer buys on-line or in one store location and returns to a store that does not carry the specific stock-keeping unit), timely issuance of refunds and credits, capture of critical metrics, selection of appropriate disposition method, and so on (Gooley 2013; Rogers et al. 2002).

Therefore, returns management software should be the following: customized to the individual needs of the user, flexible in terms of functionality, and upgraded on a regular basis. Because of technological and resource constraints, purchasing software from external specialized solution providers often presents a feasible approach to accessing state-of-the-art information technologies. Also, because reverse logistics and returns management processes can vary significantly across companies, customizable software appears to be a very popular solution (Greve and Davis 2012). Therefore, our focus is externally developed customizable returns management software. The software is developed by specialized external vendors and can be customized to have different configurations or components in order to meet specific user requirements.

Resource commitment is critical to returns management performance (both economic and service performance) (Daugherty et al. 2005). Specifically, we look at joint development orientation as a resource that can be used to enhance another resource--returns management software effectiveness. We propose that a joint development orientation is an important concept in understanding and meeting customer needs with commercially available software that is customizable. Input from users provides direction for software development and greater likelihood that software can be designed in a way to comprehensively accommodate a broad range of needs. We also examine the relationship between software effectiveness and firm market performance. Each of these relationships will be developed in greater depth; however, an important contribution of the research rests in how joint development orientation is viewed in our research.

Traditionally, the joint development process has been operationalized in terms of the interactions between potential customers and developers (Gadde, Hjelmgren, and Skarp 2012; Newman and Robey 1992). For example, within the software development literature, Svendsen et al. (2011) created a three-item scale that includes: (1) active outreach by software vendors, including observations, interviews, visits, surveys, and so on; (2) assessment of efforts, involving experience prototyping and participatory methods; and (3) measurement of the degree to which companies' inputs (ideas/suggestions) are valued. While we believe these inputs are critical to understanding joint development, we also argue that joint development should be theoretically broader--reflecting users' and developers' business philosophies. Thus, we introduce a multidimensional construct: joint development orientation.

In the next sections, we present an overview of the service-dominant (S-D) logic as our conceptual framework and then discuss the development of the higher-order construct--joint development orientation. Then we introduce our conceptual model and discuss hypothesis development. This is followed by a detailed presentation of methodology and analysis results. Finally, we provide a discussion on research and practical implications along with the limitations and suggestions for future research.

Theoretical Development

Vargo and Lusch's (2004) initial work on the service-dominant (S-D) logic has gained widespread attention in the marketing field. Lusch, Vargo, and Tanniru (2010) subsequently extended their original S-D logic from a general marketing application to that in a supply chain context. They argued that an extended view of goods should be considered to include service distribution or a provision mechanism and that the firm is the essential service provisioning agent operating in a complex and adaptive value network. With the S-D logic, the term "service" is a transcending concept that captures both the more traditional terms "goods" and "services."

Vargo and Lusch (2004) further suggested that service is a process, rather than an output, in which a firm uses its resources to create value for its customers. Such value is embedded in its offerings. In the returns management software context, users of the software want to provide greater value for their customers through efficient, speedy returns processing. We argue that adoption of a joint development orientation (combining resources and knowledge of the software users as well as developers') enhances the likelihood of the software users being able to provide better service for their customers. The jointly developed value-added software solutions reflect the basic premise of the S-D logic that service provision should be a paramount business objective. Thus, the S-D logic provides a viable theoretical lens.

Value co-creation is a core concept in the S-D logic (Ehrenthal, Gruen, and Hofstetter 2014; Liu et al. 2014; Lusch, Vargo, and Tanniru 2010). Flint, Lusch, and Vargo (2014) proposed that value co-creation involves co-design and co-production. This means customers are actively involved in helping design and even in manufacturing output. Further, they posited that a product essentially has no value until a customer uses it to achieve a desired goal. Thus, by definition, the customer is a co-creator of the value. Such perspectives have been echoed by supply chain management researchers (e.g. Daugherty 2011; Edvardsson, Gustafsson, and Roos 2005; Liu et al. 2014; Maas, Herb, and Hartmann 2014). We believe the value co-creation concept is also applicable to the returns management software context. More specifically, the fact that customizable software is purchased externally helps user firms improve returns management efficiency, thereby creating actual value. In the current study, we term such value as returns management software effectiveness. It represents a resource for users.

We believe involving users is at the core of a successful returns management software development process, and therefore, our focus is on "orientation." Orientation is the recognition by an organization of systemic, strategic implications of managing certain tactical activities (Mentzer et al. 2001). Firms do not take specific actions without reasons; organizational behaviors are guided by the firm's strategic orientations (Gatignon and Xuereb 1997). Therefore, based on the S-D logic's emphasis on value co-creation, we first conceptualize a key concept--joint development orientation--and then develop a theoretical model (as shown in fig. 1). With such an orientation, the resources of both parties--user and vendor--are combined in the value co-creation process to achieve cost or learning advantages (Borys and Jemison 1989).

The Conceptualization of Joint Development Orientation

While the concept of joint development process is not new, the proposed joint development orientation construct has not been covered in existing literature. Therefore, we first discuss the development of this higher-order construct along its three dimensions. In general, the term "orientation" refers to adopting a specific view or philosophy of business. Swierczek and Ha (2003) expounded upon this by defining orientation as a state of mind that directs attention and behaviors toward a specific goal. Orientation can be conceptualized as having both behavioral and cultural dimensions (Kirka, Jayachandran, and Bearden 2005; Kohli and Jaworski 1990). Behavioral aspects include organizational activities; cultural aspects reflect organizational norms and values (Deshpande, Farley, and Webster 1993; Kirka, Jayachandran, and Bearden 2005). To illustrate, consider market orientation, which has been defined as "the organization-wide generation of market intelligence pertaining to current and future customer needs, dissemination of the intelligence across departments, and organization wide responsiveness to it" (Kohli and Jaworski 1990, 6).

Within the supply chain discipline, Min and Mentzer (2004) examined the concept of supply chain orientation (SCO). They defined SCO in terms of an organization's implementation of the systemic, strategic implications of tactical activities involved in managing supply chain flows. Consistent with this, a joint development orientation reflects organizations' implementation of tactical activities aimed at strategically supporting joint development initiatives. We further propose that a joint development orientation should reflect both behavioral and cultural aspects. From a behavioral perspective, a joint development orientation includes, but is not limited to, interactions between users and developers such as discussions, interviews, and completion of surveys. Cultural aspects of a joint development orientation include the willingness to make the necessary efforts to work collaboratively and recognition of the potential value to be gained. More specifically, we propose that a joint development orientation includes top management support of returns management, shared vision (user and vendor), and a joint development process.

Top management support has been defined as "devoting time to the [specific] program in proportion to its cost and potential, reviewing plans, following up on results, and facilitating the management problems involved with integrating [the program] with the management process of the business" (Young and Jordan 2008, 715). Top managers include those individuals who "have the authority to influence other members of the business, and are more likely to succeed in overcoming organizational resistance" (Thong, Yap, and Raman 1996, 248). Ravi and Shankar (2005) specifically addressed the need for top management support of returns management in order to help insure success. Further, Newman and Robey's (1992) research is directly relevant. They identified top management support as an important predictor of successful information system development.

When there is a high level of top management support, personnel involved are more likely to take an interest in the project and be willing to take ownership and risks to make the project successful (Swink 2000). Top management support can pull functional groups together, providing organizational support for change and assuring sufficient allocation of resources (Zirger and Maidique 1990). And, importantly, top management support is needed to make sure that critical resources are available (Bardi, Raghunathan, and Bagchi 1994).

If those resources are not available internally (in this instance, if adequate information-related resources are not available), it is likely that firms will collaborate with external providers. As noted by Gadde, Hjelmgren, and Skarp (2012), the combining of resources across firms "plays a crucial role because companies are increasingly dependent on access to the resources of their business partners" (210). Considering that many firms have not fully realized the strategic importance of returns management, top management support becomes especially critical in this context. Lack of commitment by top management has been identified as a chief barrier for successful returns management (Ravi and Shankar 2005; Rogers and Tibben-Lembke 1999). The cross-firm joint development of returns management software would not be possible without the support from a firm's top management. Therefore, we suggest top management support is an integral component of joint development orientation.

Having a shared vision means that the parties involved have common goals (Abrams et al. 2003). Pearce and Ensley (2004) defined shared vision as "a common mental model of the future state of the team or its tasks that provides the basis for action within the team" (260). Thus, it represents a roadmap of desirable outcomes and what needs to be accomplished. Further, the common mental model can serve as a "bonding mechanism" to facilitate "integration and the combining of resources" focused at achieving the desired outcomes (Tsai and Ghoshal 1998, 467). Our research centers on the impact of the returns management software development team (the user and vendor members) having a shared vision of what is needed to effectively support the user's returns management operations.

Shared vision implies a common commitment to achieving goals and a common or shared sense of organizational purpose. Further, people tend to support what they help to create (Garcia-Morales, Llorens-Montes, and Verdu-Jover 2006). As noted by Holtzblatt and Beyer (1995), "customers and designers must develop a shared understanding of the work problems and the impact of technical solutions" (31). Shared vision enables team members to integrate their efforts and engage in productive behaviors (Tsai and Ghoshal 1998). For example, the user and the vendor should have consensus on the key functions and features of the returns management software. Thus, we argue that the shared vision between user and vendor is also a dimension of joint development orientation.

Last, joint development orientation requires that a process be in place. Otherwise, this specific business view or philosophy regarding returns management software cannot be successfully implemented. The joint development process involves the software user and the software vendor collaborating and combining their resources and knowledge in the development of the returns management software solution. A joint development process allows firms to efficiently source external knowledge (Kang and Kang 2014). We thus propose that joint development process is also a pertinent dimension of joint development orientation.

The S-D logic has a primary focus on operant resources, which are resources that produce effects (i.e., create value) (Constantin and Lusch 1994; Vargo and Lusch 2004). Operant resources are often intangible (e.g. knowledge and skills) (Lusch, Vargo, and Wessels 2008). In the current study, the joint development orientation is conceptualized with three dimensions, encompassing behavioral and cultural aspects, that is, activities and the norms/values of the organizations. The three dimensions--top management support of returns management, shared vision between user and vendor, and a joint development process--are combined to represent a higher-order operant resource.

Therefore, we propose that:

H1: A firm's joint development orientation of returns management software has three dimensions.

H1a: Top management support of returns management constitutes a dimension of a firm's joint development orientation.

H1b: Shared vision between user and vendor constitutes a dimension of a firm's joint development orientation.

H1c: Joint development process constitutes a dimension of a firm's Joint development orientation.

Joint Development Orientation and Returns Management Software Effectiveness

Having conceptualized the joint development orientation construct, now consideration will be given to the outcomes of this orientation--more specifically, its impact on returns management software effectiveness. "Software effectiveness" is defined as the degree to which the user achieves the goals he or she sets out to achieve with respect to using the software (Segall, Doolen, and Porter 2005). We consider returns management software effectiveness (a resulted value) as an outcome of the joint development orientation. Degree of user involvement in service/product development (i.e., joint development) is generally believed to be a predictor of achieving goal success (Alam 2002; Gadde, Hjelmgren, and Skarp 2012). In a more general sense, when customers and suppliers are willing to share relevant information, firms may be able to truly extend the planning and execution across firms (Stenger 2011).

It has long been recognized that user participation is a critical factor in achieving information system success (Hartwick and Barki 1994; Rosenbaum 2014). Users can influence the development of information systems so that the end result provides a match with the user's needs (Lee and Xia 2010). By extension, the same should be true of the development of specific types of software. When successful, a tailored or customized product (software) results. Previous research indicates that when a software team is responsive to user requests, improvements in the functionality or effectiveness of the software can be achieved (Gefen and Ridings 2002; Lee and Xia 2010). Also, users who participate in the development of software and thus have the chance to ensure their needs are met by the resulting solution are likely to perceive the system as important and relevant (Hartwick and Barki 1994).

The S-D logic suggests that firms utilize operant resources to create value; the resources can be either internal or external. We previously conceptualized joint development orientation as an operant resource that has three dimensions. In combination, these dimensions--top management support of returns management, shared vision between user and vendor, and joint development process--are likely to improve the chances of creating effective returns management software. New technologies and software, particularly very specialized software, are most often developed by external information technology (IT) providers with the requisite core competencies (Peterson, Handfield, and Ragatz 2008). Ravi and Shankar (2005) underscored the necessity for better technological systems and information support for reverse logistics/returns management activities and noted the "dearth" of good commercially available products. When the top management within a firm acknowledges the importance of and supports returns management, the firm's returns management personnel are likely to seek the best available software solutions, usually coming from external IT developers. The user firm and IT developer are likely to collaborate and work closely on the software development to ensure the software can meet the specific requirements related to handling returns, that is, they utilize a joint development process resulting in a customized product. Circumstances can be improved for all involved parties when organizations use their own resources and the resources of others (Siguaw, Gassenheimer, and Hunter 2014; Vargo and Lusch 2004).

Chang and Huang (2012) proposed that "when the exchange parties have a shared vision, they have the same perception about how to integrate strategic resources and how to interact with each other" (851-52). The existence of a shared vision (of what is needed with respect to returns management software) can be considered as a valuable resource, specifically input into the returns management software development process. Defining software requirements has been proposed as being the most important and most challenging part of the software development process (Guinan, Cooprider, and Faraj 1998). Lack of user input and incomplete requirements are common reasons for failed software projects (Barney, Aurum, and Wohlin 2008; Boehm 2006). Shared vision, in effect, prescribes specific software requirements. Within the current context, a shared vision between the user and vendor of the returns management software solution can help to ensure the effectiveness of the joint development process.

A joint development process also has the potential for yielding significant benefits. For example, "involving and working closely with suppliers in ... development might facilitate internal coordination (both within and between supply chain partners), facilitate the effective leveraging and deployment of relation-specific assets, and enable knowledge-sharing routines within supply chains" (Paulraj, Chen, and Lado 2012).

Therefore, it is proposed that when a user firm embraces a joint development orientation and works closely with a software vendor by sharing expertise and insights, it results in more effective returns management software. Because such software addresses the user firm's specific needs regarding returns management, it can be expected to help the user firm not only reduce return-related costs such as service, freight, receiving, handling, storage costs, but also capture more value through secondary markets, fewer markdowns, asset recovery, and asset utilization and rationalization. We thus propose that:

H2: Joint development orientation is positively related to returns management software effectiveness.

Relationship between Returns Management Software Effectiveness and Firm Market Performance

In order to further validate the value of returns management and returns management software, we examine the relationship between returns management software effectiveness and firm market performance. Market performance can be viewed as an indirect value and resource resulting from the utilization of the software. With the support of effective returns management software, properly handled returns can positively affect customer satisfaction and repurchase behavior, help companies better recover assets, reduce costs and losses, and so on. However, returns management support requirements at the firm/organization level are complex due to the nonstandard nature of the tasks involved (Mollenkopf and Closs 2005; Rogers et al. 2002; Stock and Mulki 2009). In addition, return management processes vary across companies. Such specific requirements can be addressed through the joint efforts of the user and the software vendor during the development process resulting in highly effective software. In spite of this, Greve and Davis (2012) suggested that while there are not many supply chain executives who have significant experience managing returns, there are even fewer IT executives with such experience. This situation supports the argument that returns management software effectiveness is a resource that is often rare.

Stock, Speh, and Shear (2002) suggested that returns management can be seen as an opportunity to build competitive advantage. Similarly, Mollenkopf and Closs (2005) identified potential positive impacts of returns management, including improved asset turnover, lower operating expenses through the reclamation process, and goodwill. In line with their arguments, we thus propose:

H3: Returns management software effectiveness is positively related to firm market performance.


Research Design and Data Collection

A survey was developed to evaluate relevant constructs using multi-item measures. The preliminary draft of the survey was reviewed by three academics and five industry experts, all of whom were familiar with the topics of interest. Based on their inputs, questions that were ambiguous or did not relate to the construct of interest were reworded or eliminated, and others were added. Last, a pilot study of the survey was conducted with 30 managers in the returns management field, and the results of the pretest were used to develop the final version of the survey.

In order to improve the efficiency of data collection, a dedicated web link through Qualifies was made available in addition to a traditional mail survey. The respondents had the option to complete the survey either in paper format or online. Previous studies have validated and supported such an approach (Boyer, Olson, and Calantone 2002; Griffis, Goldsby, and Cooper 2003). An initial list of potential respondents was randomly generated from the member directory of the Reverse Logistics Association (RLA).

RLA is the largest trade organization in the reverse logistics field. RLA members represent a balance of companies involved in manufacturing, retailing, wholesaling, logistics service providers, and software solution providers. Potential respondents were selected because they hold returns management-related managerial positions (must be managers, directors, or above); they are knowledgeable about their companies' returns management operations. They were contacted with e-mails and telephone calls to verify their contact information. We identified a total of 500 potential respondents; inclusion was limited to one potential respondent from each company. After the initial survey was sent to them, up to three follow-up phone calls were made to each person.

In the survey, we asked the respondents to indicate if their company is a returns management software user or vendor. User companies are in the best position to evaluate the effectiveness of returns management software; thus only software-user companies are included in our analysis. Furthermore, because the current study focuses on externally developed customizable software, we asked respondents to identify the sources and types of the returns management software used. In line with our previous discussion, the vast majority of the responses indicated they acquired software externally with the capability of being customized.

During the data collection period, a total of 154 responses from user companies were received. Of these, 25 questionnaires were eliminated for the following reasons: (1) too much missing data (9); (2) the respondent did not hold a qualifying position within the company (i.e., not in manager or director positions) (6); or (3) the responding company did not use purchased customizable software (10). This resulted in 129 usable responses, representing a 25.8 percent response rate (129/500). Among these responses, 32 are mail responses and 97 are online responses.

All items were submitted to t-tests to compare mail and online responses. The results showed no significant difference, which is in line with Boyer et al. (2002) finding that print and electronic surveys generate statistically similar results in terms of reliability and validity and that the two methods are largely interchangeable. Therefore, it was determined that all usable responses could be analyzed as a single dataset. Our targeted sampling approach and data filtering process helped to assure that all 129 respondents held returns management positions at the manager or director level. On average, they had been in their current position for 4.7 years. Thus, the respondents' job titles and experience ensured that they are knowledgeable and qualified informants about their companies' returns management operations.

All respondents are from US-based organizations. In terms of industry type, the final sample has good representation across different industries with 66 (51.2%) of the responding firms being manufacturers, 38 (29.5%) being retailers, and the remaining 25 (19.3%) being wholesalers. The average annual sales volume of the responding companies for the most recent year was $545 million, ranging from $46 million to $75.4 billion. Responding companies had 8,450 employees on average with a range from 687 to 135,000. Regarding the years their companies had been using the current returns management software, the respondents reported an average of 3.9 years of usage, with the minimum being 1.5 years and maximum being 8 years.

Potential nonresponse bias was examined with two approaches. One commonly used means is to compare early and late responses; it is assumed that late respondents are more characteristic of nonrespondents than early respondents (Armstrong and Overton 1977). A multivariate t-test of all items was used to compare the early 25 percent and the late 25 percent respondents; no significant difference was shown. Second, 20 nonrespondents were randomly selected and sent an abbreviated version of the survey (Lohr 1999; Mentzer and Flint 1997). Follow-ups were made to ensure that all completed this survey. The comparison between respondents' answers and nonrespondents' answers using t-test yielded no significant difference. Therefore, it was concluded that nonresponse bias was not a concern.

Measurement Scales

The final questionnaire was comprised of multi-item measures either adapted from existing scales or developed as necessary to evaluate the constructs of interest (Churchill 1979). All survey items used a seven-point Likert-type scale. Detailed information about these measurement items and related basic descriptive statistics are provided in the appendix, and table 1 presents the construct correlation matrix along with relevant construct statistics.

As discussed previously, we propose that the joint development orientation is a second-order construct that consists of three dimensions: top management support of returns management, shared vision between user and vendor, and joint development process. Top management support of returns management within responding firms was assessed using items adapted from Min and Mentzer (2004). These items were anchored at 1 = strongly disagree, and 7 = strongly agree. The range of means for the measurement items of top management support was 3.64 to 4.33, reflecting that most firms still do not view returns management as a strategically important area and do not provide a high level of management attention. Shared vision between returns management software user and vendor was assessed using items adapted from Min and Mentzer (2004). The means for the three items ranged from 3.90 to 4.31 (1 = strongly disagree, and 7 = strongly agree). In general, the results indicate that software users and vendors do not share a high level of common understanding and shared goals regarding returns management operations. The measures for returns management software joint development process were adapted from Svendsen et al. (2011). The mean responses ranged from 4.03 to 4.29 (1 = strongly disagree, and 7 = strongly agree), indicating only a moderate level of joint development process in use among responding firms.

Since no appropriate existing measure was identified during review of the literature, the returns management software effectiveness scale was developed following the approach suggested by Churchill (1979). First, relevant literature was reviewed and utilized as the foundation to capture the essence of returns management software effectiveness with the new scale. The literature search included both trade publications on returns management and academic literature on similar topics. An initial list of 15 items was generated based on our literature review and synthesis. Then, in-depth interviews with eight experts in the field of returns management provided further up-to-date inputs. The experts' inputs were used to consolidate and modify the initial item pool and also to identify additional items. A total of eight potential items were identified. These eight scale items were then subjected to an exploratory factor analysis, and the results suggested the final use of six items (as shown in the appendix). The means for these items ranged from 4.67 to 5.07 (1 = strongly disagree, and 7 = strongly agree), indicating moderate levels of returns management software effectiveness.

Firm market performance was measured using items adapted from Claycomb, Droge, and Germain (1999) and Jaworski and Kohli (1993). Respondents were asked to indicate the performance of their firms in the past year compared to the performance of their major competitors in certain areas (1 = much worse, and 7 = much better). The mean values for the four items ranged from 4.98 to 5.19, indicating a slightly better market performance for the respondents relative to their major competitors.

Like most empirical studies, the current study collected data on both independent and dependent variable data from the same respondents. Therefore, tests were conducted to assess common method bias. First, the correlation matrix did not have any highly correlated factors, whereas evidence of common method bias should have resulted in extremely high correlations (> 0.90). Second, Harman's one-factor test was performed by including all items in a principal components factor analysis (Podsakoff et al. 2003). No single factor accounted for most of the covariance, suggesting that common method bias is not a problem in the current study. Third, another analysis prescribed by Podsakoff et al. (2003)--a partial correlation method--was performed by adding the highest factor from a principal component factor analysis into the partial least square (PLS) model as a control factor for all dependent variables. However, in our study this factor did not significantly increase the variance explained in any of the dependent variables, once again indicating that common method bias is not a significant problem in the data (Podsakoff et al. 2003). Collectively, the results of these tests suggest that common method bias is not a major concern.


Prior to statistical analysis, a basic analysis of the collected data was conducted covering incorrect coding, item normality (skewness and kurtosis), means, standard deviations, and outliers, yielding acceptable results (Mentzer, Flint, and Kent 1999). As discussed previously, we propose that joint development orientation is a higher-order construct that consists of three dimensions: top management support of returns management, shared vision between user and vendor, and joint development process. Following the procedures used by Carter and Jennings (2004) and Chen, Daugherty, and Roath (2009), we first used confirmatory factor analysis (CFA) to assess the measurement model of the joint development orientation construct at the first-order factor level (Anderson and Gerbing 1988). Then, a second-order CFA was used to validate the three dimensions of the second-order construct. Next, the overall measurement model of constructs displayed in figure 1 (above) was assessed. Last, a structural equation modeling was used to test the hypotheses proposed. The primary statistical tools used for analysis include SPSS 20 and AMOS 20.0.

Validating Joint Development Orientation as a Higher-Order Construct

The three proposed dimensions of joint development orientation were first subjected to a first-order CFA using maximum likelihood estimation (MLE) with the purpose of ensuring reliability and validity (Gerbing and Anderson 1988). All latent variables were allowed to correlate with each other. Because chi-square is sensitive to sample size--its value tends to be substantial when the sample is large (Byrne 2001; Joreskog and Sorbom 1993)--fit indices examined include chi-square/degree of freedom ratio (CMIN/DF), comparative fit index (CFI), Bender and Bonnett's (1980) non-normed fit index (NNFI), and root mean square error of approximation (RMSEA). The relative chi-square value (CMIN/DF) of 2.830 falls into the recommended range of 3 to 1 (Carmines and McIver 1981). The current model has a CFI value of 0.949, above the suggested 0.9 threshold value (Bender 1990). In addition, Bender and Bonnett (1980) suggested a value of NNFI above 0.9 also indicates a good fit. Our model has a NNFI of 0.925, which is also above the suggested value. The RMSEA value of 0.072 is within the suggested range (lower than 0.08) for good model fit (Browne and Cudeck 1993). These indices indicate a satisfactory fit.

Then, CFA results were used to examine the constructs' unidimensionality and validity. Standardized regression weights showed that all items loaded on appropriate factors (constructs) as expected. All factor loadings exceeded 0.5, indicating content validity (Bollen 1989). The critical ratio (CR) was obtained by dividing the estimate by its standard error; CR tests the null hypothesis that, in the population, the regression coefficient is zero. All regression weights have a significance level lower than 0.05 (CR > 1.96), supporting the unidimensionality and convergent validity of the constructs (Gerbing and Anderson 1988). Discriminant validity was assessed by testing the chi-square difference between one- and two-factor models with respect to all pairs of measures (Anderson and Gerbing 1988). All chi-square differences were significant (p < 0.001), indicating the proposed three-construct model has a better fit with the data and supporting the discriminant validity of the constructs. As an additional test, average variance extracted (AVE) was also calculated. AVE values of all the constructs exceeded the shared variances between each pair of the constructs, again supporting discriminant validity (Fornell and Larcker 1981).

Cronbach's alpha values were calculated and all values well exceeded the suggested 0.7, demonstrating a high level of reliability (Nunnally 1978). The constructs' reliability was further tested with the approach recommended by Fornell and Larcker (1981), which does not assume all loadings are the same. Again, all composite reliability values were well above the suggested 0.7 (Flair et al. 1998). In sum, the proposed constructs demonstrated a high level of internal consistency reliability. Together, above results support the overall reliability and validity of the scale items used to measure the hypothesized dimensions of joint development orientation.

Next, the three dimensions were subjected to a second-order CFA resulting in satisfactory fit indices: CMIN/DF = 2.830, CFI = 0.949, NNFI = 0.925, and RMSEA = 0.072. All regression weights associated with the three first-order construct paths are significant at 0.001, suggesting that they are dimensions of the first-order joint development orientation construct. Therefore, H1 a, b, and c are supported.

Overall Measurement Model Assessment

To test the overall measurement model, the three dimensions of joint development orientation were analyzed along with returns management software effectiveness and firm market performance (see Chen et al. 2009). The analysis of the overall measurement model yielded CMIN/DF value of 1.693, CFI value of 0.940, NNFI value of 0.929, and RMSEA value 0.074 respectively (table 2). All regression weights are highly significant at 0.05 level (CR > 1.96), with the lowest CR value equal to 8.338, supporting the unidimensionality and convergent validity of the constructs (Gerbing and Anderson 1988). Again, discriminant validity was supported by the significant chi-square differences between one- and two-factor models with respect to all pairs of measures and the AVE values of all the constructs higher than the shared variances between each pair of the constructs (table 1) (Anderson and Gerbing 1988; Fornell and Larcker 1981). In summary, all the scales--including the newly developed measures--are reliable and valid, and an excellent fit exists between the measurement model and the data.

Structural Model Testing

The structural model yields satisfactory key model fit indices with chi-square = 283.840 (df = 165, p < 0.001}, chi-square/df (CMIN/DF) = 1.720, CFI = 0.936, NNFI = 0.926, and RMSEA = 0.075. AMOS outputs on paths' standardized regression weights with relevant critical ratios (CR) and p-values were then examined to test the hypotheses. H2 examines the causal relationship between a firm's joint development orientation and returns management software effectiveness and is supported with standardized regression weight = 0.846, CR = 5.110, and p < 0.001. Also, H3 test results (standardized regression weight = 0.772, CR = 7.784, and p < 0.001) confirmed the positive impact of returns management software effectiveness on a firm's market performance (table 3).


Our research focuses on a critical business issue--returns management. As previously discussed, the volume of returns is likely to continue to grow with increased e-commerce and omni-channel retailing. The fact that UPS dubbed January, 6, 2015, as "National Returns Day" underscores the issue (24/7 Staff 2015). The returns issue may peak during the post-holiday season; however, it is clear that more and more companies will face the increasingly important returns management challenge on an on-going, year-round basis.

Companies have limited options for returns management strategies. They can make returns policies more restrictive, but that option has potential for making a negative impact. QVC, the online mega-retailer, sent an e-mail to "valued customers" in February 2015 regarding changes in shipping and handling charges. The changes included modifications to returns policies. Specifically, they stated shipping and handling charges would be returned for defective merchandise and inaccurate shipments. However, they stated that "if you return an item for any other reason, we will no longer refund the original S&H." This represents a major policy change. They run the risk of experiencing pushback from customers and lost sales. Only time will tell if they (QVC) experience pushback from customers. Past research and trade publications have indicated that liberal returns policies are generally considered necessary and impact customer satisfaction as well as repurchase intentions (Berman 2011; Burnson 2014; Janakiraman and Ordonez 2012; Richey et al. 2005). More restrictive policies may be risky and are likely to reflect competitive and economic conditions at a specific point in time. However, handling returns cannot be avoided and will continue to be a necessary condition of doing business.

In light of this, we propose that better management of returns is critical and that effective software can be used as a strategic tool. Not only can effective returns management software be used as a blueprint to help firms better plan the returns processes, it will also ensure information accessibility. Such information support has important performance implications. First, access to the information can allow better monitoring and control of returns. And, second, it can allow quicker response (such as faster authorization and issuance of credit), which can directly influence customer satisfaction and customer retention.

From an academic perspective, previous work relating to returns has addressed the returns process, disposition methods, reclaiming value, and avoidance of returns (Rogers et al. 2002; Stock 1998; Stock and Mulki 2009). These issues are important; however, firms must develop the means to better manage returns. Software is not the only way to achieve improvements, but it can be considered foundational. Firms must be able to accurately track returns, etc. As noted by Terry (2014), better use of data is at the heart of many returns management initiatives. Terry goes on to say that pertinent data goes beyond information about the returned item to pinpoint root causes of returns, to guard against fraud through proper verification, and to allow users to make intelligent decisions. Consistent with the S-D logic, we have proposed the value of externally developed customizable software is providing appropriate data.

Customizable software means that the software has the potential to be modified to meet specific customer requirements. In other words, the software may have various components or features configured according to user firms' needs. Returns management consists of many processes that companies may handle differently, prompting the need for different software content or capability. Some user firms may only need basic functions to handle claims, RMA, returns receiving, warranty management, repairs, or dispositions, but others may want the software to handle extra processes such as refurbishing or refund processing. Some user firms may have special requirements in terms of real-time information sharing across different functional areas; others may emphasize the compatibility of the software with their existing forward logistics systems (such as warehouse or inventory management systems). Specific needs can vary significantly. Early involvement of users in the development process could have positive impacts.

In the current study, our focus is placed on adoption of an orientation or business philosophy that embraces a collaborative approach to designing returns management software in order to match the needs of users. We introduced the concept of a joint development orientation reflecting the tactical activities needed to strategically support joint development initiatives. Joint development orientation is proposed as including three dimensions--top management support of returns management, shared vision (between user and vendor), and a joint development process. Each of these three dimensions represents a resource. Hypothesis (H1) testing validated our conceptualization of joint development orientation.

For user firms, top management support of returns management is indicative of the relative importance the organization places on development of effective returns management. Returns management must be given adequate support. Adequate support may seem like a lukewarm endorsement, but--as Stock and Mulki (2009) note--too often organizations do not realize the critical nature of product returns. Awareness of its importance is critical. Shared vision, the second dimension of a joint development orientation, refers to consistency or agreement in users' and vendors' expectations. More specifically, shared vision means they have common goals regarding what is needed to support returns management operations, there is a clear understanding of the procedures required for handling product returns, and the two parties are involved in standardizing returns management practices and operations. Software vendors must fully understand how the software will be used and what is needed. This understanding influences the individual features that they develop and offer to users. A joint development process is the final dimension of a joint development orientation. With a joint development process, software vendors actively seek input from users, include the potential users in development through participatory methods and prototyping, and generally value the inputs, ideas, and suggestions of users.

How does this all work to influence the actual effectiveness of returns management software? We proposed a conceptual model based on the S-D logic examining externally purchased customizable software. The software is not customized for a specific company but can be configured to meet individual demands through the grouping of programs or features that the software vendors make available. With a high level of joint development orientation, software vendors can gain insights as to specific needs and develop focused solutions to meet those needs through interactions with potential users. Effective software can help the customer address issues such as damage control (e.g., tracking and collecting recalled products), improving customer satisfaction levels (e.g., faster handling and credit authorization for returned items), value reclamation (e.g., identifying location and quantity of products being returned for recycling), and general problem identification (e.g., recurring mispicking/misshipment or damage in transit). Our H2 analysis results supported the notion that a joint development orientation directly impacts returns management software effectiveness.

Returns management software effectiveness allows user firms to gain control and develop a prioritization of responses. What customers or products should be singled out as critical? What warning systems can be built into the system to call attention to problems and focus efforts on developing a response? Returns management software effectiveness also has important relationship implications. What is the proper level of information sharing across company borders? How should returns management responsibilities be allocated across different supply chain members? While the current study centered on returns management software, the ultimate intent is to identify means for improving overall returns management performance. Many companies still struggle with challenges related to returns management. Finding an effective tool set can be critical. Returns management software is such a tool with the potential for significant impacts. The importance of choosing appropriate returns management software cannot be overemphasized and ensuring software effectiveness has substantial far-reaching implications. Our study confirmed the significant positive link between returns management software effectiveness and a company's market performance (H3).

Limitations and Future Research

Our research provides important preliminary insights. As previously discussed, our research contributes through managerial implications as well as theoretical/academic implications. In sum, our results support the need for managers to consider the importance of closeness between the returns management software vendors and users, as well as the opportunity for greater effectiveness depending upon resource assignments. The theoretical implications relate to how the internal and external resources come together through adoption of a joint development orientation to create an additional operant resource in the form of returns management software effectiveness.

However, we must acknowledge limitations related to our study. Two constructs in the proposed conceptual model involve both software users and vendors: shared vision and joint development process. We only captured the user perspective. It can be expected that dyadic data including the vendor perspective can generate more meaningful and valid results. Our research focuses on externally developed customizable returns management software. It would be valuable to explore the development of the software in-house, including the critical success factors. Our study suggests that returns management software effectiveness is directly impacted by a joint development process. However, we acknowledge that other factors might also be relevant in this process, and we encourage further research to validate and expand our conceptual model.

Despite the above-mentioned limitations, we believe our research makes an important contribution to the existing literature and business practices by focusing attention on a critical area--helping to manage the product returns process. At a time when returns are increasingly becoming a consumer priority (particularly in e-commerce), it is more important than ever to prioritize returns management. Collaborative efforts between software users and vendors, working together in joint development, can produce software to effectively manage the complexity of returns and keep customers happy. Additional research is warranted to further develop and empirically test the newly introduced construct, joint development orientation. However, we believe our research provides an important building block to further the understanding of how returns management tools can be tailored to specific needs.

Haozhe Chen

Corresponding Author

Iowa State University

Patricia J. Daugherty

Michigan State University

Angela L. Jones

Michigan State University


Table A1/Construct Measurement Descriptive Statistics

Constructs and Measurement Items                            Mean  Dev.

Top Management Support (Cronbach's alpha = 0.938; composite
reliability = 0.939) (a)

  Please indicate your level of agreement with the
  following statements regarding your company's top
  management's support of returns management.

    TMS1. Our company's top managers repeatedly tell        3.64  1.87
    employees that our business' success depends on our
    returns management programs.
    TMS2. Our company's top managers repeatedly tell        4.10  1.84
    employees that returns management related information
    sharing is critical to our business success.
    TMS3. Our company's top managers repeatedly emphasize   4.33  1.91
    that returns management should be a critical part of
    our performance measurement.

Shared Vision between User and Vendor (Cronbach's alpha =
0.877; composite reliability = 0.883) (a)

  Please indicate your level of agreement with the
  following statements regarding the shared vision on
  returns management between RM software vendors and your

    SVV1. RM software vendor and our company have common,   4.27  1.43
    agreed-to goals for returns management.
    SVV2. RM software vendor and our company both clearly   4.31  1.46
    understand the procedures involved in returns
    management operations.
    SVV3. RM software vendor and our company both are       3.90  1.36
    actively involved in standardizing returns management
    practices and operations.

Joint Development Process (Cronbach's alpha = 0.888;
composite reliability = 0.889) (b)

  Please indicate your level of agreement with the
  following statements regarding the development process
  of RM software used by your company.

    JDP1. RM software vendor conducted observations,        4.29  1.13
    interviews, visits, discussion, and surveys with our
    company to identify our needs for RM software.
    JDP2. RM software vendor made great efforts (such as    4.03  1.10
    lead user approach, experience prototyping,
    scenario-based design, participatory methods, etc.)
    to have our company involved in RM software
    development process.
    JDP3. RM software vendor highly value our company's     4.08  1.22
    inputs (such as ideas and suggestions) in the RM
    software development process.

RM Software Effectiveness (Cronbach's alpha = 0.904;
composite reliability = 0.906)

  Please Indicate your level of agreement with the
  following statements regarding your company's RM
  software effectiveness.
    Our company's RM software has done a great job in
    helping us
    RSE1. to develop secondary channels.                    4.74  1.23
    RSE2. to achieve fewer markdowns.                       4.73  1.27
    RSE3. with asset recovery.                              5.07  1.28
    RSE4. to reduce customer service costs.                 4.82  1.09
    RSE5. to reduce freight, receiving, handling, and       4.76  1.47
    RSE6. to improve asset utilization and                  4.67  1.23

Firm Market Performance (Cronbach's alpha = 0.884: composite
reliability = 0.886) (c)

  Please compare your firm's market performance in the
  last year to major competitors.

    Our firm's market performance in last year
      compared to major competitors
    FMP1. Overall firm competiveness                        5.19  1.05
    FMP2. Sales volume                                      5.19  1.17
    FMP3. Profit margin                                     5.11  1.17
    FMP4. Return on investment (ROI)                        4.98  1.16
    FMP5. Customer satisfaction                             5.12  1.25

(a) Source: Min and Mentzer 2004.

(b) Source: Svendsen et al. 2011.

(c) Sources: Claycomb, Droge, and Germain
1999; Jaworski and Kohli 1993.


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Table 1/Construct Correlation Matrix

Construct                TMS       SVV       JDP       RSE      FMP

Top Management            1
Support (TMS) (a)

Shared Vision w/       .279 **      1
Software Vendor
(SVV) (a)

Joint Development      .382 **   .440 **      1
Process (JDP) (a)

Return Management      .446 **   .314 **   .651 **      1
Effectiveness (RSE)

Firm Market            .399 **   .377 **   .613 **   .682 **     1
Performance (FMP)

Mean                    4.023     4160      4.132     4.798    5.119

Standard Deviation      1.765     1.268     1.040     1.041    1.005

AVE                     .837      .718      .728      .646     .608

(a) 7-point Likert-type scale: (1) Strongly disagree to (7)
Strongly agree.

(b) 7-point scale: (1) Much worse to (7) Much better.

** Correlation Is significant at the 0.01 level (2-tailed).

Table 2/Measurement Model Results

Measurement Items    Standardized Weight   Critical Ratio

TMS1                        0.900             (Fixed)
TMS2                        0.970              18.337
TMS3                        0.871              14.757
SVV1                        0.787             (Fixed)
SVV2                        0.958              11.086
SVV3                        0.786              9-772
JDP1                        0.859             (Fixed)
JDP2                        0.848              11.773
JDP3                        0.853              11.867
RSE1                        0.797             (Fixed)
RSE2                        0.810              10.187
RSE3                        0.817              10.303
RSE4                        0.809              10.176
RSE5                        0.786              9.796
RSE6                        0.692              8.338
FMP1                        0.815             (Fixed)
FMP2                        0.778              9.698
FMP3                        0.787              9.851
FMP4                        0.751              9.262
FMPS                        0.767              9.521

Fit statistics: chi-square = 270.829 (df = 160, p < 0.001),
CMIN/DF = 1.693, CFI = 0.940, NNFI = 0.929, RMSEA = 0.074

Table 3/Hypothesized Paths Testing

Path                        St. Weight    CR        p        Note

H2: Joint Development         0.846      5.110   < 0.001   Supported
Process [right arrow] RM
Software Effectiveness

H3: RM Software               0.772      7.784   < 0.001   Supported
Effectiveness [right
arrow] Firm Market

Fit statistics: chi-square = 283.840 (df = 165, p < 0.001),
CMIN/DF = 1.720, CFI = 0.936, NNFI = 0.926, RMSEA = 0.075
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Article Details
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Author:Chen, Haozhe; Daugherty, Patricia J.; Jones, Angela L.
Publication:Transportation Journal
Article Type:Abstract
Geographic Code:1USA
Date:Jan 1, 2016
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