978-1-61284-952-2/11/$26.00 ©2011 IEEE 944 IEEE Int'l Technology Management Conference

Logistics Technology Transfer Process Model

Abdullah S Al Hajri Sultan Qaboos University, Department of Operations Management and Business Statistics

PO. Box. 20, Al Khod, Muscat 123, Oman aalhajri@squ.edu.om

Maruf Hasan University of New South Wales, School of Mechanical and Manufacturing Engineering

Abstract

A consecutive number of studies on the adoption trend of logistics technology since 1988 revealed that logistics organizations are not in the frontier when it comes to adopting new technology and this delayed adoption creates an information gap. Given the integrative nature of logistics technology, failure to implement the technology successfully could result in writing off major investments in developing and implementing the technology or even in abandoning the strategic initiatives underpinned by these innovations. Consequently, the need to employ effective strategies and models to cope with these uncertainties is crucial. This research addresses the aspect of uncertainty in implementation success by process research models. Process research approach focuses on the sequence of events in the technology transfer process that occur over time. Through the use of optimal matching from natural science and cluster analysis, this research revealed four patterns that organizations follow when transferring logistics technology namely, formal minimalist, mutual adaptation, development concerned, and organizational roles dispenser. Analysis of the relationship between these patterns and success pointed to a set of crucial and necessary events which differ from one pattern to another.

Introduction Unlike other business fields, logistics has gone through

several major redefinitions of its role in organizations [1-3] starting with a fragmented approach to an integrated to a strategic to a supply chain orientation. Along this line of development, several technologies in the logistics and supply chain area have been developed to satisfy the requirements of each stage. Robeson [4] predicted that in 1995, logistics managers would spend their time dealing with issues related with computers and information processing. Later, Dawe [5] stated that logistics organizations are not in the frontier when it comes to adopting new technology and this delayed adoption creates an information gap. Nilsson [6] found that one of the main sources of uncertainty that faces logistics managers is concerned with technology implementation and understanding. Due to the growing complexity of the developed information systems in the logistics area, implementation of these systems becomes expensive, risky, and difficult to achieve [7]. Consequently, the need to employ effective strategies and models to cope with these uncertainties is crucial. Organizational innovation literature offers three ways to study the adoption and implementation of innovations in organizations; diffusion of an innovation, organizational innovativeness and process theory [8]. Diffusion of an innovation refers to its spread through a

population of potential adopters. This stream’s objective is to explain or predict rates and patterns of innovation adoption over time and/or space [9, 10]. Organizational innovativeness investigates the determinants of an organization’s propensity to innovate. It has been operationalized as the composite score based on the number of innovations adopted by an organization or as the yes/no adoption decision [11]. Process theory investigates the nature of the innovation process; how and why innovations emerge, develop, grow, and perhaps terminate [12, 13]. It places a premium on the temporal sequence of activities in the development and implementation of innovations. Little research has been devoted to process theory compared to the other two streams. This research adopts process theory to the study of logistics technology1 adoption and implementation.

Process view of innovation has theoretical as well as practical benefits. Theoretically, it’s useful for describing and explaining a broad class of events and sequences central to innovation generally. Additionally, it helps in classifying the contribution of other studies of innovation according to the relevant stage(s) within the innovation process. Further, it aids in building theoretical models for the individual processes within the overall layout as well as models for ultimate implementation success for innovation. Practically, the process-view of innovation acts as a roadmap for managers by giving them a clearer picture of the activities that constitute the innovation process. Also, it helps management anticipate the extent of their control over the process and reduces the randomness associated with innovation management [14].

Review of relevant literature Process theory ascertains that temporal sequences of

events for a given phenomenon can follow a certain number of generic paths or patterns [15]. Wolfe [8] differentiated between two generations of process models; first generation stage models (SM) which conceptualizes innovation as series of events that unfolds over time and process research (PR) which involves in-depth research conducted to fully describe the sequences of, and the conditions which determine, innovation processes [16].

Scholars of stage model research acknowledge that the innovation process is continuous in nature [17]. However, they observed that there are repeated stages that are involved while adopting innovations. These stages overlap and differ in length. Researchers differed as too how many stages are

1 Logistics technology is a technology which is used in

part or whole by logistics/supply chain managers to perform tasks directly related to logistics.

involved. Thompson [18] viewed all technology transfer process efforts as consisting of a sequence of three processes (initiation, adoption, and implementation) (table 1). Other researchers of stage models concurred with Thompson, but

they argued that this three stage model may have overlooked the importance of some pre-adoption and post-adoption evaluation processes.

Table 1: Stage models

The application of these stage models to the actual technology transfer process has shown moderate support. Ettlie and Vellenga [20] and Ettlie [21] used Rogers [9] model to study the transfer process of some transportation innovations. Before presenting the model to the companies, they added one stage to the model (i.e. implementation stage) to make it a six-stage model and the results were about 60% supportive. Payton and Ginzberg [22] used Cooper and Zmud’s [23] six stage model to understand the Inter-organizational health care systems transfer process. The authors concluded that the model is able to capture some aspects of the health care system implementation and seems best suited to less competitive market situations. King [24] compared between Zaltman’s model which depicts the transfer process in a series of discrete stages and Schroeder et al.’s [25] model which shows the process more fluid, without a fixed sequence of stages. They found that inter-rater reliability is higher for Schroeder's model, while overall support across raters tends to be stronger for the individual stages of Zaltman's model. However, there are problems with the sequence of stages in the latter model.

The conclusion that the above studies have come to is that one stage model can’t describe the different situations in which technologies are implemented. While stage models have better ability to describe the individual stages, their ability to capture the sequence of the stages in the actual transfer process is weak. The recognition of these limitations has led scholars to investigate the type of patterns available and the theoretical contingencies which could be used to explain the different patterns observed.

After studying the adoption of three innovations at 18 different sites, Pelz and Munson [26] and Pelz [27, 28] found that for simple innovations that are copied with little change the succession of stages will be moderately clear; but for organizational innovations that are locally developed or those that are complex or uncertain, the staging sequence will appear more disorderly. Sabherwal and Robey [29] studied the transfer process of various information system projects in 53 companies which were collected by students as part of

their assignments. Using optimal matching and cluster analysis, the authors arrived at 6 patterns of sequences. The patterns are logical minimalist, traditional off-the-shelf, problem-driven minimalist, textbook lifecycle, outsourced cooperative and in-house trial and error. In a similar study, Mintzberg et al. [30] studied the strategic decision processes of 25 decision projects which were compiled by students over 5 years. The authors found seven decision patterns which explained the different sequences of the decision processes. However, each pattern is composed of slightly different activities, depending on several things among them whether the project starts with recognition of a problem, an opportunity or a crisis and whether the solution is ready-made, custom made, or modified. Rijnders and Boer [31] studied the transfer process of Continuous Improvement (CI) philosophy in 26 companies. Four typologies or patterns of implementation were identified namely, exercisers, sprinters, novices, and stayers. Newman and Robey [32] developed a process model for user-analyst relationships in information system development that depicts the transfer process into encounters and episodes. Episodes refer to a set of events that stand apart from others, whereas encounters are shorter in time and mark the beginnings and ends of episodes. In other words, episodes are separated by encounters between analysts and users. Analysts refer to management information systems department and users refer to the users of the technology.

The relationship between patterns and implementation success has been of interest to some researchers of process theory. Pelz [28] examined the effect of the identified patterns on the effectiveness of implementation outcomes. He observed that the implementation of technology was effective within those patterns which started with recognition of a problem, but not with other patterns. Nutt [33] examined implementation tactics used by managers in various projects. He found that managerial intervention tactics (i.e. key executives justify need for change) and complete and token participation tactics were the most successful ones in terms of adoption rate of investigated projects. However, the author

AUTHORS 1 2 3 4 5 6 7 8 [18] Initiation Adoption Implementation

[9] Agenda-setting

Matching Redefining/ restructuring

Clarifying Routinizing

[12] Initiation Adoption Adaptation Acceptance Routinization Infusion

[13] Knowledge-awareness

Formation of attitudes

Decision Initial implementation

Continued-sustained implementation

[19] Basic research

Technology development and testing

Diffusion of information

Adoption Implementation Assessment of outcomes

Routinization

[10] Awareness Matching Selection

Adoption Implementation Routinization

indicated that other tactics (i.e. persuasion, edicts) could be successful in other occasions if necessary conditions can be available. Sabherwal, Jeyaraj and Chowa [34] argued that their data didn’t permit them to measure project success independently from the events described (i.e. project success was one of their events) and therefore didn’t examine this relationship. Additionally, they speculated that choice of pattern doesn’t determine the success or failure of a project, and that any type can be successful. This is consistent with process theory which asserts that sequences provide the necessary conditions for implementation success [35]. Therefore, successful implementation of logistics technology can be perceived as equi-final (i.e. several sequences may lead to implementation success) [36, 37].

Research questions Prior research recognized the importance of studying the

patterns of sequences in the innovation process early in literature [30]. Gopalakrishnan and Damanpour [14] proposed that adoption of simple innovations, and adoption of administrative innovations can be adequately explained by the unitary sequence pattern. However, adoption of complex innovations, and adoption of technical innovations can be better described by the multiple sequence pattern. Due to their integrative nature, logistics technology is considered complex and therefore, multiple sequence pattern can better describe their technology transfer process. Hence, this study aims to answer the following research question:

Question 1: What are the patterns of sequences of the different logistics technology transfer processes?

Implementation success has received significant attention in literature, but has been studied mostly using the factor-based approach. Process theory offers a different methodology for modeling the relationship between process stages and implementation success. Process theory asserts that each event in the process is necessary, but not sufficient to cause a certain outcome. To make this clear, Salancik and Leblebici [38] developed a process model for food service transactions and in one of their sequences, the transaction states that a meal must be cooked before it is eaten. Note that this sequence does not obligate anyone to eat a meal just because it has been cooked. Rather it makes it necessary for a food to be cooked for someone to eat it. In contrast, factor-based models (or variance models) state that for some population of interest, if all other things are equal, variance in any one of the independent variables is necessary and sufficient to cause variance in the dependent variable(s) [See 32, 35, 39 for discussions about process and variance models] . A study on the relationship between process sequences and implementation success should, therefore, investigates the necessary conditions for implementation success. Hence, necessary conditions for successful implementation should be designated for each identified pattern.

Question 2: What are the necessary conditions for implementation success in each pattern?

Methodology and Data Collection

Data Collection The domain of this study is derived from the logistics

technology adoption and implementation studies published in literature. The use of literature to answer the above questions requires the use of content analysis as a guiding methodology. Content analysis is defined as a research technique for making replicable and valid inferences from texts (or other meaningful matter) to the contexts of their use [40]. Others defined content analysis as a set of research tools for the scientific study of written communications with the objective of determining key ideas and themes contained within them [41].

Logistics technology literature encompasses several and varied studies. Some studies examine the relationship between a set of factors and implementation success whereas others examine the role of logistics technology in improving the overall performance of the company. Relevant text for this study is captured by the single and multiple case studies which have their main research question as the depiction of the process of logistics technology transfer. In multiple case studies, several companies are investigated. Hence, the logistics technology transfer of each company represents a separate observation. Some multiple case studies don’t put emphasis on the details of the technology transfer story for each company. These case studies are not considered relevant and therefore are not included.

The search for the relevant refereed articles from literature is bounded by a set of rules which help prevent the inclusion of irrelevant material. Two general search rules were used to arrive at the list of relevant articles for this study; journal search and guided search. In the journal search, logistics and supply chain management journals were identified through an advanced scholar search in Google. Specifically, logistics and supply chain terms were input separately in the scholar search. Each term was entered twice; one is in the “publication” field and the other is in the “with all the words” field. This search resulted in a huge list of articles. After going through several pages of the results, publication names started to repeat. The search continued until full replication of publication names was observed. The list of logistics and supply chain journals was then searched in the e-journals’ section of the online library. The identified journals through this journal search procedure were investigated in detail. Since logistics and supply chain articles can be published in other journals which don’t have the terms logistics and supply chain on their titles, journals which are familiar to the authors were identified and searched.

The guided search used meta-search engines in the online library to find the articles that match either of two predefined search rules. The databases searched are Academic Research Library, Business Source Premier, Expanded Academic, OmniFile, Social Sciences Index, and Web of Sciences. The first rule used is a search for Technology ‘and’

Implementation ‘and’ Case Study. Various combinations of the three keywords were used in order to maximize the chances of obtaining relevant articles. The second rule was more formalized. In order to maximize the chances that the

obtained results relate to logistics information technology, the definition of logistics management in the Council of Supply Chain Management Professionals’ [42] website was used.

Each of the activities of logistics management replaced “logistics activity” in the following search rule:

Subject: Logistics activity AND Subject: Technology AND All fields: Implementation

The above search was rewarding and resulted in various specialized logistics technology case studies. The overall search resulted in the technology transfer process of 36 companies. The initial gathering of logistics technology studies resulted in a higher number of case studies. However, the stringent rules of the study allows for a certain type of studies to be included.

Content analysis requires the identification of categories which define units by their membership in a class or category-by their having something in common [40]. For example, a color category can have white, black, green, yellow …etc. Categories must be mutually exclusive and exhaustive. Mutually exclusive refers to the ability of the definition of a collection of categories to make clear distinctions among the phenomena to be recorded. In other words, no recording unit may fall between two categories or be represented by two distinct data points. Exhaustive refers to the ability of the collection of categories identified to represent all the recording units, without exception. In this study, events in the technology transfer process (or adoption and implementation process) should be classified into distinct categories. The initial classification scheme was developed through coding 142 events in seven case studies resulting in 22 categories. The codes or categories of those events emerged naturally after extracting the event sequences from the case studies. The coding scheme was then applied to the seven case studies. During this application process, the scheme was improved through a number of successively refined versions resulting in 22 distinct categories (see table 2).

In content analysis, the classification of events into categories is expected to be reliable or reproducible. This condition is attained through the establishment of inter-coder reliability. At least, one independent coder in addition to the author should classify a sample of events into their designated categories. An independent coder was selected with the appropriate background and the necessary cognitive abilities. The coder was given a sample of extracted events and a copy of the coding scheme and half an hour training session. Examination of these codes resulted in a Krippendorff’s of 80% which is acceptable according to Krippendorff [40].

Patterns Methodology In order to arrive to the patterns from the coded sequences

and subsequently the relationship between those patterns and successful implementation, process researchers in the reviewed literature used varying ways. In Pelz [28], the author and a colleague independently took the 18 event sequence plots in his study and grouped them into patterns that were intuitively meaningful. Through a comparison, the two reached a consensus on the types of patterns. Nutt [33] repeatedly sorted 91 sequences until distinct categories of implementation-related activities emerged. Pentland and Rueter [43] studied the process of handling customer calls in

a 24-hour-a-day technical assistance in a software company using grammatical models [44]. Out of 168 calls, the authors were able to parse (i.e. grammatically interpreting) 109 calls. Sabherwal and Robey [29] used optimal matching technique to develop an empirical taxonomy (i.e. patterns) of implementation processes in information system development. Using 53 student reports, the authors coded the data into event sequences using a coding scheme that consists of 15 codes. Using optimal matching software, the authors obtained minimum distance matrix between all pairs of sequences. This was then fed to a clustering software from which the different patterns were identified.

Among the different methods used by process researchers, Optimal matching provides the most replicable and the least method requiring subjective judgment. Optimal matching is a widely used technique in natural science especially in the analysis of resemblance in DNA sequences. The method was popularized into sociology by Professor Andrew Abbott who used it to identify patterns in careers of musicians active in Germany in the 18th century [45]. Basically, the method starts by measuring resemblance between sequences using a dynamic programming algorithm called optimal matching (sometimes called optimal alignment) [46].

Methodology for successful implementation The relationship between identified patterns and

successful implementation which represents the second research question has rarely been examined in literature. Nutt [33] examined the relationship between the identified tactics that managers use in implementing projects and success. The author defined success as adoption rate not ultimate implementation success2. In drawing the relationship, Nutt calculated the percentage of success in each tactic with the tactic having the highest percentage of success is considered the most successful one. However, the author commented that success of other tactics is limited by the number of cases in each one of them (93% of the cases were from the most successful tactic). Mohr [35] stated that process theory can lead to significant results if one can provide the ingredients plus the recipe that strings these ingredients together in such a way as to tell the story of how a given outcome occurs whenever it does occur. Given that the potential list of ingredients is already known (i.e. the event codes); the analysis should aim to find the recipe. However, there should be a recipe for every pattern, because each pattern is distinctively different from others. In process theory, focal events in each pattern are capable of combining with one another in such a way as to yield the desired outcome [35]. Therefore, the coexistence of these crucial events in each pattern is of paramount importance and further can be used to increase the probabilistic occurrence of successful implementation.

2 We used extended use of technology and accrued

benefits as implementation success measures in this study.

Event Category Examples 1. Internal need Decrease in performance, shortages, outdated systems,

productivity, long term strategy 2. External need Order from headquarters, competition, customer or trading partner

pressure 3. Management interest/support CEO identified successful order fulfillment as one of his top goals

for fiscal 1993; CEO agreed to the solution 4. Forming a team to oversee project

implementation A SCM business team was assigned to take over the project; a

steering committee was appointed 5. Forming a partnership A partnership was formed to find an effective way to solve these

issues; an IS consultancy was hired to conduct ERP project 6. Identifying a solution ERP was found the solution for the above issues 7. Technology shortlisting (includes request for

proposal, evaluation criteria, vendors’ demonstrations) A number of alternative replacement systems were examined

8. Selecting a specific technology (includes software vendor,, methodology or selecting in-house development

A B-50 software was selected; one scenario had been cleared for implementation

9. Project definition (includes, problem definition, project schedule, objectives, team commencement, implementation plan, scope, solution requirements)

The task force produced a three-phase plan; business requirements were defined and project scope

10. Seeking knowledge early in the project Top management of Cummins visited Case Institute. 11. Business process reengineering Process modification, process mapping; business process

procedures development 12. Technology modification/development (includes

developing technology prototype) WINO was programmed in C++ with a spreadsheet interface, SAP

R/3 was modified in the consignment fill-up and inventory areas 13. Training and support (includes, training of super

users and end users, preparing training documents, support)

The team taught participants of the design-change meeting how to use the software; for emergencies a “hot line” phone number was connected to Melbourne to provide branches with more specific information

14. Data clean-up, replacement of legacy systems, data conversion, and hardware

upgrade

Data conversion from legacy systems to SAP; switching legacy systems to ‘view-only mode’

15. Pilot implementation/testing SAP pilot project was conducted at one of Rolls Royce facility; Beta testing with five customers

16. Go live/full scale implementation The system is implemented in inventory area; the system went live 17. Creation of organizational roles A key customer supply clerk was employed by the vendor; the job

of the logistics manager has changed 18. Hiring, firing and resignation of employees A new vice president was hired; the EDT team was dismantled 19. Good implementation outcome (user acceptance,

management satisfaction, performance enhancement Nortel began routine use of the DSS in January 1995

20. Implementation issues (includes problems, abandonment, resistance)

After two years, the system had to be abandoned; senior management referred to typical lament in worker’s attitude to change the way business is conducted; from the end user perspective the implemented ERP solution was unacceptable

21. Post-implementation evaluation (includes budget, completion time, performance measurement)

A final evaluation of the project was conducted.

22. Other (activities not related to the technology implementation project)

The demand for plant’s product increased during the implementation of the ERP system.

Table 2: The classification scheme

Data Analysis Data analysis consisted of three distinct stages:

computation of inter-sequence distances, cluster analysis, and implementation success analysis.

Computation of inter-sequence distances: The distance between any two sequences is measured using the number of

insertions, deletions, and substitutions required to transform one sequence into another and is referred to as total cost of transforming sequence ‘A’ into sequence ‘B’. In optimal matching, it’s necessary to specify values for insertion, deletion, and substitution costs. For substitution costs, the practice is whether to assign similar substitution costs for all

potential substitutions (e.g. substituting code 1 for code 3 = substituting code 5 for code 17) or to assign different values for different transitions. In our event coding scheme, each code is distinctly different from others. Therefore, we assigned a value of 1.0 for all transitions. This assures that all events are treated equally. For the assignment of insertion and deletion costs or indel costs, the sum of insertion and deletion costs (or 2*indel costs) should equal or exceed the substitution cost. Given this, we examined two alternative sets of indel costs: (1) indel cost = 0.5 and (2) indel cost = 1.0. Upon examining the two alternatives, we found that there were no significant differences in the final results of clustering. However, alternative one resulted in a normally distributed distances. Therefore, the final results were based on an indel cost of 0.50. The length of the sequences obviously influences the number of transformations required. A single substitution is relatively more important in a short sequence than it is in a long sequence. To compensate, we standardized by dividing the number of transformations required by the length of the longer sequence [45].

Cluster analysis: After experiencing with different clustering methods, we settled on Ward’s minimum variance method as it provides the most identifiable and separable clusters. The decision on the number of cluster is mostly subjective. However, fusion coefficients and dendrogram are used widely to decide on the number of clusters. Sudden

jumps in fusion coefficients are indicators of existence of mutually exclusive clusters. The agglomeration schedule indicated that the highest jumps were at the two-cluster solution followed by four-cluster, six-cluster, and nine-cluster solutions. The two-cluster wasn’t considered good solution because the it divides all the sequences in two large sets which omits important information within those clusters. We selected the four-cluster solution since it had the second highest fusion coefficient.

Event distribution within clusters should be different from that expected by chance otherwise interpretation of clusters wouldn’t produce meaningful results. Chi-square test was performed by comparing actual frequencies (table 3) with expected frequencies. Expected frequency for each cell in table 3 equals to [(total of cell row * total of cell column)/grand total] [47]. The chi-square value is 183.7832 which is significant at = 0.001 which indicates that clusters differ in terms of event frequencies.

Ideal sequences in figure 1 were developed by looking at table 3 and examining the special features of each cluster. These features were then placed in their ideal positions by aligning (but not changing) the different sequences to reveal these features. The use of ideal sequences was so helpful in extracting the overall pattern in each cluster.

CLUSTER EVENT A B C D Total

Internal need 10 16 6 20 52

External need 8 5 3 7 23 Management support 8 8 8 13 37 Forming a team 18 18 11 16 63 Forming a partnership 7 9 9 6 31 Identifying a solution 19 23 5 21 68 Technology short-listing 4 12 2 9 27 Selecting a specific technology 6 11 3 11 31 Project definition 25 21 28 42 116 Seeking knowledge 2 3 0 8 13 Business process reengineering 5 18 1 4 28 Technology modification/ development 13 16 27 14 70 Training and support 35 24 3 8 70 Data clean-up 5 9 7 8 29 Testing 6 7 8 5 26 Go live 12 25 9 26 72 Creation of organizational roles 1 1 0 10 12 Hiring, firing 0 11 5 5 21 Good implementation 13 9 5 21 48 Implementation issues 3 16 5 19 43 Post implementation evaluation 11 1 1 2 15

Total 211 263 146 275 895

Table 3: Event frequencies for each cluster

Implementation success analysis. Using the identified

patterns, the necessary conditions for implementation success will be operationalized as those events that form the structure of the pattern plus the crucial events that differentiate a given

pattern from others. Graphical depiction of ideal sequences along with use of Excel will be used to derive these conditions.

Figure 1: Event sequences and ideal types

Results

Patterns Type 1- Formal minimalist. This approach to logistics

technology transfer places a premium on project definition,

education and post-implementation evaluation. Table 3 shows that this type has the second least amount of implementation problems. As shown in Figure 1, typical sequences in this type (as summarized in the ideal sequence) begin with an internal problem, but are also characterized by external need

such as competition and customer pressure and then proceed to identifying a solution and forming an implementation team. Often this team starts with little knowledge about technology development and implementation and therefore it is exposed to an appropriate education program. Once the team feels that sufficient knowledge has been acquired, it starts by defining how the project is going to advance. Then, technology is developed or adapted to suit the organization. In some of cases in this cluster, technology is a methodological process which requires further education at the part of the users. Therefore, education in these cases is considered the main form of technology. After implementing the technology, the team formally measures and evaluates the costs and benefits of technology transfer.

According to table 3, team formation has a high frequency of 18. Therefore, selecting the right team members has a direct effect on technology transfer success. The case studies which were classified in this category are [48], [49], [50], [51], [52], [53], [54], [55], and [56] consecutively according to figure 1.

Type 2- Mutual adaptation. This approach is very similar to the one described by Leonard-Barton [37]. Logistics technology adoption and implementations in this pattern tend to focus on simultaneously changing the technology and the organization. However, implementation issues tend to be high and success proves to be difficult. According to table 3, hiring, firing and reassignment is the highest in this group. Throughout the reviewed articles, we found that hiring and firing is one of the most unpredictable actions which could result in success or otherwise in a drastic failure. Typical sequences in this pattern generally start with recognition of a problem, followed by identification of a solution. Then, a technology is selected from a vendor or opted for in-house development. After that, a team is formed which begins by defining the project. Once a project is sufficiently defined, a consultant or a team helps in directing the business process reengineering activities. The technology is then developed or modified to fit with reengineered process. Data cleanup and technology testing usually follow technology modification. Education and training proved to be crucial in this pattern. Two technology transfer projects (34 and 5 in figure 1) eventually failed because they didn’t go through an education program. Several implementation issues arose after educating end users. Some of these were attributed to user resistance to the new system which was alleviated by extra training and continuous support. Other issues arose as a result of users testing the system such as insufficient technology customization. Overall, this pattern was characterized by several Enterprise Resource Planning implementations. The case studies which were classified in this cluster are [57], [58], [59], [60], [61], [62], [63], [64], [65], and [66].

Type 3-Development concerned. The development-concerned approach to logistics technology transfer is heavily occupied by the design and development of the technology. This approach requires the organization either to cooperate with one or more external parties or to depend on the in-house team to develop the technology. Typical sequences in this approach begin with a recognition of a problem or external pressure which captures top management interest. A

gross solution in terms of a need for a system is then identified. Through a cooperative relationship with a university research group or consultants, a joint team is formed which subsequently works its way through a mutual understanding environment. The joint team begins by extensively investigating the internal environment and areas of identified problems. Once an understanding is reached, a project is defined in terms of implementation approach, hardware and system requirements, and time allocation. System design and development takes several stages including system prototyping, small scale system development, and large scale system development. Testing is crucial in this pattern and can be considered part of technology development because an internally developed system can only be defect-free if it can be tested successfully. However, successful testing proved to be not sufficient condition for successful implementation in sequence number 4 [35, 67]. The case studies which were classified in this cluster are [67], [68], [69], [70], [71], and [72].

Type 4-Organizational roles dispenser. In addition to being a problem-driven approach, this type is very likely to end with a new set of organizational roles. This approach is very similar to the problem-driven minimalist in Sabherwal and Robey [29]. As shown in figure 1, the ideal sequence starts with recognition of a problem, identification of a solution, project definition, technology short-listing, technology selection, technology development, implementation and creation of organizational roles. The main characteristics of this type are the high project definition, low emphasis on business process reengineering, and formalized short-listing process. The case studies which were classified in this cluster are [63], [73], [74], [75], [76], [77], [78], [79], [80], [81], and [82].

Successful Implementation Necessary conditions for successful implementation

consist of event sequences plus crucial activities that a logistics technology transfer pattern depends on.

Type 1- Formal minimalist. In this approach, we found that the combination of team formation and intensive education are considered crucial events. In addition to the importance of education after developing the technology in figure 1, formal minimalist requires that education and training be injected after team formation indicating the importance of combining these two events if necessary condition is to be realized. Moreover, post-implementation evaluation emerged as necessary in this pattern. Due to the high responsibility given to the team formed, evaluation is required for success to be communicated.

Type 2- Mutual adaptation. This approach requires five events to be combined in order for necessary conditions of successful implementation to surface. These events are technology selection, business process reengineering, technology customization, testing and education and support. In the absence of any of these conditions, successful implementation can easily be jeopardized. The three failure stories in the corresponding cluster are the result of absence of technology customization and education program. Leonard–Barton [37] found that in production technologies mutual adaptation of business process and technology is

necessary for successful implementation. The adopted analytical methodology was successful in isolating this importance using a separate pattern.

Type 3- Development concerned. This approach revolves around technology design and development. In this pattern, several conditions appeared to be crucial. First, development of technology takes time and effort and only when it’s complete, results can be realized. Top management interest in the problem to be solved is crucial for providing the support and resources required to complete this task. Most of successful companies in this category placed a premium on long term partnerships. These partnerships are important for providing the expertise in problem analysis and technology development. Further, these enduring partnerships proved valuable in expanding technology development and implementation to other departments and branches. Sufficient project definition characterized all technology transfers in this type. This is due to the joint efforts of internal teams and outside partners which requires mutual understanding of project requirements. Importance of pilot implementation and testing can’t be underestimated. Technology development can only be complete if it’s free of problems. After examining case number four [71], we found that the unsuccessful implementation of online logistics system was a result of insufficient development and testing of the technology. Also, the company didn’t have sufficient internal knowledge of the requirements of technology development of an internet system and therefore missed the opportunities of contracting the appropriate expertise.

Type 4- Organizational roles dispenser. This approach to technology transfer covered various transfers of technology ranging from in-house development [81] through enterprise resource planning [75] to inter-organizational technology [80]. However, technology selection and project definition were the most important issues that determined the conditions for successful implementation. Figure 1 shows that technology short-listing and selection received significant attention by those companies. Moreover, project definition received the highest frequency in this type according to table 3. Combination of these two events is crucial for the success of projects in this category. An explanation for this is the large scale nature of those projects. Several of these projects were implemented by large companies which placed high importance in selecting the best scenario or technology vendor. Project definition preceded and succeeded technology short-listing and selection. In the first instance of project definition, the project is grossly defined in terms of global and long term strategies. Based on these strategies and directions, technology vendor is selected. Using a joint team from technology vendor and internal functional managers, the project is defined accurately.

Implications

Research Implications Recently, process theory gained significant attention

among scholars from different disciplines. The aim is to provide complimentary as well as competing views to those offered by variance theory [32]. Complimentary in the sense that variance theory cannot reveal the different ruts that

technology transfer processes follow. Therefore, process theory goes deeply into these paths to find out if there are any certain patterns that companies prefer to take when adopting the different technologies. The competing views are the independent validation of the claims that variance theory make with regards to successful implementation of the technology. Literature is flooded with variance theory studies which examine certain number of independent factors against one or more dependent variables. Process theory offers different theoretical propositions to the undertaking of these investigations.

Due to the rarity of studies in the process school of implementation success, this paper took a watch and walk approach. The authors began by gathering information about process theory in an exploratory way. Realizing that the only possible easy and quick way to approach this field is through literature, a fully leveraged content analysis study was conducted using state of the art concepts from literature. Using strict criteria of admission, it was possible to gather 36 detailed case studies of logistics technology. These case studies along with the event codings and the resulting coded sequences are fully documented within this paper which makes it easy for referencing and full replicability.

This study aimed to answer two main questions; one is the identification of patterns in logistics technology implementations and the other concerns the linkages between those patterns and successful implementation measured by performance improvements and extended use of technology. To answer these questions, the authors reviewed several techniques and concepts used in literature for the identification of patterns in process data and settled on optimal matching from natural sciences and cluster analysis. Optimal matching is a dynamic programming technique that works to minimize an objective function of the total distance between any pair of sequences measured by the total number of required insertions, deletions and substitutions to turn one sequence into another. Cluster analysis, in contrast, is heuristic technique that doesn’t give an optimal number of clusters. However, the technique is well-known in literature and several techniques have been developed to determine and validate the number of clusters.

The joint analysis of optimal matching and cluster analysis, which is explained in detail within the paper, revealed the existence of four taxonomies to the transfer of logistics technologies. The taxonomies are formal minimalist, mutual adaptation, development concerned, and organizational roles dispenser. It is advisable that researchers take these patterns in consideration when conducting field research in variance theory. It was found that factors which were important in one pattern may not be as influential in others. For example, in formal minimalist education and team formation are crucial, but technology short-listing and selection are not of paramount importance, because technologies are mostly methodological.

Process theory offers the necessary conditions for successful implementation and without their existence the success of the technology transfer project is jeopardized. These conditions depend so much on the relevant pattern

which proved to be significantly different among the different patterns.

Practical Implications This study revealed that companies take four different

paths when adopting and implementing logistics technology. Designated names which represent their qualities were given to them. These are formal minimalist, development concerned, mutual adaptation, and organizational roles dispenser. Factors which determine the paths these companies take are outside the scope of this study. However, the factors leading to successful implementation in each one of those paths are significantly different among them. Therefore, careful planning should be giving to certain processes within those paths. In formal minimalist, team formation and intensive education are crucial processes for successful transfer of logistics technology. Development concerned depends on technology development, top management interest, long term partnerships, sufficient project definition, and pilot testing. Mutual adaptation relies on technology selection, business process reengineering, technology customization, testing, and education and support. Organizational roles dispenser depends on strategy formulation, project definition, and technology short-listing and selection.

There are certain characteristics that differentiate each path from another which can be helpful in designing an optimum sequence of events in order to achieve a successful logistics technology transfer. In the formal minimalist, several of the logistics technologies were methodological in nature which had education and training as the best way to communicate the technology. Team formation represented an important part of this pattern as well because the team was responsible for gathering required information about the technology, deploying the technology, educating the employees, and communicating the benefits. Therefore, formal minimalist is best in cases where the technology represents a shift in the company’s working system such as moving from a push system to a pull system. In development concerned, the development of the technology is the main concern and therefore the firm should plan a sequence of processes to make sure that the developed technology is error-free and represents the different interests within the company. In the analyzed case studies, the companies contracted consultants or experts whom they spent considerable time investigating the current system and analyzing the problems. Further, the consultants began by developing a basic model and then expanding it overtime which resulted in the development of long term partnerships. The development concerned process, however, began with recognition of an internal genuine need which captured management interest. System testing took a significant portion of successful projects in this category. Mutual adaptation is best suited for projects with an off-the shelf technology. In designing a mutual adaptation sequence model, management should bear in mind that customizing the technology is inevitable most of the times and therefore, the project plan should allocate a significant portion of the time for this step. Furthermore, on-going support is crucial for the success of the project because post-implementation problems

are a common case. Additionally, system testing should allow for sufficient time in the training environment. In organizational roles dispenser, the projects are of large-scale such as global rollout which results in the creation of new jobs or inter-organizational systems. In these projects, a good event sequence should begin with long-term strategy formulation which explains the direction of the organization. A highly qualified project team is desirable to appropriately convert the importance of this move to other partners or branch companies. Technology selection process which proved to be a significant process in this pattern should reflect the direction of the organization and the interests of the branch companies or trading partners.

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