SERVICE SCIENCEVol. 4, No. 1, March 2012, pp. 55–68ISSN 2164-3962 (print) � ISSN 2164-3970 (online) http://dx.doi.org/10.1287/serv.1110.0002

© 2012 INFORMS

Enterprise Transformation to EnableUniversity–Industry Collaboration: A Case Study

in Complexity and UsabilityChen-Yang Cheng

Department of Industrial Engineering and Enterprise Information, Tunghai University,Taichung City, 407 Taiwan, Republic of China, chengcy@thu.edu.tw

Tanna PughIndustrial Research Office, Pennsylvania State University, University Park, Pennsylvania 16802, uxp3@psu.edu

Ling Rothrock, Vittal PrabhuDepartment of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, Pennsylvania 16802

{lxr28@engr.psu.edu, prabhu@engr.psu.edu}

Enterprises need to make continuous fundamental transformations—such as improving current business processes, perform-ing entirely different tasks, and conducting automated business processes—to maintain or gain competitive advantage.

These transformations may increase value or decrease time, costs, and uncertainties. However, it is difficult to choose trans-formations that deserve major investment without assessing the relative value of alternative transformations. Analyzing andredesigning business processes to ensure consistency with business requirements and information technology (IT) specifica-tions is a critical factor for successful enterprise transformation. This paper provides an evolution methodology based onprocess complexity to implement effective and efficient best practices for enterprise transformation. This paper uses a processcomplexity and usability metrics, combining software science and cognitive science, to evaluate the cognitive loading of thebusiness processes. Furthermore, to illustrate the metric, this paper describes an IT-driven enterprise transformation to enableuniversity–industry collaboration. The purpose of this study is to evaluate the need for conducting operations with and withoutthe use of information technology. The complexity model shows a more than 60% decrease in the complexity, suggestingthat the IT-integrated process is less complex than earlier processes.

Key words : enterprise transformation; university–industry collaboration; complexity; usabilityHistory : Received May 10, 2011; Received in revised form December 1, 2011; Accepted December 6, 2011.

1. IntroductionThe increasing complexity of cross-functional business processes at various roles, departments, and functionsin an enterprise increases the failure risks of these processes. Furthermore, complex business processes are themajor reason for error and rapid increases in costs across all business models. Information technology (IT) hasdriven enterprise transformations to overcome the competitive market environment, uncertain opportunities, andchanges in customer behavior. Enterprise transformation is normal, critical, and obligatory, and it is equally agreat concern for corporate executives. Enterprise transformation is driven by expected value deficiencies andis enabled by changes in business processes (Rouse 2005a, b; 2006). These changes may result in increasedvalue or decreased time, costs, and uncertainties in the business processes. Enterprises may choose to improvethe current business processes, perform different tasks altogether, or conduct automated business processes.Furthermore, in an IT-based enterprise transformation, managing critical changes in existing business processesand supporting deployment of new processes are major challenges (Caverlee et al. 2007). To mitigate thesechallenges, the enterprise requires proven methods and technologies for effectively and efficiently enhancingpractices of enterprise transformation. This paper discusses a case study of enterprise transformation basedon university–industry collaboration and focuses on techniques relevant to enterprise transformation, such asbusiness process complexity and usability.

Over the past 20 years, collaborations between universities and industries have resulted in mutually beneficialinteractions. Industry benefits include breakthrough ideas that have led to new products on the market. Net-working with leading academic institutions also provides industry with access to superior basic and fundamentalresearch. Universities have the basic research capabilities and motivations to explore concepts too risky forindustry to pursue. This type of fundamental research without a specific application may lead to an innovativedevelopment for industry. The university setting contains a variety of research teams that specialize in solvingspecific technical problems, developing new products, or leading to new patents. Because university students,as key members of these research teams, already have knowledge of and experience with future products, they

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Cheng et al.: Enterprise Transformation to Enable University–Industry Collaboration56 Service Science 4(1), pp. 55–68, © 2012 INFORMS

also serve as ideal future employees for industry. In addition, small- and medium-sized enterprises typically donot have sufficient resources to explore research and development. Thus, universities can play a role as theirstrategic partners.

Similarly, universities may gain many benefits through collaborations with industry. These include significantfunding for specific projects, which helps the institution acquire valuable lab equipment. The financial supportfrom industry aids students in their academic studies. In addition, industry sets challenging research problemsthat can drive faculty to work on practical problems rather than working in the “ivory tower.” Faculty cantherefore also solve real-world problems with their research and theory. The opportunity to do relevant andcommercially useful research forms an important component of students’ educational experience—both graduateand undergraduate. These students are then well prepared for internships and job placement opportunities.

Although university–industry collaboration may benefit both partners, obstacles affect collaboration and limittheir ability to work together. Areas of concern include the ability to track projects, meetings, and budgets. Atremendous amount of effort and coordination is required to move ideas toward concepts and onward to projectswith deliverables, and at the same time complete the successful negotiation of a research contract. Additionally, togenerate a pipeline of prospects, a university has to implement a marketing strategy that encompasses attendanceat large industry-relevant trade shows and conferences, as well as engage in directed sales efforts with companies.The emergence of information systems offers the possibility of satisfying the need to maintain researchers’and industry data and to do it more efficiently. Information systems enable enterprises to track and manageall customer interactions, from first contact to purchase to postsales. Therefore, an information system is onepotential enhancement to the university–industry collaboration.

Although information systems may significantly improve customer relationships and traceability, they mayalso increase process complexity. As service processes become more complex, the difficulty of locating andcorrecting problems rises dramatically. Indeed, many business information system implementations fail becauseof the lack of focus on the business process and change management (Bose 2002, Jarrar et al. 2000). TheGartner Group reported that up to 70% of information system implementations fail to meet basic companygoals (Davis 2002). The failure of systems implementation poorly addresses cross-functional workflow processesamong process owners, end users, workflow process analysts, and software developers predeployment (Cornerand Hinton 2002). Also, most implementations of enterprise information systems fail because of an inability tocommunicate with system users. Other problems include inadequate requirement specification and poor prepa-ration for accepting and using the information system (Block 1983). In other words, the critical success factorin such a system implementation is analysis and redesign of business processes in a manner that makes themconsistent with system specifications and supports the software (Mary 1999). However, the increasing complex-ity of to-be processes may also result in failed implementations of information systems. The complexity modelthat compares as-is processes with to-be processes provides decision makers with an evaluation basis before theprocess redesign or implementation of the system.

The objective of this research is to evaluate what is needed to conduct operations with and without theuse of an integrated information system. In this paper, we use a metric for quantifying the complexity of theinformation system applying a business process that is based on the activity interactions and cognitive load. In §2,relevant metrics of business process complexity from software engineering are reviewed. Section 3 describes thecomplexity metric as applied to the specific case of Penn State University’s Industrial Research Office (IRO)business process. Section 4 presents an empirical evaluation. Section 5 offers perspectives on future work.

2. Literature Review

2.1. Complexity Metrics

The study of measuring business process complexity is a relatively new field. A significant amount ofresearch has focused on the complexity of software programs, and software complexity metrics have success-fully predicted error rates, estimated maintenance costs, and identified software modules that should undergoreprogramming.

Control-flow complexity (CFC) examines decision nodes as indicators of control flow in the business process(Cardoso 2005). Volker and Werners (2000; see also Laue and Gruhn 2006) believed the shortcoming of theCFC metric was due to its ability to only count the number of possible decisions in a model, thereby providinglittle information about its structures. Information flow metrics measure the flow of information within a system(Henry and Kafura 1981) and provide a method for quantitatively assessing the balance between structure andefficiency. The cognitive weight of a basic control structure is a measure of the difficulty of understanding that

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Cheng et al.: Enterprise Transformation to Enable University–Industry CollaborationService Science 4(1), pp. 55–68, © 2012 INFORMS 57

structure (Shao and Wang 2003). For instance, complicated human-to-human and human-to-system interactionmay lead to the formation of cognitive loading and increase hidden complexities that current metrics cannotclearly indicate.

2.2. Usability Metrics

GOMS stands for goals, operations, methods, and selection rules, which is best suited to the analysis of rou-tine, skilled performance, as opposed to problem solving (Card et al. 1980, John and Kieras 1996, Ritter andChurchill 2007). GOMS analysis is an example of applied information process psychology (Card et al. 1983).It is also an engineering model for usability that produces quantitative predictions of how well humans willbe able to perform tasks with a proposed interface design. The GOMS models the task and system design andenables prediction of useful properties of human system interaction (Card et al. 1983) and system functionalanalysis (Kieras 1997). It has several variants, including the keystroke-level model, Card–Moran–Newell GOMS(CMN-GOMS), natural GOMS language (NGOMSL), and cognitive-perceptual-motor GOMS (CPM-GOMS)(John and Kieras 1996). Some of the GOMS have led to the solution of real-world problems. One of the typicalexamples is use of the CPM-GOMS variant to evaluate a new telephone workstation used by toll operators(Gray et al. 1993). NGOMSL stems from using the GOMS program form to present a cognitive architecturecalled cognitive complexity theory (CCT) (Bovair et al. 1990). The NGOMSL procedure provides predictionsof the operator sequence, execution time, and time to learn methods. The level of external operations has beenempirically validated and can produce reliable quantitative estimates for items such as time or click movements.However, the estimation of execution time for the internal operator is not easy and is case dependent. There is aone-to-one relationship between statements in NGOMSL and production rules in CCT (John and Kieras 1996,Kieras et al. 1997). Therefore, the number of operators can be counted and regarded as an indication of the levelof the model’s cognitive complexity.

2.3. Complexity and Usability Metrics in Software Implementation

The complexity and usability metric in process and system evaluation has been studied previously (Cheng 2008,Cheng et al. 2008). The resulting model, business process analysis in goal, operations methodology, and selec-tions (BPA-GOMS), is composed of two attributes—the internal complexity of an activity and the interactioncomplexity. The internal complexity models user-level behaviors and how to perform the business process usingcognitive theory. Interaction complexity is identified through a business process analysis that examines interac-tions with other resources, including employees, customers, and computer programs. Usability and interactioncomplexity provide insights into node-level exchange and information exchange in business process analysis.Thus, interactivity complexity can be measured using a sequence, as follows:

Interactivity complexity = Total number of information flows = 4Fan-in + Fan-out50

A relatively high information exchange indicates stress points in the business process. This means that achange in this component would tend to affect many other components, making implementation or modificationof such a component difficult.

In this research, the complexity of intra-activity is measured by the number of statements or operators. Thus,

Intra-activity complexity = a ∗ 4No. of external operators5+ b ∗ 4No. of internal operators51

where a and b are weighted parameters.The fundamental principle behind the metrics is that the complexity of a business process is composed of two

attributes—the internal complexity of an activity and the complexity of its interaction with the environment—andis expressed as follows:

Complexity = Intra-activity complexity ∗ 4Interactivity complexity520

Raising the equation to the power of 2 is done to express the complexity as a nonlinear function, which meansthat the information flows contribute to the complexity of the business process in a nonlinear relationshipwith intra-activity complexity (Shepperd 1993). It also reflects the fact that it is more difficult to perform theinteractions among the entities than to perform the individual activity. This evaluation approach has also beenapplied in software complexity (Henry and Kafura 1981) and manufacturing complexity estimation (Phukanet al. 2005).

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Figure 1. Example of a BPA-GOMS Model

2 2.6

5 6.5

2 2.6

System 1Search data

System 2Enter data

9.17

Method name

No. ofoperators

Duration time(sec)

User

Launchsystem 1

Launchsystem 2

Table 1. Total Complexity in an Example of a BPA-GOMS Model

Interactivity Total Total timeElements Intra-activity Fan-in Fan-out complexity (sec)

User 4 1 2 36 502System 1 5 1 1 20 605System 2 7 1 0 7 901

Total 16 3 3 63 2008

Figure 1 shows an example of the BPA-GOMS model, which involves three-two systems. Each block containsthe operation name, number of operators, and spending time. In this case, the assumptions of weight parameterare equal to 1. Table 1 expresses the total complexity of this example and estimated time to complete thebusiness process. These complexity metrics are intended to provide a basis for comparing design alternativesrather than to establish an absolute quantification. The intermodule metrics can evaluate interactions betweensystems and alleviate stress points, as well as uncover the reasons for those stress points. In the next section,we apply the complexity metric in the case study.

3. Case StudyAs discussed in §1, university–industry collaboration is a growing type of interorganization alliance in theUnited States. The IRO at the Pennsylvania State University establishes relationships with companies to fundcooperative research and enable technology transfer. In this section, we briefly introduce the technology transferprocess at Penn State and then analyze the IRO as-is and to-be processes with an integrated information system.Finally, the complexity metric is applied in an evaluation of the IRO process.

3.1. Technology Transfer Organization

At Penn State, technology transfer is handled through the integrated efforts of four units, as illustrated inFigure 2. These four units are described as follows:

a. Industrial Research Office: Matches faculty expertise to industry needs.b. Intellectual Property Office (IPO): Manages, protects, and licenses intellectual property.c. Research Commercialization Office: Creates spin-off companies from university research.d. Ben Franklin Technology Center: Provides research for Pennsylvania’s high-tech economy.

Together, these units cover all major aspects of the commercialization process—from linking industrialresearch sponsors with faculty; to patenting and licensing intellectual property; to assisting start-ups with incu-bation and advice; to providing financing, counseling, technical assistance, and convenient physical facilities forcompanies of all sizes.

Although university–industry collaboration can provide benefits for both sides, obstacles affect the collabo-ration and limit the collaborative ability of these two unique groups. Publication of research results by facultyand students has always been an area of concern for companies. Although completion of an academic program

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Figure 2. Technology Transfer Process (from Idea to Product)

includes the completion of a thesis, industry wants to protect its proprietary and confidential information. Withthe passing of the Bayh–Doyle Act in the mid-1980s, universities were given ownership of any intellectualproperty generated in research on behalf of federal programs. Because intellectual property is the foundationfor many companies, they are unwilling to allow universities to take ownership of any intellectual propertygenerated in a sponsored project. This has created a chasm that still exists today, but it is closing because of theneed for innovation and the changing global business climate.

3.2. Business Goal

The IRO, as one of the Penn State technology transfer organizations, acts as a catalyst in linking industrialresearch sponsors with faculty. In 2010, IRO facilitated 161 projects generated by 47 companies, totaling$9.3 million in industry-sponsored research. These results were generated through significant effort and coor-dination to move ideas to concepts and then to projects with deliverables. At the same time, they involved thesuccessful negotiation of research contracts. To generate a pipeline of prospects, the IRO has implemented amarketing strategy that encompasses attendance of large industry-relevant trade shows and conferences as wellas directed sales efforts with companies.

In this role as intermediary between university and industry, the IRO employs personal information manage-ment software (e.g., Microsoft Outlook) to handle customer relationship data. However, such software does notfully satisfy the IRO’s needs for data management, such as quick response to customer needs. The emergence ofan integrated solution offers the possibility of satisfying the need to maintain researchers’ and industry data andto do so more efficiently. The goal is to provide a streamlined interface for managing customers in a way thatintegrates well with other Microsoft applications. To conduct its mission-related work in a more effective andefficient process, the IRO invested in a software management tool, Microsoft Customer Relationship Manage-ment (MSCRM). MSCRM was chosen after a thorough evaluation of existing commercial off-the-shelf packages.The main criterion was compatibility with Microsoft Outlook, which is used for most communication with cus-tomers and is used to maintain contact management. Use of the as-is process with operations in MS Outlookand the to-be process with operations in several software packages, including the customized MSCRM, browser,and MS Office software, provides a fairly seamless integration with IRO’s established routines. However, theincreasing complexity of IRO to-be processes may result in the failure of the integrated solution implementation.Therefore, a complexity model that compares the as-is and to-be processes provides IRO decision makers withan evaluation basis before redesigning or implementing the integrated information system process.

3.3. Core Business Processes

The major business activities in the IRO are marketing, sales, and networking. In the marketing area, IROparticipates in large trade shows and other events held throughout the country. It also coordinates trade shows,workshops, and symposia on campus. Several typical on-campus events are Hydrogen Day, CrossOver, andMaterials Day. The publication of the IRO Newsletter offers industry an opportunity to periodically screenemerging technologies, identify new research centers or initiatives, and advertise upcoming events at which theIRO will be representing Penn State’s capabilities. These trade shows generate hundreds of leads that are thenqualified with the goal of developing a relationship with a company that leads to a sponsored research project.

The IRO is an information-rich environment. Staff spend significant time collecting, analyzing, and dissemi-nating information gathered from a variety of sources. These sources include existing faculty intellectual property

Start-up

PatentIdea

IndustrialResearch

Office (IRO)

IntellectualProperty

Office (IPO)

Existingcompany

License

Product

Develop-mentSmall

company Ben FranklinTechnology

Center

Researchcommerciali-zation office

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Cheng et al.: Enterprise Transformation to Enable University–Industry Collaboration60 Service Science 4(1), pp. 55–68, © 2012 INFORMS

Figure 3. Integrated Information System Supporting Functions in the Industry–University Collaboration Business Process

Leadsmanagement

Opportunity

managementmanagement

Status report

Integratedinformation

system

Knowledge-based

Trade show

Campus visting

and publication data,1 federal funding opportunities,2 industrial trends, and technology developments. All thisinformation comes into play as the IRO works to establish credible and appropriate links between university fac-ulty and industrial partners. As these linkages are made with existing companies,3 more than 500 new contactsare added annually.

To accomplish its goals, the IRO has built good networks with internal and external organizations. Withinthe university, the IRO provides linkages to other technology transfer units and university-wide efforts such asrecruiting. Linking with student recruitment resources provides industry with the best-possible human resourcedatabase. Outside the university, IRO represents a portal to Penn State’s broad technical resources, faculty,research center capabilities, and unique university facilities. The IRO is also a member of several professionalorganizations that provide opportunities for continued networking. The entire sales process from introduction tocontract has a very long timeline. It is essential to utilize as much information as possible to advance effortsand to constantly be looking for the next opportunity.

To build these connections, the IRO maintains a database of more than 200 university research centers as wellas a plethora of specific industry needs. The IRO establishes relationships with companies to fund cooperativeresearch and enable technology transfer. As mentioned earlier, the IRO’s major business activities, includingmarketing, sales, and network, can be divided into six major business operations, as illustrated in Figure 3.

A. Trade show task management: Trade shows are one of the IRO’s key marketing activities. Running asophisticated trade show requires the coordination of many people, activities, realistic deliverables, and deadlines.A detailed trade show working list has to be distributed for each owner and finished by a specific date. A workinglist is developed to track university partners, booth registration, logistics, marketing lists, follow-up activities,supporting literature, and cost and revenue goals, etc. Financial tracking is also a requirement to determine thereturn on investment. The IRO uses paperwork to complete task management.

B. Leads management: In addition to leads generated by trade show participation, the IRO receives referralsfrom faculty and administrators, and it meets new contacts at on-campus events and through alumni and thewebsite. Many of these leads never become qualified as contacts and remain merely as prospects. In MS Outlook,the IRO could only use contacts without distinguishing between the two. Without this capability, there was noway to monitor their pipeline or to clean out old leads.

C. Knowledge-based management: The IRO has always maintained a resource library of key literature anddocuments that support the university’s research strengths. Maintaining this library of information is a difficulttask because the documents are currently not available electronically.

1 These come from 2,500 science- and engineering-related faculty members in more than 10 colleges.2 One hundred forty-seven billion dollars in federal agency funding was granted in 2009.3 Three thousand contacts are in the current database.

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D. Campus visiting work list management: The IRO hosts approximately 200 on-campus visits by industry.These visits typically span at least one full day. In that course of time, a company may be introduced to 10 facultymembers from varying backgrounds. These visits include formal presentations and tours. All the informationreceived from the company and by the faculty is held on a shared drive, but there is no relationship betweenthis information.

E. Status reports generation: Reporting allows an organization to measure its effectiveness and performance.Without proper tools, these data cannot be collected or evaluated, which leads to ineffective activities. TheIRO was frustrated by numerous requests for subsets of information that were impossible to generate—forinstance, how many Pennsylvania companies visited the campus in the last six months and had an interest innanotechnology. By developing customized reports as well as following the IRO report style, users can easilygenerate a total of 31 different reports by defining conditional searches, such as expertise area, department, ortime frame.

4. Business Process Complexity AnalysisThe purpose of this study is to evaluate the complexity of conducting operations in university–industry col-laboration such as the IRO office with and without the use of integrated information technology. We analyzetwo scenarios using sequence diagrams as well as the BPA-GOMS model to show the complexity metric ofeach scenario for the existing process and the automated process with the integrated information system. Thesescenarios stem from the daily business operations of the IRO. The goal of this evaluation study is to performan analysis that compares the efficiency of this new software with the previous use of Outlook and InternetExplorer (IE) as stand-alone applications. The two scenarios are described next.

Scenario 1: Create a list of faculty who are performing research in a specific area.As-is process: The particular goal was to find a faculty member at Penn State with a specific expertise.

The process usually went as follows: The university received a call from a potential industrial partner about atechnology. An IRO employee would then seek to identify a faculty member interested in this topic and providethat information to the interested industry. Because this information was kept in separate Outlook and Collegeof Engineering (COE) databases, the IRO employee would search in two different sources. Figure 4 shows thesequence of the process flow. This is a common, daily business practice in the university’s IRO, making thefulfillment of customers’ demands one of its highest priorities.

To-be process: With the integration with a COE database, there is a single source of information. The IROemployee uses the integrated information system to obtain all the relevant information with a few keystrokes. Fig-ure 5 shows the sequence of the interaction with the integrated information system.

Figure 4. BPA-GOMS Model of Existing Process in Scenario 1

Salesrepresentative

Outlook

COEdatabase

Verify thesearch result

Open COEwebsite

Verify thesearch result

OpenMS Outlook

2 2.6 1 12 1 123 12.8

Search with IRO-defined category

8 10.4

Open the facultyresearch page

Search with industryusing keywords

2.6 2 9.1 7

Figure 5. BPA-GOMS Model of IT-Integrated Process in Scenario 1

Salesrepresentative

CRM

OpenMS CRM

Verify thesearch result1 12

3.933.95.23.93 34

2 2.6

Use IRO-definedsearching view

Search with IRO-defined keywords

Search with industryusing keywords

Combine twosearch options

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Scenario 2: Create account status reports.As-is process: The particular goal was to create status reports for the IRO director. The process usually went

as follows: the IRO sales representative, the account owner, was responsible for creating a status report everymonth. The report included account history information, collaborative projects, and industry funding. The salesrepresentative and the secretary both contributed to the report. Because the report required retrieval of informationabout communications with clients and historical data on accounts from Excel files, the report creation processusually took several days. Figure 6 shows the sequence of the process flow and the involved members.

To-be process: The integrated information system becomes an aggregate database into which the sales repre-sentative and the secretary may enter data. The IRO director is able to review projects’ statuses periodically byaccessing the database. Figure 7 shows the sequence of the interaction with the integrated information system.

To assess the extent to which the new integrated information system would increase the productivity of certaintasks in the IRO, we employed the BPA-GOMS model to account for task behaviors. Table 2 provides a partialview of Scenario 1 process statements. Each operator belongs to either an external operator or an internaloperator. According to Fitt’s Law, the external operator takes 1.1 seconds to target a display and 0.2 seconds toclick on an item with his or her mouse (Card et al. 1983). Thus, each step, including a mouse movement and amouse click, takes 1.3 seconds to complete.

Figures 4–7 present the BPA-GOMS models for each process and scenario. The block in the process containsthe method name, number of operators, and estimated time to complete one activity. The arrows represent theinteraction with other elements. The direction of the arrow shows the fan-in or fan-out direction. The salesrepresentative, MS Outlook, MSCRM, etc., are different elements involved in completing the business process.There are some assumptions in the BPA-GOMS analysis:

1. The weight of the internal and external operators is 1. Thus, the number of operators for each activity isthe sum of the internal and external operators.

Figure 6. BPA-GOMS Model of Existing Process in Scenario 2

IRO director

Salesrepresentative

Secretary

Excel

OpenExcel

Retrieveaccount

documents

Retrieveproject

documents

Updateprojectstatus

Updatefundingstatus

Updateproject

probability

2

7

2.6

Aggregatestatus report

Evaluateprojects

probability

Retrievefunding status

Checkaccount

status report

1 P(t)

1

7 9.1 3005

P(t)

P(t)9.1 7 2 P(t)2 P(t)29.1

Figure 7. BPA-GOMS Model of IT-Integrated Process in Scenario 2

IRO director

Salesrepresentative

Secretary

CRM system

OpenMSCRM

OpenMSCRM

Retrievefunding status

Update projectsstatus

Update projectsstatus

2

7

2 2.6

2 2.69.1

P(t) 2

1

P(t)

P(t)

2

12 2.6

P(t)

P (t)

Updatefundingstatus

Evaluate projectprobability

Checkaccount

status report

OpenMSCRM

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Table 2. Partial View of Scenario 1 Process Statement

Process statements Operators Time (sec)

Method for goal2 Open MS OutlookStep: Click Start (EOP) 103Step: Click MS Outlook application (EOP) 103

Subtotal No. of EOP = 2 206

Method for goal: Search of IRO-defined categoryStep: Click Find button (EOP) 103Step: Click Options button (EOP) 103Step: Click Advanced Find option (EOP) 103Step: Click More Choices tab (EOP) 103Step: Click Categories button (EOP) 103Step: Click the option of “current keywords” (EOP) 103Step: Click OK button (EOP) 103Step: Click Find Now button (EOP) 103

Subtotal No. of EOP = 8 1004

Method for goal2 Search with IRO-defined categoryDecide: Verify the search result (IOP) 1200

Subtotal No. of IOP = 1 1200

Method for goal2 Open MS OutlookStep: Click Start (EOP) 103Step: Click MS Outlook application (EOP) 103

Subtotal No. of EOP = 2 206

Method for goal: Search of IRO-defined categoryStep: Click Find button (EOP) 103

Note. EOP, external operator; IOP, internal operator.

2. The “Verify the search result” activity requires one internal operator and takes 12 seconds to complete.3. Some activities in Scenario 2 take P4t5 time in the cognitive effort. For example, the time needed to update

the status of several projects depends on the condition of each project and the number of projects that need tobe updated. Because the aim of this research is to analyze an alternative process that requires the same cognitiveloading on these time-consuming activities, the results have the same variable activity and can be eliminated incomparison.

Based on these BPA-GOMS models, the results of the analysis of the complexity of each business processare shown in Tables 3–6. Each element has its intra- and interactivity complexity with other elements. The totalcomplexity is calculated according to §2.3. The estimated processing time can reduce the existing process time

Table 3. Complexity Analysis of Existing Process in Scenario 1

InteractivityTotal Total time

Elements Intra-activity Fan-in Fan-out complexity (sec)

Sales representative 7 2 2 112 39.4Outlook 8 1 1 32 10.4COE database 9 1 1 36 11.7

Total 24 4 4 180 61.5

Table 4. Complexity Analysis of IT-Integrated Process in Scenario 1

InteractivityTotal Total time

Elements Intra-activity Fan-in Fan-out complexity (sec)

Sales representative 3 1 1 12 14.6CRM 13 1 1 52 16.9

Total 16 2 2 64 31.5

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Table 5. Complexity Analysis of Existing Process in Scenario 2

InteractivityTotal Total time

Elements Intra-activity Fan-in Fan-out complexity (sec)

IRO director 1 1 0 1 P4t5Sales representative 3 1 2 27 206 +P4t5Secretary 12 2 1 108 309.1Excel 20 3 3 720 1802 + 3P4t5

Total 36 7 6 856 32909+ 5P4t5

Table 6. Complexity Analysis of IT-Integrated Process in Scenario 2

InteractivityTotal Total time

Elements Intra-activity Fan-in Fan-out complexity (sec)

IRO director 3 1 0 3 206 +P4t5Sales representative 3 1 2 27 206 +P4t5Secretary 9 1 1 36 11.7CRM 6 3 3 216 3P4t5

Total 21 6 6 282 1609+ 5P4t5

Figure 8. Total Complexity for Two Scenarios

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by half in Scenario 1. In Scenario 2, because the sales representatives, secretary, and IRO director all use theintegrated information system as a single platform to input and communicate information, the effort to requestdata and aggregate reports can be eliminated, thereby reducing the routine processing time by more than 20times that of the current process.

Using the integrated information system, IRO employees use fewer mouse clicks and exert less cognitiveeffort to complete a task. Figure 8 presents the total complexity of the two scenarios. Both scenarios have alower total complexity score using integrated information system. In Figure 9, Scenario 2 shows a decrease of67% in the total complexity, suggesting that the IT-integrated process has higher usability.

5. Empirical EvaluationThis empirical study recorded the durations and mouse clicks in two scenarios and compared the existing processand IT-integrated process in Scenario 1. A comparison of prediction times also provided a view of the systemresponse time in these two processes.

First, the computer programs used to execute actual tasks were Microsoft Outlook 2003, Microsoft InternetExplorer (version 7), and Microsoft Dynamics CRM 3.0 (Snyder and Steger 2006). These programs were run

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Figure 9. Total Complexity for Two Scenarios

Scenario 1

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on an Intel Pentium 4 notebook computer with a CPU 2.8 GHz processor with 512 MB of RAM. To recordthe task durations and mouse clicks, the recording user input (RUI) application was used (Kukreja et al. 2006).When the participant was ready to start, the RUI was opened, and a file was created to log the keystrokes, mousemovements, and mouse clicks. Once the task was finished, the RUI was stopped, and the files were saved. TheRUI log files were opened, and the number of clicks and strokes needed to accomplish the overall task andsubtasks were copied into Tables 7 and 8. The number of mouse clicks in the log file for each software trialwas counted and noted in these tables as well.

Based on the procedural steps shown above, we found that using Microsoft Outlook and IE to achieve thedesired goal required only four tasks (two for each program needed) but necessitated many subtasks. Completingthe desired goal took a total of 2 minutes and 1.86 seconds and involved 19 mouse clicks (see Table 7).Completing this same task using the integrated information system required six tasks but necessitated fewersubtasks. The time to complete the action in this program was 1 minute and 47.4 seconds, 14.46 seconds less

Table 7. Time Recording and Mouse Clicks in Scenario 1 (Existing Process)

Elapsed Mouse Prediction OffsetTask time (sec) clicks time (sec) (sec)

1. Search in the Outlook contact list 300811 9 13 170811• Open MS Outlook 10484 2 206 −10116• Search with IRO-defined category 290327 7 1004 180927

2. Verify the MS Outlook search result 120000 0 12 03. Search the COE database 670051 10 2405 420551

• Open COE database website 280500 3 1208 1507• Open the faculty research page 110792 2 206 90192• Search using keywords 260759 5 901 170659

4. Verify results 120000 0 12 0

Total 1210862 19 6105 600862

Table 8. Time Recording and Mouse Clicks in Scenario 1 (IT-Integrated Process)

Elapsed Mouse Prediction OffsetTask time (sec) clicks time (sec) (sec)

1. Open MSCRM software 150687 2 206 1300872. Use IRO-defined search view 220077 3 309 1801773. Search with IRO-defined keywords 140657 4 502 904574. Search using keywords 340263 3 309 3003635. Combine two search options 80718 3 309 408186. Verify results 120000 0 12 0

Total 107040 15 3105 750902

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Table 9. ANOVA Table for Elapsed Time

Source Degree(s) of freedom Sum of squares Mean square F P -value

Regression 1 14,396 14,396 59.8 0Residual error 13 3,132 240

Total 14 17,528

Note. S = 15052, R2 = 8201%, adjusted R2 = 8008%.

than the other two programs combined (see Table 8). Fewer mouse clicks were required in integrated informationsystem—15 fewer than in the current IRO process.

In this experiment, the most important factor is the elapsed time, which is the measurement of actual efficiencybetween alternatives. Table 9 shows the analysis of variance (ANOVA) result of prediction and elapsed timesfor the regression model. The model accounts for 80.8% variability in the current experiment. It is observed thatthe prediction and elapsed times are not significantly different.

Several points may be highlighted from this study. The results show that the use of an integrated informationsystem to complete tasks was faster than the use of Outlook and IE by 14.46 seconds. Given that each task tookroughly two minutes, a 10% increase in efficiency is significant for a task that may require speedy completionin response to an industry’s query. In contrast, over time, 15 seconds per search occasion may add up. If thispersonnel search task is undertaken regularly, a move to an integrated information system could save time andmoney. The integrated information system method also has fewer actual steps, but more than may be expectedwhen using just one program rather than two. That said, there may be other reasons to favor the simplicity ofusing one program rather than a combination of programs. Even without the time record, these two procedurallists provide useful information about how to use these two programs to complete a given task and how theycompare in terms of steps and functions. The offset time also provides information on the system response timeto complete the process. The IT-integrated process takes longer when responding to a user request. This mayimply that more computation resources are required in the IT-integrated business process environment.

Figure 10. Mixed Complexity Metric and Predicted Time vs. Actual Time in Scenario 1

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Figure 11. Gantt Chart of Creating Account Reports in Scenario 2

25242322212019181716151413121110987654321(min)

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Figure 10 shows the mixed complexity metric and time measurement in Scenario 1. Based on the complexitymetrics in Scenario 1, the total complexity is reduced by 64.4% with the integrated information system. In theempirical result, the elapsed time is reduced by 21%. This also shows the positive correlation between thecomplexity metric and the elapsed time. With the summation information for creating a report, Figure 11 showsthe elapsed time for completing account reports in a Gantt chart. The cognitive time of P4t5 is assumed tobe five minutes. The result of this study also was verified with IRO users, and the response from users to theIT-integrated process thus far has been very positive.

6. Conclusions and Future WorkThe adoption of an integrated information system application at the Penn State IRO provides the flexibilityneeded to provide the best return via the use of an efficient database/IT solution. Relationship management is abusiness strategy, and the IRO uses an integrated information system as a technology platform to help implementits strategy, processes, and procedures. The process complexity metric provides IRO decision makers with anevaluation basis to make a decision either to customize the integrated information system or to redesign the IROprocess. When analyzing the as-is process with operations in MS Outlook and the to-be process with operationsin the integrated information system, it is clear that the customized integrated information system provides afairly seamless integration with the IRO’s established routines.

The complexity model shows a decrease of more than 60% in the complexity metrics, suggesting that theIT-integrated process is easier to use. In the empirical study, we evaluated the usability of two different methodsof customer information management. The results showed that using an integrated information system is moreefficient than using Outlook and Internet Explorer together. User response thus far to the customized integratedinformation system has been very positive. The important takeaway for other university–industry collaborationsand organizations is that following this reference model and using the complexity metric will enable evaluationand analysis of the process at the design phase.

Future research may include a prediction of system response time and resource evaluation. In our case, becausethe integrated information system is a Web-based application, the time to open it would depend on networkspeed. Both Outlook and IE are installed programs that run locally. Based on processing speeds, the functioningof these programs could vary as well. A combination of a speedy computer with a patchy Internet connectioncould yield wildly different results than those from an older computer on a T3. Thus, our time comparison islimited to using both software approaches on the same system, and the actual times are further limited to ourparticular computer system. The future study can provide a resource evaluation approach based on the systemresponse time.

ReferencesBlock R (1983) The Politics of Projects (Yourdon Press, New York).Bose R (2002) Customer relationship management: Key components for IT success. Indust. Management Data Systems 102:89–97.Bovair S, Kieras DE, Polson PG (1990) The acquisition and performance of text-editing skill: A cognitive complexity analysis. Human-

Comput. Interaction 5:1–48.Card SK, Moran TP, Newell A (1980) The keystroke-level model for user performance time with interactive systems. Comm. ACM 23:396–410.Card SK, Moran TP, Newell A (1983) The Psychology of Human-Computer Interaction (Lawrence Erlbaum Associates, Mahwah, NJ).Cardoso J (2005) Evaluating the process control-flow complexity measure. IEEE Internat. Conf. Web Services, Orlando, FL, 803–804.Caverlee J, Bae J, Wu Q, Liu L, Pu C, Rouse WB (2007) Workflow management for enterprise transformation. Inform. Knowledge Systems

Management 6:61–80.Cheng C-Y (2008) Complexity and Usability Models for Business Process Analysis, Industrial Engineering (Pennsylvania State University,

University Park).Cheng C-Y, Barton D, Prabhu V (2008) The servicisation of the cutting tool supply chain. Internat. J. Production Res. 48:1–19.Corner I, Hinton M (2002) Customer relationship management systems: Implementation risks and relationship dynamics. Qualitative Market

Res. 5:239–251.Davis R (2002) The Wizard of Oz in CRM: CRM’s need for business process management. Inform. Systems Management 19:43–48.Gray WD, John BE, Atwood ME (1993) Project Ernestine: Validating a GOMS analysis for predicting and explaining real-world task

performance. Human-Comput. Interaction 8:237–309.Henry S, Kafura D (1981) Software structure metrics based on information flow. Software Engrg., IEEE Trans. 7:510–518.Jarrar YF, Al-Mudimigh A, Zairi M (2000) ERP implementation critical success factors—The role and impact of business process manage-

ment. Proc. 2000 IEEE Internat. Conf. Management Innovation Tech. (IEEE Computer Society, Washington, DC), 122–127.John BE, Kieras DE (1996) The GOMS family of user interface analysis techniques: Comparison and contrast. ACM Trans. Comput.-Human

Interaction 3:320–351.Kieras DE (1997) Task analysis and the design of functionality. AB Tucker, ed. The Computer Science and Engineering Handbook (CRC

Press, London), 1401–1423.Kieras DE, Wood SD, Meyer DE (1997) Predicitive engineering models based on the EPIC architecture for a multimodal high performance

human-computer interaction task. ACM Trans. Comput.-Human Interaction 4:230–275.

INFORMS

holds

copyrightto

this

article

and

distrib

uted

this

copy

asa

courtesy

tothe

author(s).

Add

ition

alinform

ation,

includ

ingrig

htsan

dpe

rmission

policies,

isav

ailableat

http://journa

ls.in

form

s.org/.

Cheng et al.: Enterprise Transformation to Enable University–Industry Collaboration68 Service Science 4(1), pp. 55–68, © 2012 INFORMS

Kukreja U, Stevenson WE, Ritter FE (2006) RUI—Recording user input from interfaces under windows and Mac OS X. Behav. Res. Methods38:656–659.

Laue R, Gruhn V (2006) Complexity metrics for business process models. Abramowicz W, Mayr HC, eds. Bus. Inform. Systems: 9thInternat. Conf. Bus. Inform. Systems, Klagenfurt, Austria, 1–12.

Mary S (1999) Critical success factors in enterprise wide information management systems projects. Proc. 1999 ACM SIGCPR Conf.Comput. Personnel Res. (ACM, New York), 297–303.

Phukan A, Kalava M, Prabhu V (2005) Complexity metrics for manufacturing control architectures based on software and information flow.Comput. Indust. Engrg. 49:1–20.

Ritter FE, Churchill E (2007) The ABCS of Humans: Guidance for Interactive System Designers (MIT Press, Cambridge, MA).Rouse WB (2005a) Enterprises as systems: Essential challenges and approach to transformation. System Engrg. 8:138–150.Rouse WB (2005b) A theory of enterprise transformation. System Engrg. 8:279–295.Rouse WB, ed. (2006) Enterprise Transformation: Understanding and Enabling Fundamentals Change (John Wiley & Sons, New York).Shao J, Wang Y (2003) A new measure of software complexity based on cognitive weights. IEEE Canadian J. Electronic Comput. Engrg.

28:69–74.Shepperd, M (1993) Software Engineering Metrics2 Measures and Validations (McGraw-Hill, London).Snyder M, Steger J (2006) Working with Microsoft Dynamics CRM 3.0 (Microsoft Press, Redmond, WA).Volkner P, Werners B (2000) A decision support system for business process planning. Eur. J. Oper. Res. 125:633–647.

Chen-Yang Cheng is currently an assistant professor in the Department of IndustrialEngineering and Enterprise Information at Tunghai University. He received his Ph.D. inindustrial and manufacturing engineering at Penn State University. His research interestsinclude computer-integrated manufacturing, human–computer interaction, distributedsystems and control, and intelligent systems.

Tanna Pugh is the director of the Industrial Research Office at Penn State University.Her focus areas include contract research and technology transfer. She served the officeas associate director from December 1997 until her appointment as director in January2002. A 1988 graduate of Penn State with a B.S. degree in chemical engineering, Tannaalso serves as advisor to Penn State’s student chapter of the Society of Women Engineers.

Ling Rothrock is an associate professor in the Industrial and Manufacturing Engineer-ing Department and an affiliate faculty member in the College of Information Sciencesand Technology at Penn State University. His research interests include human-in-the-loop discrete event simulations, display visualization, and human–machine performanceevaluation. He teaches courses in human factors and human-in-the-loop simulations.

Vittal Prabhu is currently a professor in industrial and manufacturing engineering at PennState University. He received his Ph.D. in mechanical engineering from the University ofWisconsin–Madison. His research interests include distributed control systems, sensingand control of machines, and high-performance computing for manufacturing systems.He teaches courses in robotics, controls, and manufacturing systems. He is the founderand codirector of the Center for Manufacturing Enterprise Integration at Penn StateUniversity.

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