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1 This chapter provides a conceptual model that academic leaders can use to navigate the complex, and often contentious, organizational terrain of academic program development. The model includes concepts related to the institution’s external environment, as well as internal organizational structures, cultures, and politics. Drawing from the literature in management, organizational studies, and higher education, this chapter explains how various organizational configurations lead to different assumptions and practices regarding data use and program development decisions. These assumptions and practices are illustrated through a case study of the development of online programs in a community college system.

Understanding the Organizational Context of Academic Program Development

Jay R. Dee, William A. Heineman

Academic program developers are unlikely to escape expectations for data- based decision making. Given the budgetary and strategic implications of academic program development, institutions have established elaborate procedures for approving new programs and authorizing expansions and modifications of existing programs. Proposals for new academic programs may need to be supported with extensive data on the labor market demand for program graduates, as well as detailed information on the resources needed to implement the program, including faculty, facilities, and tech- nology (Posey & Pitter, 2012). Efforts to expand or modify existing pro- grams may need to demonstrate alignment with the curricular standards of the discipline or field of study in which the program is offered, as well as document the performance of the existing program in terms of learning outcomes, degree completion, and job placement rates. Academic program developers, therefore, may need to marshal a wide array of data, from a va- riety of sources, to influence the campus committees, administrative bod- ies, and governing boards that ultimately decide which proposals will be approved.

Academic program developers are challenged not only to get the right data to the right people; they also need to understand how those data will be

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used and interpreted within the organizational context of their institution (Kezar, 2014; Terenzini, 1993). The organizational context can be defined as the structural, cultural, and power configurations that characterize a partic- ular college or university, as well as the external environment in which that institution operates (Bess & Dee, 2008). The organizational context reveals which external stakeholders are linked to the institution, which procedures are used to make decisions, which values and beliefs guide those decisions, and which groups and individuals will ultimately have the most influence on the decision outcomes (Shepherd & Rudd, 2014).

In addition to the general context of the organization, academic pro- gram developers may need to consider the specific context of the decision that they are seeking (Elbanna & Child, 2007; Papadakis, Lioukas, & Chambers, 1998). Academic program development can occur in at least three decision contexts: (a) creating a new program, (b) expanding an existing program into new subfields or new student populations, and (c) substantially modifying an existing program in terms of its curriculum, pedagogy, and/or learning outcomes. The decision context involves not only the type of decision to be made but also the scope of that decision. Scope refers to the number of departments and units in the organization that will be impacted by the decision. Does the decision affect only one academic department, or do the implications extend to include the entire institution? Furthermore, the decision context relates to the institution’s history with a particular type of decision. Has this type of decision been made routinely at the institution, or is this decision considered novel and therefore somewhat risky? Finally, the decision context includes the current stage in the devel- opment of the decision. Organizational members may use data differently at various stages in the decision-making process. At early stages in the decision-making process, organizational members may use data to identify and understand problems that need to be addressed. As the process unfolds, they may use data to select among alternative courses of action. At later stages, data may be used to understand how best to implement a decision that has already been made (Mintzberg, Raisinghani, & Theoret, 1976).

The organizational context and the decision context are linked to each other. We can think of the organizational context as the map that depicts the terrain of a college or university, and the decision context as the specific route that a decision will take through that terrain. Depending upon the route, different components of the organizational context will become rel- evant. If a program development effort involves creating a large number of new courses, then the route will likely proceed through the institution’s fac- ulty governance committees, and academic program developers will need to be aware of how faculty from a range of disciplines will view the proposal. If the effort instead focuses on developing a new program in an emerging pro- fessional field, then the route will likely intersect with external stakehold- ers that have an interest in workforce development, such as the business community and government agencies.

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Figure 1.1. How the Organizational Context and the Decision Context Influence the Decision-Making Process

Organizational context

Structure

Decision context

Type

Scope

History

Stage

Environment

Culture

Power

Data Sources

Rational model Internal

Entrepreneurial External

Data Analysis Objective

model

Subjective

Political model

Exploratory model

Together, the general context of the organization and the specific con- text of the decision will shape the decision-making process that emerges (see Figure 1.1). Based on our review of the organizational decision-making literature, we suggest that the decision process will follow one of four paths, depending on whether internal or external data are emphasized, and whether decision makers are likely to engage in objective or subjective anal- ysis. The rational model is characterized by an objective analysis of internal data on organizational or unit performance. The entrepreneurial model also involves objective analysis, but the focus shifts to opportunities in the ex- ternal environment. In contrast, the political model is based on subjective analyses that reflect the interests and power positions of internal groups. Finally, the exploratory model refers to a subjective analysis of trends and developments in the external environment. These models have different as- sumptions about data (objective or subjective) and assign different priori- ties to data sources (internal or external).

The model used for a particular decision is generally not determined by organizational members in advance of a decision; instead, the decision- making model emerges organically in relation to the interaction between the organizational context and the decision context (Mintzberg et al., 1976). For example, consider an organization that has had a long history of antag- onism between faculty and administrators. In terms of the organizational context, there would be a low level of trust in the organizational culture. Furthermore, if the decision context involves issues that are considered novel and somewhat risky to the institution, then the decision-making pro- cess would likely emerge within the political model, given the focus on internal issues (risk to institution) and subjective analyses (the different views of faculty and administrators). This example is rather simplistic in that it focuses on only one component of the organizational context and

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only one dimension of the decision context. In fact, decision-making pro- cesses are shaped by many contextual factors that academic program devel- opers should consider as they gather, analyze, and share data.

If academic program developers lack an understanding of the organi- zational context or the decision context, then their efforts may not be suc- cessful (Schmidtlein, 1999). The data that they generate may not be used or interpreted as they had expected. The accuracy of their data might be ques- tioned. The data might not meet the expectations of key internal and exter- nal stakeholders. If the data are not shared with a key stakeholder group, then its members may complain that they have not been consulted. Further- more, if data are not provided before key deadlines in the decision-making calendar, then decisions may stall and the “academic clock” will run out, thus postponing decisions for another academic year (Tierney, 2001).

Similar consequences may emerge if academic program developers do not understand the decision-making process that has emerged around their proposed initiative. A common issue is seeing the decision process as ratio- nal and objective, when in fact, it has emerged as subjective and political (Schmidtlein, 1999). In this scenario, academic program developers may encounter unexpected resistance or unanticipated challenges from people who interpret the data differently. Another common issue relates to seeing the decision-making process as exploratory and open to new ideas, when in fact, key decision makers have already determined what they want to do. In this scenario, academic program developers may gather data for a range of options that have already been withdrawn or were never in contention for adoption. Instead, they could use their time more effectively to gather and evaluate data on how best to implement the decisions that have already been made.

A further consideration is that the decision-making process can shift from one model to another, as the decision context changes, or as differ- ent elements of the organizational context become relevant or activated (Tarter & Hoy, 1998). An exploratory process, for example, may shift to entrepreneurial, if external trend data clarify how institutional leaders want to proceed vis-à-vis opportunities in the external environment. Likewise, a decision-making process may shift from rational to political, as groups with competing interests become aware of the resource implications associated with a particular proposal. Academic program developers always need to be attentive to the organizational context and the decision context so that they can anticipate which decision-making model will emerge.

This chapter is organized into three parts. First, the chapter uses the higher education literature to describe the key features of the organizational context that affect data and decisions regarding academic program devel- opment. Second, we delineate relevant dimensions of the decision context for academic program development. Finally, we discuss the four decision- making models that can emerge based on conditions in the organizational and decision contexts. We illustrate these four models with examples from

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a case study (Heineman, 2011). The case study was conducted at three com- munity colleges, which were selected on the basis of the maturity and scope of their online programs. Interviews were conducted with eight decision makers at each college. These individuals were involved in online program development and included chief academic officers, deans, technology direc- tors, and faculty.

Organizational Context

Many higher education scholars have suggested that institutional decision making can be improved when administrators, staff, and faculty have an understanding of the organizational context in which they operate (Bess & Dee, 2008; Birnbaum, 1988). Kezar (2014), for example, argues that learning about the contextual conditions of the institution can help change agents be more successful. Terenzini (1993) includes contextual knowledge of the organization, alongside technical skills and knowledge of higher ed- ucation issues, among the key competencies for institutional researchers. Schmidtlein (1999) also suggests that institutional researchers need a keen understanding of the organizational context, and this knowledge can lead to better decisions.

Higher education institutions have unique organizational contexts that differentiate them from other types of organizations. Some of these unique features include quality assurance through accreditation and assessment, systems of shared governance, decentralized and loosely coupled organiza- tional structures, and academic values that promote autonomy and the ex- ploration of knowledge for its own sake (Bess & Dee, 2008; Kezar, 2014). Furthermore, colleges and universities lack clear, measurable goals, such as profit for a corporation. Instead, the goals of higher education—enhancing learning, creating new knowledge, and serving society—are somewhat vague and difficult to measure objectively. As a result, organizational mem- bers will interpret and prioritize these goals in different ways, and they will pursue activities that are consistent with their own interpretations and pri- orities (Temple, 2008) rather than adhere to some form of centralized co- ordination. Thus, the organizational context of a college or university may appear uncoordinated and somewhat chaotic to an outside observer.

Although common organizational features are found at nearly every higher education institution, the prevalence and importance of these fea- tures will vary depending upon institutional type, size, and mission (Mor- phew & Hartley, 2006). Shared governance committees, for example, might be more numerous in universities with large numbers of tenured faculty, and conversely, less prevalent in community colleges where most faculty are em- ployed on a part-time basis. This section of the chapter identifies the fea- tures of the organizational context (external, structural, cultural, and power dynamics) that are most likely to influence academic program development. Although these features are discussed in general terms, readers can assess

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the extent to which each is important and relevant to the organizational context of the institution in which they work.

External Environment. The external environment affects academic program development primarily in the areas of government regulation, quality assurance, and market-based competition. Clark (1983) identified three external forces that impact the internal operations of colleges and universities: the government agencies that regulate higher education in- stitutions, the academic disciplines that set standards for curriculum and learning outcomes in various fields, and the marketplace for students and other resources. This section of the chapter will examine those three forces in relation to academic program development.

Government agencies affect academic program development largely through their consumer protection function, and through policy incentives that encourage program development in certain professional fields (Well- man, 2006). Typically, before a public, private, or for-profit institution can offer a new degree program in a particular state, that state’s board of higher education must approve the proposal. State boards seek to protect con- sumers (students) from programs that do not meet minimum standards of quality or that do not have sufficient educational resources to support stu- dent learning. Furthermore, these boards may also require institutions to provide labor market projections for graduates of the proposed program, to demonstrate that students will be able to obtain a job in the field after grad- uation, thus protecting students from entering programs that ultimately do not lead to careers. In addition to this regulatory function, government agencies also seek to stimulate the development of academic programs in fields deemed critical to economic development or social well-being. Fed- eral and state government agencies have invested resources in workforce de- velopment initiatives that frequently target the STEM fields (science, tech- nology, engineering, mathematics), as well as high-need fields that promote social well-being, such as early childhood education and health care. In addition to government agencies, foundations and other private funding sources have provided similar incentives for academic program develop- ment, again largely in the STEM fields. Private foundations, therefore, can also be viewed as a shaper of academic program development.

Through their professional societies and accreditation associations, academic disciplines have a significant impact on academic program de- velopment. Academic disciplines shape how faculty think about teaching and learning; thus, the disciplines have a significant influence on curricu- lum development (Becher & Trowler, 2001). This influence is formalized in fields that have accreditation associations or licensing boards that reg- ulate entry into professions such as law, medicine, and teaching. In these fields, curriculum standards and learning outcomes are specified by leaders within the discipline or field of study. Academic program developers may need to address such standards and outcomes in their proposals for new programs.

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Academic programs operate within a marketplace that includes poten- tial students, employers of graduates, and competing programs at other in- stitutions (Bok, 2003). Potential students are an important data source for academic program development. They can indicate preferences for curricu- lum delivery in terms of online, in-person, and hybrid formats. They can describe their intellectual and professional goals, which, in turn, can inform curriculum development. For existing programs, current students can pro- vide data relevant to program growth and change. They can identify new subfields in which they want training and preparation and suggest addi- tional support services that could assist with their academic success.

Employers constitute another source of data for academic program de- velopment. They can provide data directly to academic program developers through their participation on advisory boards that seek to ensure that the curriculum is relevant for the types of jobs that students are likely to obtain after graduation. Advisory board members can help faculty and staff under- stand how emerging trends in the field can inform teaching and learning practices. Academic program developers can also conduct employer sur- veys, where the results might identify the need to expand programs into new subfields or reform existing programs so that they incorporate new skills and content. To obtain a related perspective, program developers can survey alumni regarding how well a program of study prepared them for their profession. Some scholars, however, have questioned whether employ- ers have too much influence over the curriculum. The emphasis on satisfy- ing employers might contribute to student consumerism, in which higher education is viewed as simply another consumer good, rather than as an opportunity to pursue knowledge for its own sake (Molesworth, Nixon, & Scullion, 2009). To establish balance across multiple educational goals, academic program developers can consider how their proposals not only advance workforce development priorities but also contribute to the public good and the betterment of society as a whole (Rhoads & Szelényi, 2011).

Data regarding comparable programs at other institutions can also in- form academic program development. Depending on the level of compe- tition in the market, individuals at other institutions may or may not be willing to share data with academic program developers. However, a great deal of information is available through publicly accessible sources, such as institutional websites. Also, Integrated Postsecondary Education Data System (IPEDS) data can show the number of degrees that institutions award in a particular field (Posey & Pitter, 2012). If comparable programs at other institutions tend to award only a small number of degrees, then academic program developers can question whether a viable market exists for the proposed program, particularly if labor market projections are not suggesting future growth for the field. Information about comparable pro- grams can also help program developers determine how they can position their proposed program as distinct from the competition. New programs may initially struggle to attract sufficient enrollments if they too closely

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resemble programs at other institutions that have been operating in the market for many years. Long-standing programs have reputations and re- sources that make them attractive to potential students, and thus provide a source of competitive advantage. Instead of competing head-to-head with long-standing programs, academic program developers can seek to identify a distinctive niche for their proposals. An analysis of comparable programs can reveal subfields and student populations that are not currently being served, and those areas could become the focal point for academic program development.

Although programs at other institutions are often viewed as competi- tors, they can also offer opportunities for collaboration. For example, aca- demic programs at transfer institutions can be an important source of data for community college academic program development. Proposers of new academic programs at community colleges may need to demonstrate how the courses will transfer to programs of study at 4-year institutions. Thus, community college academic program developers may need to collaborate with faculty and staff at 4-year institutions, who can provide information regarding curricular design and desired learning outcomes for students who transfer into their programs of study. Similarly, graduate programs can pro- vide important data to undergraduate programs, regarding the skills and knowledge bases that are needed for student success at the master’s and doctoral levels. Undergraduate program developers could collect data from the graduate programs that accept the largest number of students from their institution.

Organizational Structure. Academic program developers can antic- ipate a range of structural issues that are likely to impact their efforts. High levels of structural differentiation and decentralization can complicate ef- forts to attract collaborators and obtain resources for program development. Academic program developers may also need to navigate through their in- stitution’s online and continuing education divisions, which often operate outside the structures of traditional academic departments. Furthermore, program development efforts often need to be aligned with institutional planning, budgeting, and governance structures that adhere to specific pro- cedures, priorities, and time lines. Academic program developers may also need to assess the institution’s structural capacity to support proposed program changes.

Most higher education institutions are structurally differentiated into a large number of academic departments. Given norms associated with aca- demic freedom and professional autonomy, the faculty in these departments typically set their own curriculum and identify learning outcomes that are relevant to their respective disciplines. Although this structural arrange- ment aggregates the intellectual talents of faculty into coherent units, it also creates at least two challenges for academic program development. First, given that academic departments are the primary sites of academic program development, any effort that transcends these departmental boundaries

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may struggle to attract a sufficient number of collaborators (Kezar & Lester, 2009). Program development efforts that transcend departmental boundaries include general education reforms, first-year seminars, learning communities, service learning, and interdisciplinary initiatives. When faculty have extensive responsibilities in their own department, they may not have time for or may not develop an interest in collaborating on these types of institution-wide reforms. Second, the budget process at many institutions is based on credit hour production, which allocates funds to departments based on the number of students that they enroll. Enrollment- based budgeting, however, often serves as a disincentive for departments to collaborate on interdisciplinary initiatives. Under a system of enrollment- based budgeting, each unit will attempt to maximize its own enrollment, rather than explore opportunities to collaborate with other units on pro- gram development. Furthermore, given that enrollment-based budgeting directs the bulk of resources to academic departments, any initiative that exists outside the academic department structure might struggle to find the budgetary support that it needs (Keeling, Underhile, & Wall, 2007).

In addition to understanding the complexities of academic department structures, program developers may need to examine the policies and pro- cedures of online and continuing education divisions. Many colleges and universities have developed online and continuing education divisions that seek to respond rapidly to emerging workforce development and profes- sional preparation needs. These units tend to operate outside the typical academic department structure. Curricula may be developed by academic managers, rather than by faculty in academic departments (Toma, 2007). Decision-making processes are typically streamlined; new program propos- als may not need to pass through multilevel approval processes that in- volve time-consuming deliberations by governance committees. The fund- ing model for these units is often designed to generate revenue. This goal can be pursued by employing part-time faculty and/or by charging differen- tial tuition rates. By generating surplus revenues, these units can operate at least somewhat independently from the institution’s budget process. With their own funding streams secure, they can pursue new ventures without being overly concerned if some new programs initially fail to generate suf- ficient enrollments. In summary, the policies and procedures of online and continuing education divisions are likely to be quite different from those that govern academic departments. If academic program developers will be collaborating with these online and continuing education units, then they will need to become familiar with a different business model and a different mind-set for academic decision making.

Beyond the academic departments and divisions that comprise a college or university are institution-wide planning, budgeting, and gover- nance committees that seek to advance the interests of the organization as a whole. Proposals for new programs or initiatives to expand or modify exist- ing programs often need to be approved by faculty governance committees,

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as well as by planning and budgeting committees that are comprised mostly of administrators (Bess & Dee, 2008). Academic program developers can tailor their presentations of data to the expectations and preferences of the various parties involved in these processes. Faculty committee members, for example, might expect to see data that relate to the academic quality of the proposed curriculum, the qualifications of the faculty who will teach in the new or modified program, and the resources that will be available to support student success, such as advising, library materials, and instruc- tional technology. Administrators might expect to see data that focus on projected enrollments, revenues, and expenses. They might also expect that the proposed program will be linked to themes and priorities expressed in the institution’s strategic plan. Connecting academic program proposals to the strategic plan is important, because at many institutions, administrators allocate resources based on the extent to which a proposed initiative can contribute to the institution’s strategic priorities. Academic program developers can use data to make a compelling case that the proposed or modified program will advance institutional goals and priorities.

Before academic program developers can fulfill the data expectations of various committee members, they might need to assess the institution’s structural capacity for implementing the academic changes they are propos- ing. Program developers may be required to provide detailed information on the resources that will be needed to implement the new program or modify an existing program (Posey & Pitter, 2012). Institutions may re- quire that such proposals differentiate between resources that are currently available, and new resources that will need to be acquired or reallocated. In such cases, program developers will need to assess the capacity of ex- isting structures to support their proposal. Structural needs for program development include the availability of faculty, classroom and laboratory facilities, library and instructional technology resources, student advising and support services, and faculty development and training. In addition to considering the infrastructure needed to implement a proposed program, academic program developers can also assess the institution’s capacity for collecting and analyzing data to support academic proposal development. Large institutions may have sizable institutional research offices in which particular staff members have been assigned responsibility to support aca- demic program development. In this context, institutional research offices may routinely collect program-level data that track student performance, measure student engagement and satisfaction, and monitor retention and graduation rates. In smaller institutions, capacities for data collection and analysis may be more limited. In some cases, outside consultants or market research firms may be hired to provide data and analysis in areas for which the institution does not have sufficient expertise.

Organizational Culture. Although a consideration of external en- vironments and organizational structures can provide an understanding of the key stakeholders and venues in which decision making occurs, an anal-

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ysis of organizational culture offers insights into the values and beliefs that shape those decisions. Most conceptualizations of organizational culture suggest that it comprised a set of values and beliefs that are widely shared in an organization and that guide decisions and actions (Schein, 1992). The organizational cultures of most colleges and universities, however, seldom present a unified set of values and beliefs. Instead, higher education insti- tutions are characterized by multiple, and sometimes conflicting, subcul- tures (Bess & Dee, 2014). The subcultural differences that are most likely to affect academic program development include the divide between faculty and administrative subcultures, as well as the subcultural differences among faculty themselves, based on distinct traditions in the disciplines in which they have been trained. The tensions among subcultures are not necessar- ily dysfunctional. Higher education institutions have historically embraced divergent values and beliefs so that creative thinking can emerge and new knowledge can be produced. Most organizational members in higher educa- tion institutions will adhere strongly to their respective subcultural values. Thus, rather than try to get different subcultures to unite around common values, academic program developers can frame proposed changes in ways that connect to multiple value systems.

Given that proposals for new programs and changes to existing pro- grams often need to be approved by committees comprised of faculty and administrators, academic program developers must learn to work with both subcultures. In broad terms, the administrative subculture values consis- tency and efficiency, whereas the faculty subculture values freedom and flexibility (Kezar, 2014). These differences in values often lead to tensions between administrators, who seek to centralize and standardize practices (to promote efficiency and coordination), and faculty, who prefer decen- tralization and customized approaches (to preserve freedom of thought and action). An examination of these subcultures can also reveal differences in beliefs about data. In the context of significant pressures to demonstrate ac- countability, administrators might view data as a tool to measure effective- ness and efficiency. Faculty, in contrast, might be more skeptical about in- stitutional data. They might be concerned that data will be used punitively. For example, if student learning outcomes data show a decline in perfor- mance, then faculty may worry that administrators will blame them for the problem, rather than provide more resources to support student success.

A further difference between the administrative and faculty subcultures pertains to their motivation for academic program development. Faculty are motivated to engage in program development because they want to explore emerging trends within their disciplines and fields of study (Clark, 1996). If a new domain of knowledge becomes relevant within their disci- pline, then faculty may want to develop new courses or programs of study in that area. In contrast, administrators are motivated to engage in program development because such efforts can generate revenues and enhance the prestige and reputation of the institution. Although these motivations are

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not inherently contradictory, some scholars have questioned whether so many academic decisions should be guided by a revenue-generation logic (Slaughter & Rhoades, 2004). The focus on revenue generation might displace other historically valued goals of higher education, including initiatives that seek to advance the public good (Rhoads & Szelényi, 2011), as well as efforts to enhance programs in disciplines that are far removed from the market, such as the arts and humanities.

Although understanding the differences between administrative and faculty subcultures is important to successful academic program develop- ment, program developers can also consider the subcultural differences among faculty. Given the different ontological and epistemological assump- tions that characterize the various disciplines, faculty are likely to have quite different views regarding what constitutes effective teaching and what counts as valid research (Becher & Trowler, 2001). In terms of academic de- cision making, faculty may use their disciplinary research assumptions to assess the quality and validity of institutional data. If faculty are accustomed to using experimental designs with control groups in their own research, then they might view academic program proposals as lacking rigor if they contain only descriptive statistics and frequency counts. In contrast, if fac- ulty work in disciplines that primarily use qualitative methods, then they might question whether statistical reports can capture underlying meanings and unique experiences of people and groups. Furthermore, some depart- mental subcultures have a longer history of data-based decision making. Professional fields with accreditation, for example, have engaged in student outcomes assessment for many years. Their knowledge and familiarity with institutional data will likely be greater than departments with less engage- ment in assessment (Dill, 1999). Academic program developers will need to be sensitive to these disciplinary differences, particularly in two scenarios:

When a proposal for program development emerges in one department, but will be evaluated by faculty committee members from other departments

When a proposal for program development pertains to an institution-wide initiative, such as first-year seminars or general education, that will involve faculty from multiple disciplines

Although colleges and universities are fragmented into multiple subcul- tures, these institutions also have some overarching values and beliefs that characterize the organization as a whole, and which may affect academic program development. Birnbaum (1988) suggests that although colleges and universities have multiple sets of values and beliefs, each institution is characterized by a particular cultural model, such as collegial or bu- reaucratic. Research has shown that change processes, including academic program development, are more likely to be successful if organizational members align their strategies for change with the cultural values of their institution (Kezar & Eckel, 2002). For example, if the culture at an

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institution is primarily bureaucratic, then change agents might be more successful if they engage with formal committees and follow a clear chain of command. Specific dimensions of the organizational culture can also affect academic program development. The degree to which risk taking and innovation are valued in the organizational culture, for example, can signal whether new academic initiatives will be met with resistance or enthusiasm.

Organizational Power. The ability of organizational members to achieve outcomes that reflect their values depends, in large part, on the amount of power that they hold in the organization. In fact, the success of an academic program proposal depends not only on the quality of the data but also on the power possessed by the person or group presenting the proposal. Organizational power is derived from a variety of sources, in- cluding the formal authority associated with a hierarchical position such as dean or provost, as well as informal sources of power that emerge through social networks and interpersonal connections (Yukl, 2002). When viewed in terms of power dynamics, academic program development entails com- petition, negotiation, and bargaining, and requires political skills such as alliance building.

Power dynamics in colleges and universities emerge, in part, from the multiple and broadly framed goals of higher education. As noted earlier in this chapter, the goals of teaching, research, and service are somewhat vague, difficult to measure, and subject to multiple interpretations. These ambiguous goals do not provide an objective basis for academic decision making. Data-based arguments may not persuade organizational members who interpret the institution’s goals in different ways. Instead, negotiating, bargaining, and alliance building may be necessary to advance proposals for change. As Temple (2008) notes, an academic decision is often deemed suc- cessful not because it fulfills some rational objective standard, but because it attracts sufficient political support from key groups within the organization.

The shared governance committees described earlier in this chapter of- ten become venues for political conflict between faculty and administrators. The norms of shared governance suggest that decisions regarding curricu- lum, teaching, and learning should be delegated to faculty governance com- mittees, whereas decisions that involve budgets and institutional strategy should be determined by administrators who seek advice on those decisions from the faculty. Although this statement appears to clarify the shared gover- nance roles of faculty and administrators, in practice, the boundary between these domains is not entirely clear. The curricular decisions of faculty are likely to have budgetary implications, and the budget decisions of admin- istrators may limit the types of curricular decisions that faculty can make. Thus, when decisions involve curriculum, budgets, and strategy, some de- gree of conflict is likely to emerge between administrators and faculty.

The institutional budget-setting process is a particularly intense arena for conflict. As noted previously, some institutions allocate resources on the basis of enrollment. Departments that enroll more students receive a

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larger share of institutional funds. Although this arrangement serves as an incentive for program growth, it also pits departments against each other for funding. Milam and Brinkman (2012) note that “the addition of enroll- ments to an existing major or the creation of new majors or a program may have a significant impact on departments other than the one responsible for the major’s home” (p. 215). Department chairs and faculty may see aca- demic program development as a zero-sum game, in which enrollment and resource gains for new programs in other departments come at the expense of enrollments and resources for their department.

The power dynamics of program development become even more com- plicated in multicampus systems, where other institutions in the system may be able to veto proposals for programs that they believe would com- pete with their degree offerings. Nearly all institutions offer programs in the basic arts and sciences disciplines that provide the core undergraduate cur- riculum. Beyond those disciplines, the mix of academic programs offered by a particular institution may depend on the domains already occupied by other public higher education institutions in the state. If a flagship uni- versity has already established a school of engineering, for example, then another university in that same state system might be blocked from pur- suing a similar initiative. The extent to which a new program at one in- stitution competes with existing programs at other system institutions is, however, often a subjective matter. An institution might seek to develop a new program that occupies a unique niche in a particular field, but another university in the same state system might discount the unique aspects of the program, and instead see the proposal as a threat to their own enrollments and revenue.

The Decision Context

Along with the general context of the organization, the specific context of the decision shapes how the decision-making process will unfold. Im- portant components of the decision context include the type and scope of the decision being considered, the organization’s history with similar deci- sions, and the current stage of the decision-making process. These compo- nents can influence whether internal or external data are emphasized, and whether organizational members engage in objective or subjective analyses of those data.

Three types of decisions characterize academic program development: the creation of a new program, the expansion of an existing program, or the substantial modification of an existing program. If the decision pertains to the expansion or modification of an existing program, then organizational members will focus primarily on internal data. Academic program develop- ers may need to gather and analyze data regarding student performance and outcomes in the existing program (Posey & Pitter, 2012). If student perfor- mance and outcomes are problematic, then expansion of the program may

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not be warranted until the existing program is improved. In addition, pre- vious program reviews or accreditation reports can be examined to identify strengths and areas for improvement, which, in turn, can serve as the ba- sis for program expansion or modification (Bogue & Saunders, 1992). Al- though the focus will be directed mostly toward internal data, some external data might be needed to document trends in the discipline if the program is expanding into new subfields, or trends in professional practice if the pro- gram is modifying its curriculum to include new competencies to prepare students for employment more thoroughly.

In contrast to the internal data focus for expanding or modifying an existing program, new program development will rely primarily on exter- nal data. Program developers will need to consider data on the enrollment market for students and the labor market for graduates in the proposed pro- gram area. Data from government agencies, businesses, and industry groups might be needed if the new program is intended to fulfill workforce develop- ment needs in the state or region. Although the data focus for new program development will be mostly external, some internal data might be needed to assess the extent to which the institution can support the new program in areas such as faculty resources and facilities.

The scope of the decision will also affect the use of data. The scope of a program development decision may be institution-wide, such as a change in the general education curriculum, or specific to a particular department or program. If the scope of the decision is institution-wide, then data anal- ysis is likely to be more subjective and political. An institution-wide pro- posal might compel academic departments to change at least some of their courses, teaching practices, or academic policies. Such changes might pro- voke resistance from faculty and administrators who were not involved in developing the proposal (Esterberg & Wooding, 2012). Furthermore, the various academic departments are likely to have different interpretations of institutional goals and priorities, and their members might prefer that the institution pursue other initiatives, rather than what has been proposed. In contrast, if the scope of the decision relates only to a particular program or department, then data analysis might be more objective. Faculty and admin- istrators in other departments might defer to the expertise and judgment of their colleagues in the department that has proposed the change. If the pro- posed change will have minimal impact on other departments, then organi- zational members will likely not challenge or question how the proposing department has analyzed and interpreted supporting data. However, if the department’s proposal is seen as having major resource implications, then other departments might view the proposal in terms of a zero-sum competi- tion for resources (Milam & Brinkman, 2012), and the analysis of data then becomes more subjective and political.

The institution’s history with similar decisions also shapes the decision-making process. If the institution has made similar decisions in the past, and will likely continue to make such decisions in the future, then

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institutional leaders may create standard procedures for making these rou- tine decisions (Huber & McDaniel, 1986). For example, a community col- lege that creates a large number of workforce development programs may have standard procedures for interacting with local businesses, collecting relevant data, and creating new program proposals. When routine decision- making processes are used, data analysis is likely to be more objective, be- cause agreed-upon criteria for decision making have already been estab- lished. In contrast, when the institution has made few or no decisions in a particular domain, data analysis is likely to be more subjective. If the con- tent is unfamiliar to organizational members, then they might not know how to interpret the proposal. Here, multiple and potentially conflicting interpretations of the proposal may emerge, thus leading to a subjective analysis of data.

Finally, data usage will differ depending on the stage of the decision- making process (Mintzberg et al., 1976). At early stages, decision makers might use data to identify or understand a problem. In this scenario, data analysis would likely be subjective, because the specific problem has not been identified yet or is still open to interpretation. At middle stages, deci- sion makers may want data that help them select among several alternative courses of action. Here, data analysis would be somewhat objective, because the problem has now been defined and options for addressing the problem have been identified. For example, a department might discover that it has a problem with student retention. Department members could then examine student outcomes data and determine if students tend to depart at certain times during their program of study, or if performance in a particular course is associated with whether students persist in the major. Such analyses can suggest which alternative courses of action—enhanced advising, new teach- ing practices, or curricular changes—could improve retention rates. Finally, at later stages, data might be used to determine how best to implement a chosen course of action. At this stage, data analysis would likely be objec- tive, because a course of action has already been selected. For example, if a department has decided to implement first-year seminars to improve re- tention, faculty members may want data from other departments that have used this practice to determine if the seminars should be co-taught and/or paired with another required course.

Four Decision Models

The organizational context (environment, structure, culture, and power dynamics) and the decision context (type, scope, history, and stage of decision) influence how data will be used in decisions regarding academic program development. Specifically, these contextual variables shape the relative importance of internal and external data in the decision-making process. These variables can also suggest whether data are likely to be analyzed objectively or subjectively. When considered together, these two

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dimensions (internal/external and objective/subjective) yield four decision models: rational, entrepreneurial, political, and exploratory. This section of the chapter provides a brief explanation of each model. In addition, the four models are illustrated with examples from a case study of online program development at three community colleges. We refer to these colleges using pseudonyms.

Rational Model. The rational model is based on research that ex- amines managerial decision making. Researchers in this tradition suggest that decisions are more likely to enhance organizational effectiveness when managers follow a linear process that involves data collection and analysis (Dean & Sharfman, 1993; Fredrickson, 1984; March & Simon, 1958). The process typically begins with gathering and analyzing data on organizational performance. When performance is less than optimal, managers attempt to identify or diagnose the problems that might be interfering with the orga- nization’s capacity to achieve its goals. After a problem has been identified, managers will consider a range of options to address the problem, as well as identify the criteria they will use to select among those options. At this point in the process, managers can gather additional information about the feasibility and likely success of the various options they are considering. After managers select a particular course of action, they will allocate re- sources and direct personnel to implement the decision, as well as establish feedback mechanisms to gauge how well the decision addresses the prob- lem. This model assumes that data, analyzed objectively, will lead to choices that maximize the effectiveness of the organization.

The rational model depicts an ideal decision-making scenario in which people have all of the relevant information they need to make a decision, as well as sufficient time to analyze that information. Such favorable condi- tions, however, are unlikely to materialize for many decisions. Data might be unavailable, irrelevant to the problem, or overwhelming in volume and complexity. Even if the right data are available, people may lack the time or expertise to interpret them. Under these more typical conditions, or- ganizational members tend to consider just a few options that differ only slightly from the status quo. This simplified process enables people to ad- dress more quickly a larger number of organizational problems. But the problematic implication is that decision makers are not considering more innovative approaches to addressing organizational problems, and thus, the decisions that they make may have only a marginal impact on improving organizational outcomes. This simplified process, which Lindblom (1959) described as “muddling through,” suggests that organizational members at- tempt to make rational decisions, but they encounter shortcomings in their capacity to enact fully each step in the process. For the purpose of this chapter, we consider the rational model to include processes in which de- cision makers thoroughly gather and analyze data on a wide range of op- tions, as well as instances in which only a simplified version of the model is carried out.

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In the context of academic program development, the rational model draws attention to internal data about institutional performance and out- comes. Data on retention, degree completion, or student learning outcomes, for example, could reveal problems that need to be addressed at the level of specific academic programs or the institution as a whole. Although the focus is on internal data, decision makers are not necessarily neglecting external considerations, because accountability pressures from external stakehold- ers have been a strong driving force for the collection of internal data on institutional performance. The rational model also assumes that the par- ties involved in academic program development will analyze internal data objectively. Objective analysis usually depends on the parties having some sort of common understanding of the problem, as well as agreement on the criteria that will be used to select a course of action to address the problem.

Example of a Rational Decision. Decision makers at Zorn Valley Community College enacted a rational model to select a new learning man- agement system (LMS) for online courses. Administrators detected a prob- lem with the college’s existing LMS when they examined internal data pro- vided by faculty and students. “There were a lot of complaints about it,” noted one administrator. “We had some irate, unhappy faculty who were threatening to no longer use [the college’s LMS], and we were going to lose them [from the online program].” A staff member in the academic tech- nology office reported that “students were yelling that it [the LMS] wasn’t working with the version of the browser they were using.” The provost de- cided that the issue constituted a threat to the quality of online education at the college, and therefore asked the Academic Technology Committee to investigate options for change.

The Academic Technology Committee created a separate subcommittee to explore LMS alternatives. The subcommittee had broad representation from faculty, technology staff, and academic administration. An adminis- trator noted that the decision process “was very collegial, collaborative; ev- eryone felt they played a role in it and had some input.” The subcommittee identified six LMS options. After reviewing information provided by ven- dors and after speaking with colleagues at other colleges who were using a variety of systems, the committee narrowed its choice to three alternatives.

Each of the three LMS finalists was then piloted in two separate online courses. These live pilots allowed data to be gathered from six experienced online instructors and their students. The piloting faculty members filled out detailed questionnaires and used a common rubric to rate their LMS. An administrator noted that the rubric provided specific data about each component of the LMS: “they took one piece, such as grading. Then, two faculty looked at grading in each of the systems. It was a really thorough process.” Although the subcommittee used some external data from ven- dors and other colleges, the focus was primarily on internal data provided by students and faculty. The parties involved in the process—administrators,

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technology staff, and faculty—had a similar understanding of the problem, and they developed an objective rubric for selecting an LMS from the op- tions they were considering.

Entrepreneurial Model. As with the rational model, the en- trepreneurial model assumes that data will be analyzed objectively, but the focus shifts to external data sources. The entrepreneurial model becomes relevant when organizational members perceive an opportunity in the ex- ternal environment (Clark, 1998; Keller, 1983). The opportunity might be a new student market or an emerging academic subfield that has potential for growth. Organizational members may then collect data from the exter- nal environment to confirm that the opportunity actually exists, and to de- termine whether the institution should pursue that opportunity. To make these assessments, decision makers would likely collect data on projected enrollments, labor market demand for graduates, and instructional trends within the discipline or field of study. They might also consult external stakeholders, such as government policy makers and employers, particu- larly if the opportunity relates to addressing workforce development needs. Finally, if organizational members decide to pursue a particular opportunity, they could collect additional data regarding how best to implement their decision. Here, academic program developers could consider how other in- stitutions have designed and implemented similar programs.

Huber (1991) has described this type of external data collection as fo- cused search. When organizational members engage in focused search, they seek to acquire information about a specific opportunity that they want to pursue. Focused search depends on having organizational members who are willing to innovate and take risks with new ideas that they observe in the external environment. The potential downside of focused search is that organizational members may become so committed to pursuing an identi- fied opportunity that they no longer objectively analyze the external data. They might discount indicators of weak student demand, for instance, and push forward with developing a new program anyway.

The entrepreneurial model assumes that organizational members have set clear goals and priorities to guide their engagement with the external environment (Alfred, 2001). Given limited resources, organizations cannot pursue every opportunity that emerges in the external environment. Goals and priorities help organizational members decide which opportunities to pursue. In higher education institutions, these goals and priorities are often expressed in strategic plans. These plans might include specific strategies for academic program growth and development. An objective analysis of external data could help organizational members determine how best to implement these strategic initiatives.

Example of an Entrepreneurial Decision. Administrators and fac- ulty at Wilder Community College decided to put an allied health pro- gram online. The existing on-campus program consistently drew large

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enrollments, and given limited resources for new facilities, the online en- vironment was viewed as the best way to expand the program. As a faculty member explained, “it is sort of like ‘if you build it, they will come.’ Ev- ery community college in the state has a problem with way more qualified applicants than available spaces [for this type of health program].” Others explained the idea as a way to advance the college’s strategic goal of putting as many courses and programs online as possible, in order to expand ac- cess and stabilize institutional revenues. Both administrators and faculty expressed excitement about being the first college in the state to have such a health program online.

The novelty of the proposal, however, meant that faculty and admin- istrators at Wilder were unsure about how best to put this program on- line. They recognized that they needed to gather more data. Administrators granted a sabbatical to a faculty member in the existing on-campus pro- gram. The sabbatical allowed the faculty member to collect data on simi- lar programs in other states. The faculty member found that most of these programs were at the 4-year or graduate level. In fact, only one commu- nity college program was identified. “We invited a faculty member from that [community college] program to come here and meet with us,” re- called an administrator. “She gave us some pointers.” The primary lesson that came from studying other programs was the value of students demon- strating their comprehension of course learning outcomes through online discussion boards. This approach was incorporated into the courses devel- oped for the Wilder online health program.

Political Model. Although the rational and entrepreneurial models depend on objective data analysis, the political model is characterized by subjective analysis. The model notes that many individuals and groups are involved in organizational decision making, and the values, interests, and goals of these individuals and groups will often conflict and produce dif- ferent interpretations of the same data (Allison, 1971; Pfeffer & Salancik, 1974). According to the political model, decisions are the product of bar- gaining and competition among organizational members who have varying degrees of power. Data and information are tools in a competition for power, rather than evidence to help make an objective judgment. Organizational members may look only for information that supports their own position or that undermines the position of their adversaries. Once they obtain in- formation, they may hide, manipulate, or release it selectively to influence decisions.

The political model assumes that multiple individuals and groups are involved in the decision-making process because no single individual has the power, expertise, or information to make decisions alone. Those as- sumptions also guide the practices of shared governance, in which many actors play a role in institutional decisions. Governance committees, in fact, can be viewed as venues in which the political interests of various groups

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are expressed and negotiated (Baldridge, 1971). Although the involvement of actors from different parts of an institution could lead to dysfunctional conflict, it could also be the basis for cooperation, including the sharing of data and the incorporation of multiple points of view in the interpretation of those data. In some cases, political behavior can actually improve deci- sions (Rahim, 2002). Organizational politics can alert decision makers to sources of opposition that could be detrimental to the implementation of the decision. Political behavior also compels decision makers to consider a range of viewpoints, which may lead to a reframing of the problem or to the consideration of new alternatives. This model also allows organizational members to express their voices and feel involved in the process, even if the final decision does not reflect their views.

Example of a Political Decision. A decision about whether to con- tinue to offer two biology courses online at Yankee Community College followed a political model. The Dean of Continuing Education at Yankee made regular announcements to all faculty asking if they wished to put classes online. None of the full-time biology professors expressed an in- terest; however, an adjunct instructor agreed to move two existing biol- ogy classes with lab components into an online format. The lab portion of these two courses included a variety of simulations and virtual tools that attempted to replicate the experience of a hands-on lab. These two courses ran this way for nearly 5 years without issue. Eventually, however, full-time biology faculty began to raise questions about these courses. They asked why the courses had not been developed by a full-time professor. More fun- damentally, they challenged the idea that the student experience in the lab could be successfully simulated online. An administrator noted that “the biologists have taken the position that the lab is the lab, and if you are not [physically] in a lab, then you’re not in a lab.” Hands-on experience in the lab was viewed as crucial by these faculty members. As a full-time profes- sor argued, “there is that tactile aspect to science where students should be handling the equipment and the different tools.”

The Dean worked to allay these concerns. The full-time faculty were invited to look at the online course materials and also to view demonstra- tions of the types of laboratory simulations that were available online so that they could see their sophistication and quality. Furthermore, the Dean gathered data on the success of the students who had taken the online lab courses. As another administrator indicated, the data showed that “the course completion was fine. . . Students who took [the] online course went on to other courses and were just as successful; so there was no divergence in the grades.”

These data did not assuage the full-time biology faculty. “The data did exist for that, but it did not matter,” said one full-time instructor. The is- sue was one of principle and did not hang on course completion rates as far as the full-time biology professors were concerned. An administrator

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remarked that “the biology department has taken a firm theological posi- tion that you cannot teach a lab science online, period, end of conversation, thank you very much.”

Given the intensity of this opposition and the authority of the Dean of Continuing Education to keep the courses online, the full-time biology faculty chose to take the issue to the college senate. The full-time biology professors brought forward a motion demanding that science classes with labs must have approval from their respective academic department before they can be offered online. An administrator described the senate meeting at which the vote was taken. “The person [from the biology department] got up and gave an impassioned plea, and they [senate members] bought it.” The biology professors’ motion passed by a large margin.

The impact of this decision was substantial. Existing online science courses with labs now had to go through the curriculum approval process, the first step of which was department review. As the biology department ap- proved of neither of the two online biology lab courses, these courses were forced out of the online program. Students who needed a biology course for their program of study found that they could no longer complete their requirements online. According to a frustrated administrator, “it is really creating an issue for us, because it is preventing us from doing some com- plete degree programs [online].”

Exploratory Model. The exploratory model describes a scenario in which organizational members are engaged in subjective analyses of ex- ternal data. Although both the exploratory model and the entrepreneurial model focus on external data, the two models are characterized by some im- portant differences. In the entrepreneurial model, decision makers have al- ready identified an opportunity in the external environment, and they have decided to conduct a focused search for more information about the fea- sibility of pursuing that opportunity. In contrast, the exploratory model is enacted through a general scanning of the environment. This type of gen- eral scan is conducted not to explore a particular opportunity, but rather to gather information about external trends and emerging practices in the field. Exploratory data analysis is subjective because, in these cases, organi- zational members have not identified a particular problem to solve or estab- lished an agreed-upon course of action, in contrast to the entrepreneurial model, which presumes consensus about how to move forward with an identified opportunity. According to March (1991), there is an interac- tive relationship between the entrepreneurial and exploratory models. A general exploration of the environment is likely to identify specific op- portunities that could be exploited through entrepreneurial decisions and actions.

The exploratory model may be particularly relevant during periods of uncertainty (Mintzberg, 1990). The other three models describe scenarios in which people have some degree of certainty and clarity. In the ratio- nal model, decision makers are focused on a particular problem to solve.

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The entrepreneurial model focuses on a specific opportunity to exploit. The political model focuses on a particular conflict to adjudicate or negotiate. In contrast, the exploratory model assumes that organizational members are unsure about how to proceed, but they are willing to embrace uncertainty as an opportunity to learn. In academic program development, the exploratory model can describe the behaviors of faculty, staff, and administrators who seek to prepare themselves for making effective decisions about program growth and development. When they lack certainty about how to proceed, academic program developers can seek external data by attending confer- ences, participating in consortiums, and studying what other institutions are doing.

Example of an Exploratory Decision. A general concern for qual- ity motivated online program developers at Wilder Community College to engage frequently in environmental scanning. An administrator noted that “because distance learning is still somewhat new for a lot of people, it prob- ably gets more scrutiny than other programs. We have to make sure that we are up to speed on what’s going on in the field.” Faculty and administra- tors frequently attended conferences about online learning, and they stayed in touch with colleagues at other institutions who were involved in online programs in similar fields.

Furthermore, Wilder Community College was an active member in a statewide consortium on online education, and the institution had estab- lished an informal partnership with one of the state universities. As an ad- ministrator explained, the partnership involved training and professional development. “They [faculty and administrators at the state university] gave us all kinds of information, [and they] came down and did seminars for our faculty to get them up to speed in the online environment.” This administra- tor also noted that the partnership helped Wilder Community College deal with the uncertainties associated with developing new online programs. “At the time, [the partnership] was sort of our insurance policy that getting into this, we were at least going to get into it successfully. We were not going to make all the mistakes they [the state university] made; they made sure of that and they mentored us.”

When Wilder Community College needed to select a new hosting so- lution for its online course delivery, rather than engage in a detailed rational analysis, as Zorn Valley did to select a new LMS, decision makers instead turned to their state university partner. They selected the state university as the venue to provide the servers to host Wilder’s online courses. An admin- istrator explained that the decision “seemed like a no-brainer.”

Conclusion

When academic program developers understand the context of the organi- zation and the context of the decision that they are pursuing, they will be better able to anticipate which decision-making model is likely to emerge

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Table 1.1. Influence of Selected Contextual Variables on the Decision Process

Model Likely to Emerge Organizational Context Decision Context

Rational model E: stable S: centralized C: bureaucratic P: common interests

Type: existing program Scope: single department History: familiar Stage: later

Entrepreneurial model E: dynamic S: centralized C: risk-taking, innovative P: common interests

Type: new program Scope: single department History: familiar Stage: later

Political model E: stable S: decentralized, multilayered C: bureaucratic P: divergent interests

Type: existing program Scope: institution-wide History: unfamiliar Stage: early

Exploratory model E: dynamic S: decentralized, multilayered C: risk-taking, innovative P: divergent interests

Type: new program Scope: institution-wide History: unfamiliar Stage: early

Note: E = environment, S = structure, C = culture, P = power dynamics.

(rational, entrepreneurial, political, or exploratory). Data are used and interpreted in different ways based on the model that emerges. If academic program developers can anticipate which model will emerge, then they will be better prepared to address the data needs and expectations of key stakeholders in the decision. For example, if the rational model is likely to emerge, then they can gather and distribute internal data and assist in an objective analysis of those data. If the political model is likely to emerge in- stead, they can consider how the various stakeholder groups will interpret internal data in different ways. If the entrepreneurial model is anticipated, then program developers can focus on external data and facilitate an ob- jective appraisal of environmental conditions. If the exploratory model is expected, then program developers can engage their professional networks and bring external data into the institution, where it will be interpreted in a variety of ways.

Table 1.1 indicates how some contextual variables can affect which decision-making model is likely to emerge. The table does not provide a complete listing of all possible variables, but it can help program develop- ers make an initial assessment of the context and thereby anticipate which decision-making model they are likely to encounter. Furthermore, the pro- cess for a particular decision may shift from one model to another. Program developers can continue to monitor the organizational and decision con- texts throughout the decision-making process, and thus prepare themselves for these types of changes.

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References

Alfred, R. (2001). Strategic thinking: The untapped resource for leaders. Community College Journal, 71(3), 24–28.

Allison, G. (1971). Essence of decision: Explaining the Cuban missile crisis. Boston, MA: Little, Brown.

Baldridge, J. (1971). Power and conflict in the university. New York, NY: John Wiley. Becher, T., & Trowler, P. R. (2001). Academic tribes and territories: Intellectual inquiry and

the culture of disciplines (2nd ed.). Buckingham, United Kingdom: Open University Press/Society for Research into Higher Education.

Bess, J., & Dee, J. (2008). Understanding college and university organization: Theories for effective policy and practice (Vols. 1 and 2). Sterling, VA: Stylus Publishing.

Bess, J., & Dee, J. (2014). Bridging the divide between faculty and administration: A guide to understanding conflict in the academy. New York, NY: Routledge.

Birnbaum, R. (1988). How colleges work: The cybernetics of academic organization and leadership. San Francisco, CA: Jossey-Bass.

Bogue, E., & Saunders, R. (1992). The evidence for quality: Strengthening the tests of aca- demic and administrative effectiveness. San Francisco, CA: Jossey-Bass.

Bok, D. (2003). Universities in the marketplace: The commercialization of higher education. Princeton, NJ: Princeton University Press.

Clark, B. (1983). The higher education system: Academic organization in cross-national perspective. Berkeley: University of California Press.

Clark, B. (1996). Substantive growth and innovative organization: New categories for higher education research. Higher Education, 32, 417–430.

Clark, B. (1998). Creating entrepreneurial universities: Organizational pathways of trans- formation. New York, NY: Pergamon.

Dean, J., & Sharfman, M. (1993). Procedural rationality in the strategic decision-making process. Journal of Management Studies, 30, 587–610.

Dill, D. (1999). Academic accountability and university adaptation: The architecture of an academic learning organization. Higher Education, 38, 127–154.

Elbanna, S., & Child, J. (2007). The influence of decision, environmental, and firm characteristics on the rationality of strategic decision-making. Journal of Management Studies, 44(4), 561–591.

Esterberg, K. G., & Wooding, J. (2012). Divided conversations: Identities, leadership, and change in public higher education. Nashville, TN: Vanderbilt University Press.

Fredrickson, J. (1984). The comprehensiveness of strategic decision processes: Exten- sion, observations, future directions. Academy of Management Journal, 27, 445–466.

Heineman, W. (2011). The role of data in decision making about online distance education: A case study of three community colleges (Unpublished doctoral dissertation). University of Massachusetts, Boston.

Huber, G. (1991). Organizational learning: The contributing processes and the litera- tures. Organization Science, 2(1), 88–115.

Huber, G., & McDaniel, R. (1986). The decision-making paradigm of organizational design. Management Science, 32(5), 572–589.

Keeling, R., Underhile, R., & Wall, A. (2007). Horizontal and vertical structures: The dynamics of organization in higher education. Liberal Education, 93(4), 22–31.

Keller, G. (1983). Academic strategy: The management revolution in higher education. Bal- timore, MD: Johns Hopkins University Press.

Kezar, A. (2014). How colleges change: Understanding, leading, and enacting change. New York, NY: Routledge.

Kezar, A., & Eckel, P. (2002). The effects of institutional culture on change strategies in higher education: Universal principles or culturally responsive concepts. Journal of Higher Education, 73(4), 443–460.

NEW DIRECTIONS FOR INSTITUTIONAL RESEARCH • DOI: 10.1002/ir

34 ROLE OF INSTITUTIONAL DATA IN HIGHER EDUCATION

Kezar, A., & Lester, J. (2009). Organizing higher education for collaboration: A guide for campus leaders. San Francisco: Jossey-Bass.

Lindblom, C. (1959). The science of muddling through. Public Administration Review, 19, 79–88.

March, J. (1991). Exploration and exploitation in organizational learning. Organization Science, 2, 71–87.

March, J., & Simon, H. (1958). Organizations. New York, NY: John Wiley. Milam, J., & Brinkman, P. (2012). Building cost models. In R. Howard, G. McLaughlin,

& W. Knight (Eds.), The handbook of institutional research (pp. 203–220). San Fran- cisco, CA: Jossey-Bass.

Mintzberg, H. (1990). The design school: Reconsidering the basic premises of strategic management. Strategic Management Journal, 11(3), 171–195.

Mintzberg, H., Raisinghani, D., & Theoret, A. (1976). The structure of “unstructured” decision processes. Administrative Science Quarterly, 21, 246–275.

Molesworth, M., Nixon, E., & Scullion, R. (2009). Having, being, and higher educa- tion: The marketization of the university and the transformation of the student into consumer. Teaching in Higher Education, 14(3), 277–287.

Morphew, C., & Hartley, M. (2006). Mission statements: A thematic analysis of rhetoric across institutional type. Journal of Higher Education, 77(3), 456–471.

Papadakis, V., Lioukas, S., & Chambers, D. (1998). Strategic decision-making pro- cesses: The role of management and context. Strategic Management Journal, 19, 115–147.

Pfeffer, J., & Salancik, G. (1974). Organizational decision making as a political process: The case of a university budget. Administrative Science Quarterly, 19, 135–151.

Posey, J., & Pitter, G. W. (2012). Supporting the provost and academic vice president. In R. Howard, G. McLaughlin, & W. Knight (Eds.), The handbook of institutional research (pp. 145–164). San Francisco, CA: Jossey-Bass.

Rahim, M. (2002). Toward a theory of managing organizational conflict. International Journal of Conflict Management, 13(3), 206–235.

Rhoads, R., & Szelényi, K. (2011). Global citizenship and the university: Advancing so- cial life and relations in an interdependent world. Stanford, CA: Stanford University Press.

Schein, E. (1992). Organizational culture and leadership (2nd ed.). San Francisco, CA: Jossey-Bass.

Schmidtlein, F. (1999). Common assumptions about organizations that mislead institu- tional researchers and their clients. Research in Higher Education, 40(5), 571–587.

Shepherd, N., & Rudd, J. (2014). The influence of context on the strategic decision- making process: A review of the literature. International Journal of Management Reviews, 16, 340–364.

Slaughter, S., & Rhoades, G. (2004). Academic capitalism and the new economy: Markets, state, and higher education. Baltimore, MD: Johns Hopkins University Press.

Tarter, J., & Hoy, W. (1998). Toward a contingency theory of decision making. Journal of Educational Administration, 36(3), 212–228.

Temple, P. (2008). The integrative university. Perspectives: Policy and Practice in Higher Education, 12(4), 99–102.

Terenzini, P. (1993). On the nature of institutional research and the knowledge and skills it requires. Research in Higher Education, 34(1), 1–10.

Tierney, W. (2001). Why committees don’t work: Creating a structure for change. Academe, 87(3), 25–29.

Toma, J. D. (2007). Expanding peripheral activities, increasing accountability demands, and reconsidering governance in U.S. higher education. Higher Education Research and Development, 26(1), 57–72.

NEW DIRECTIONS FOR INSTITUTIONAL RESEARCH • DOI: 10.1002/ir

UNDERSTANDING THE ORGANIZATIONAL CONTEXT OF ACADEMIC PROGRAM DEVELOPMENT 35

Wellman, J. (2006). Rethinking state governance of higher education. In W. Tierney (Ed.), Governance and the public good (pp. 51–72). Albany, NY: State University of New York Press.

Yukl, G. (2002). Leadership in organizations (5th ed.). Upper Saddle River, NJ: Prentice Hall.

JAY R. DEE is associate professor and director of the Higher Education program at the University of Massachusetts, Boston.

WILLIAM A. HEINEMAN is vice president of academic and student affairs at North- ern Essex Community College in Massachusetts.

NEW DIRECTIONS FOR INSTITUTIONAL RESEARCH • DOI: 10.1002/ir

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