Microsoft Word - 169+redlines_JH_final.docx VOL. 11, NO. 1 SPRING 2022 The Great Generalization Organizational Adaptation Strategies as Entrepreneurship in Higher Music Education Jacob Hertzog University of Arkansas ABSTRACT: This study sought to measure how higher music education has evolved in response to the music industry’s digital revolution. I utilized a framework of organ- izational adaptation theory to synthesize five distinct organizational adaptation strat- egies: decentralization, generalization, specialization, formalization, and inaction. Music leaders were surveyed (n = 100) to assess adaptations across ten common do- mains in higher education. Higher music education was found to have undergone a great generalization through the expansion of activities in nearly every domain. Con- sistent with elements of organizational adaptation theory, and like individual musi- cians, higher music education has been entrepreneurial in response to the digital revolution. KEYWORDS: organizational adaptation, higher music education, organi- zational adaptation strategy, organizational entrepreneurship, decentralization, gen- eralization, digital revolution, music industry, music unit, music leader, great gener- alization, formalization, specialization, inaction, music, music education. DOI: doi.org/10.34053/artivate.11.1.169 Introduction Musicians are natural entrepreneurs. For centuries, composers, performers, and music makers, in addition to their art, have applied their boundless creativity to commerce and organization building (Krueger, ; Myers, ; Tschumck, ). Haynes and Marshall argue that “‘mu- sician’ and ‘entrepreneur’ are two sides of the same coin, reflecting the same cultural root within capitalist modernity” (, p. ). The music industry of the twentieth century gave rise to en- trepreneurial record labels, music publishers, venues, and concert promotors (Hull et al., ). ARTIVATE 11.1 2 The very genesis of hip-hop, currently the most popular musical genre worldwide, synthesized musical expression with entrepreneurial virtuosity to break through to mainstream audiences (International Federation of the Phonographic Industry [IFPI], ; Snell & Söderman, ). Due to the music industry’s digital revolution and rise of new economic models, the era of the recorded music industry waned in the early s, so music creators and music firms adopted new business strategies, new forms of art, and new ways to connect with audiences (Fairchild, ; Morris, ; Smith & Telang, ; Tschmuck, ). Economist Alan Krueger () argued that innovations in music have influenced innovation across other cultural industries and the economy as a whole. Against this backdrop of innovation, the educational systems that train musicians have been subject to the same artistic and market forces that have produced entrepreneurial behavior from music creators (Gandre, ; Miller, ; Tschmuck, ). Music programs in colleges, universities, and conservatories (higher music education) evolved alongside the modern music industry; thus they face the same challenges (Butt, ; Gandre, ). Contrary to the music industry’s slow evolution in the twentieth century however, the digital revolution in the music industry presented higher music education with an environmental paradigm shift demanding discipline-wide evolution. This study sought to explore higher music education’s response to this digital revolution. While previous research has focused on individual programs (Kelman, ), music administrators (Sorensen, ), or music students (Bennett, ), this study uti- lized a framework of organizational adaptation for a field-level approach. Drawing upon nine major theories of organizational adaptation, I created an organizational adaptation strategy ty- pology to operationalize and measure the strategies that individual music programs are employ- ing as a response to the digital revolution. The major research question in this study was: What organizational adaptation strategies are music programs using to adapt to the digital revolution in the music industry? Within this question this paper address two specific levels of inquiry: . What organizational adaptation strategies do programs exhibit in specific domains? . What organizational adaptation strategies does the entire field exhibit? In light of the connection between individual musicians and entrepreneurial behavior, this study also sought to compare these organizational adaptation strategies to organizational entre- preneurship on a broad scale. Theoretical Framework Major mid-century theories of organizational adaptation describe multiple pathways through which organizations respond to a changing or turbulent environment (Aldrich, ; Khand- walla, ; Sporn ). From nine major theories and Chaffee’s () constructs of organi- zational strategy, I synthesized a typology of five organizational adaptation strategies to serve as the framework for this study. Entrepreneurial behavior is present across several of these major organizational adaptation theories. As Sarasvathy () writes: “The essential agent of ARTIVATE 11.1 3 entrepreneurship is an effectuator: an imaginative actor who seizes contingent opportunities and exploits any and all means at hand to fulfill a plurality of current and future aspirations” (p. ). In their exploration of arts entrepreneurship, Callander and Cummings () highlight “remaking: renewal” as one of multiple dimensions of entrepreneurship. Relevant to academic organizations is the conceptualization of “entrepreneurship as a process for reorganizing an ex- isting organization or institution” (p. ). This type of entrepreneurial remaking and renewal through reorganizing and expanding is characterized by the organizational adaptation strategy of generalization: the expansion of organizational activities without changing organizational structure. Generalization as an organizational adaptation strategy is well represented in prior literature. Quinn and Cameron’s () work on the life cycles theory of organizations describes the formative stage of an organization’s existence as entrepreneurial—a time when organiza- tions are occupied with “innovation, niche formation and creativity” (p. ). Aldrich’s () contributions to population ecology theory describe the need for organizations to expand their activities when the shape of their environmental niche evolves. In resource dependence theory, organizations need to generalize their scope of activities to acquire new resources (Pfeffer & Salancik, ). Strategic choice theory, the backbone of contemporary management literature, describes the concept of domain offence by which organizations seek to exploit new weaknesses in a changing environment (Child, ). In contrast to generalization, four other organizational adaptation strategies can be synthe- sized from existent organizational adaptation theory: () decentralization, () specialization, () formalization, and () inaction. In () decentralization, organizations respond to change by breaking their structure into more semi-autonomous units. This behavior is described in stra- tegic choice theory (Child, ) as domain creativity, wherein organizations create new struc- tures to expand into new environments. Contingency theory posits that organizational struc- tures become more organic and fluid in challenging environments (Donaldson, ; Lawrence & Lorsch, ). Decentralization represents the late stage of Quinn and Cameron’s () life cycles model; elaborate structures are a hallmark of mature organizations. More contemporary theories such as the network organization theory (Powell, ) highlight the power of infor- mation sharing and lateral structures within organizations. The antithesis of generalization is an organizational adaptation strategy of () specializa- tion in which an organization “doubles-down” and either invests greater resources in current activities or eliminates fringe activities (Bastedo, ; Child, ; Gumport & Snydman, ; Sporn ). Population ecology theory prescribes this strategy as occurring when organiza- tions face changing amounts of environmental resources (Aldrich, ). The domain defense concept within strategic choice (Child, ) additionally positions organizations as choosing to focus on only their core strengths. Contrasting with the strategy of decentralization is () formalization: an organizational adaptation strategy that consists of creating organizational structures around previously fringe activities. (Birnbaum, ; Cameron & Quinn, ; Child, ; Khandwalla, ). Major top-down action is present in cybernetic theories (Ashby, ) as well as the theory of symbolic action (Pfeffer, ) where leadership plays a powerful role in organizational direction. The aforementioned life cycles theory includes a ARTIVATE 11.1 4 consolidation stage encompassing a formalization strategy, and resource dependence theory as- cribes centralizing managerial control as an effective way to manage dependencies (Quinn & Cameron, ; Pfeffer & Salancik, ). Finally, in the face of dramatic environmental change, an organization can also do nothing. () Inaction as an adaptation strategy is commonly at- tributed to lack of decision-making abilities (Khandwalla, ) or the general isomorphic prin- cipals often at play in organizations with strong professional traditions such as higher education (Donaldson, ). Table  displays each synthesized organizational adaptation strategy with its operational definition and major theoretical sources. Table 1: Typology of Five Organizational Adaptation Strategies Synthesized from Nine Major The- ories Strategy Definition Theoretical Sources Decentralization An organization splits its structure into a greater number of autonomous or semi-auton- omous units. Contingency Theory (Lawrence & Lorsch, ); Network The- ory (Powell, ); Life Cycles (Cameron & Quinn, ) Generalization An organization diversifies its activities with- out substantial alteration in organizational structure. Life Cycles (Cameron & Quinn, ); Population Ecology (Al- drich, ); Strategic Choice (Child, ) Inaction An organization makes no changes, either in- tentionally, or unintentionally. Institutional Isomorphism (Di- Maggio & Powell, ); Cyber- netics (Ashby, ) Specialization An organization “doubles-down” and invests a greater share of its resources and energy in its current activities or eliminates less-effective activities. Population Ecology (Aldrich, ); Strategic Choice (Child, ); Formalization An organization strengthens managerial con- trol over all activities or builds structure around fringe activities in order to exert cen- tralized direction. Life Cycles (Cameron & Quinn, ); Cybernetics (Ashby, ); Symbolic Action (Pfeffer, ); Resource Dependence (Pfeffer & Salancik, ) Literature Review Scholarship on higher music education has blossomed over the last twenty years, and Jørgensen () argues that “research into higher music education has come of age” (p. ). However, little of that scholarship focuses on the way in which higher music education reacts and interacts with the music industry (Jørgensen, ). Scholars as diverse as Khandwalla () (organiza- tional studies), Gandre () (higher music education), and Tschmuck () (music indus- try), all demonstrate that the organizational environment for higher music education is the mu- sic industry; thus titanic economic shifts in the music industry would necessitate adaptation in higher music education. Tschmuck () comments specifically on the “obligation of music education institutions to provide such knowledge and skills for a new generation of ARTIVATE 11.1 5 artepreneurs” (p. ). The obligation described by Tschmuck () stems from the paradigm shift caused by the digital revolution in the music industry. Beginning with the proliferation of digital recording— the launch of Napster in the late s and culminating in the rise of digital streaming plat- forms—each component of the music industry transformed to become more complex and de- mocratized for music creators (Fairchild, ; Tschmuck, ). The recorded music industry, previously based upon the manufacture and sale of physical recordings, is now dominated by major technology firms that control music streaming and digital distribution (Nordård, ). This has created a paradox of access wherein musicians have greater ability to create, distribute, and promote music, yet the music industry’s skewed distribution of revenue and notoriety in favor of the top one percent of artists has only increased (Coelho & Mendes, ; Krueger, ). This same trend plays out in the arena of live music where musicians have capitalized on touring to replace income generated by physical sales, driving up concert ticket prices (Krueger, ). As social media platforms have become the major modes of music discovery, and entre- preneurial virtuosity becomes the coin of the realm for music creators, this paradox of access in the music industry portends profound change within higher music education (Krueger, ; Mulligan, ). Gandre’s  historical study of the seven independent conservatories in the United States dating back to the nineteenth century represents an impressive body of scholarship on how music institutions have adapted to their changing environment. Through creative expansion, conservatories weathered shifts in enrollment, financial challenges, and new demands from stu- dents and faculty (Gandre, ). Gandre’s work precludes the digital revolution, but it show- cases numerous examples of organizational behavior consistent with the strategies previously described. While not an empirical study, the history of the National Association of Schools of Music (NASM)—the specialized accrediting body for music—illustrates how the field of higher music education evolved into a more formalized and regulated academic discipline (NASM, ). Similar to critiques of accreditation across higher education (e.g., Young et al., ), NASM’s history additionally elucidates how accreditation in music has both facilitated and pre- vented change (NASM, ). Perspectives on individual actors in higher music education occupy many of the conversa- tions around adapting to the digital revolution. Sorensen’s () survey of department chairs found that the lived experiences of music department chairs highly influence their efforts to lead change. Studies of music students and alumni frequently highlight the challenges faced by mu- sicians trying to prepare for the post-digital music industry. Bennett’s () work demon- strated the need for greater integration of music industry business training into curricula across higher music education. Addressing this need is the subject of Kelman’s () research on en- trepreneurial learning in music. Her hands-on learning projects guide students in practicing music industry skills such as artist booking, management, and entrepreneurship. While change among literature in higher music education is rare, literature on the need for change in higher music education is abundant and profound. Owing to the digital revolution in the music industry, “[t]he rationale for change in higher music education comes in part from ARTIVATE 11.1 6 the increasing prevalence of complex careers across the labour market” (Rowley & Bennett, , p. ). A landmark report by the College Music Society (CMS) in  challenged music programs to provide an “option rich environment” for students that reflected the breadth and diversity of contemporary music careers (College Music Society, , p. ). This sentiment is echoed by numerous scholars and practitioners, along with the idea that entrepreneurship itself be included in all post-secondary musical training (Bartlett & Tolmie, ; Bennett, ; Har- rison & Grant, ; Kelman, ; Latukefu & Ginsborg, ; Myers, ; Teague & Smith, ; Tschmuck, ; Young, ). Across higher education literature, organizational adaptation is both a singular topic and one woven into the fabric of the discipline. Studies focusing exclusively on organizational adap- tion, such as Sporn’s () study of six major universities or Hilbun’s () work on liberal arts colleges, prove the value of organizational adaptation as a powerful analytical tool in higher education. Furthermore, higher education is the setting for foundational ideas within organiza- tional adaptation theory, such as Cameron’s () work on post-industrial environments and Birnbaum’s () theories on cybernetic principals in higher education. These studies share an institutional-level perspective on multiple challenges faced by colleges and universities ranging across political, economic, organizational, and social issues (Birnbaum, ; Hilbun, ; Spron, ). Common themes spanning the spectrum of higher education scholarship are present in studies of both organizational adaptation in higher education and higher music education re- search specifically. In both fields, bodies of literature exist in ten clear domains. Each domain reflects research documenting change, or the need for change, in some aspect of higher music education: . Curriculum (e.g., Bennett, ; CMS, ; Myers, ; Young, ). . Co-Curriculum (e.g., Kelman, ). . Full-Time Faculty (e.g., Bok, ; Miller, ; Parkes, ). . Part-Time Faculty (e.g., Miller, ; Parkes, ; Stanley, ). . Admissions Policies (e.g., Kajikawa, ; Snell & Söderman, ; Tschmuck, ). . Leadership (e.g., Child, ; Khandwalla, ; Sorensen, ; Sporn, ). . Online Curriculum (e.g., CMS, ; NASM, ; Myers, ; Spilker, ). . Governance Changes (e.g., Bastedo, ; Bok, ; Cameron, ; Christensen & Eyering, ; Sporn, ). . Facilities (e.g., Alexandar, ; Christensen & Eyering, ). . External Partnerships (e.g., Alexander, ; Christensen & Eyering, ; Kelman & Tschmuck, ). These domains additionally represent areas of change within higher education that can be both universally understood by administrators and other constituents and examined more nar- rowly in the context of music programs. The theoretical framework establishes the power of organizational adaptation theories to illuminate the nature of change in higher education (Cam- eron, ; Hilbun, ; Manning, ; Sporn, ). Additional scholarship clearly ARTIVATE 11.1 7 demonstrates that: (a) the music industry has been significantly disrupted by the digital revolu- tion (Krueger, ; Tschmuck, ), (b) there is great need for change within higher music education in response (e.g., Bennett, ; CMS, ; Myers, ), and (c) there is sporadic documentation of organizational adaptation in higher music education; but no previous field- level studies exist. Methods I designed a survey instrument using Qualtrics to quantify, measure, and assess the organiza- tional adaptation strategies of higher music education programs. This instrument is called the Higher Music Education Organizational Adaptation Survey. The original survey encompassed goals outside the scope of this paper and included a total of fifty-seven items, each correspond- ing to one of three question types: organizational adaptation strategy, environmental percep- tion, and institutional and leader characteristics. Only the results of the organizational adapta- tion strategy of the survey and demographic results are presented in this paper. The population surveyed was composed of music leaders—occupying positions such as de- partment chair or dean—from institutions participating in the Higher Education Arts Data Ser- vices (HEADS) annual survey of music programs. Most of these institutions are accredited by the National Association of Schools of Music (NASM). Specifically, the study focused on those institutions that grant either baccalaureate, masters, or doctoral degrees in music. Music leaders provide annual data to NASM and HEADS about their programs, therefore I constructed a da- tabase of  music leader contacts by visiting the public websites of each institution listed in the – HEADS report. An initial invitation to participate was sent to each contact con- taining a link to the survey followed by three reminder emails exactly one week apart. From the original  emails sent,  surveys were successfully delivered. Following four weeks of data collection,  valid responses were obtained for a response rate of .. To operationalize and measure organizational adaptation strategy, the survey included forty dichotomous items across ten domains covering the five previously discussed organiza- tional adaptation strategies. Four items were included in each domain. Similar to methods used in quantitative organizational research by Khandwalla () and Lundvall and Kristensen (), the survey asked participants to respond “yes” or “no” to a statement about their music program in the past five years. The period of five years was selected to account for the pace of change within higher education as well as the most recent music industry innovations, such as digital streaming and social media (Bok, ; Smith & Telang, ). Each item described an action corresponding to an organizational adaptation strategy of decentralization, generaliza- tion, specialization, or formalization in each of the ten domains: curriculum, co-curriculum, full-time faculty, part-time faculty, admissions policies, leadership, online curriculum, govern- ance changes, facilities, and external partnerships. I wrote the items in the survey section based on the literature review as Crocker and Algina () allow that researchers may create items based upon prior research where concepts or behaviors “that have been most frequently studied by others are used to define the construct of interest” (p. ). ARTIVATE 11.1 8 I created a scoring scheme for the scale that expressed the complexity of organizational adaptation strategy (Sporn, ) by allowing respondents to select multiple responses to items in each domain across the typology spectrum. “Yes” answers corresponding with decentraliza- tion were coded . “Yes” answers corresponding to generalization strategies were coded . Spe- cialization strategies with “yes” answers were coded -, and strategies of formalization with “yes” answers were coded -. All “no” answers were coded , considered as corresponding to the strat- egy of inaction. For reference in the analysis, items were coded for each strategy and each do- main (i.e., G corresponds to the generalization strategy in the domain of full-time faculty). The seven demographic items in this survey were: free-standing/embedded status, public/private status, music unit region (using the IPEDS regions), music unit size, types of degrees offered, music leader background, and music leader current position. All data analysis was performed using Statistical Package for the Social Sciences (SPSS) software and descriptive statistical analysis was conducted on all the Organizational Adaptation Strategy (OAS) items (Rea & Parker, ). Validity in this study was heavily dependent upon the face validity of each item on the survey instrument (Croker & Algina, ). The items were written to express actions and vocabulary commonly known to administrators and faculty in higher education (Bok, ; Ruben, et al., ). Since many items contained the term music unit, this term was defined in the survey’s introduction as a blanket term referring to a music department, music school, music college, or independent music conservatory or institution. Re- liability was assessed using Croker and Algina’s () recommendation of coefficient alpha as an appropriate lower bound for reliability in survey research. This coefficient was evaluated for the -item OAS scale (a = .). Results Sample Prior to analysis, a frequency analysis of the demographic results was performed. This sample included more public (.) than private music units (.), and it was overwhelmingly composed of embedded music units. The size ranges of music units were relatively even, with slightly fewer music units included that contained over  students. Demographic results for music unit status, free-standing or embedded status, and size range are presented in table . Table 2: Music Units’ Status, Organizational Type, and Size Music Unit Status* Frequency Percentile Public  . Private  . Organizational Type** Frequency Percentile Free-Standing  . ARTIVATE 11.1 9 Embedded  . Size Range by Number of Music Majors** Frequency Percentile -  . -  . -  . -  . +  . *N = ; **N =  The results for the region of music units in this sample were skewed toward those in the southeast (.), with uneven distribution across the other region categories. Music units in this sample conferred a wide variety of degree combinations. For ease of analysis, these catego- ries were combined to reflect those music units where the highest degree conferred was either bachelors (.), masters (), or doctoral (.). Results for the region and degrees con- ferred items are presented in table . Table 3: Music Units’ Region and Highest Degree Conferred Region* Frequency Percentile New England  . Mid-East  . Great Lakes  . Plains   Southeast  . Southwest  . Rocky Mountains  . Far West  . Degrees Conferred** Frequency Percentile Bachelors  . Masters   Doctoral  . *N = ; **N= Music leaders in this sample primarily had backgrounds in classical performance or com- position (.), and those with music education backgrounds represented the second largest category (.). All other background types were small proportions of this sample. The most prominent position held by music leaders was department chair (), with other positions sparsely represented. All results for music leader background and current position are displayed in table . ARTIVATE 11.1 10 Table 4: Music Leaders’ Primary Background and Current Positions Music Leader Primary Background Frequency Percentile Classical Performance or Composition  . Jazz or Pop Performance or Composition  . Music Education  . Music Business or Law  . Musicology or Music Theory  . Music Technology or Music Production  . Outside of Music  . Music Leader Current Position Frequency Percentile Department Chair/Head  . Program Director  . Dean  . Chief Academic Officer   President  . Other  . N =  Individual Domain Organizational Adaptation Strategies To assess music units’ responses in each individual domain, frequency tables displayed the num- ber of institutions responding “yes” and “no” to each OAS item, providing a portrait of the kinds of adaptations in which programs are engaging to respond to music industry change. In the domain of curriculum items, formalization was the dominant strategy with . of respond- ents answering “yes.” However, generalization also represented a high percentage of “yes” an- swers (.). Although music units adopted curriculum strategies in large percentages, spe- cialization through the discontinuation of elective courses outside of current programs represented the lowest percentage of “yes” responses (). Domain results for curriculum are displayed in table A. Table 5A: Domain: Curriculum Item Strategy N Yes () No () D. We have created one or more new pro- grams Decentralization  . . G. We have created new courses in subjects where we previously had not offered instruc- tion Generalization  . . S. We have discontinued elective courses that did not fall within current programs Specialization    F. We have created new courses to better ful- fill aspects of our unit’s mission Formalization  . . ARTIVATE 11.1 11 In the domain of full-time faculty, formalization was the dominant strategy with . of “yes” responses compared to decentralization (.), generalization (.), and specializa- tion (.). Domain results for full-time faculty are displayed in table B. Table 5B: Domain: Full-Time Faculty Item Strategy N Yes () No () D. We have hired new full-time faculty as pro- gram directors, coordinators, or department heads Decentralization  . . G. We have hired full-time faculty in subject areas where we previously had no specialists Generalization  . . S. We have eliminated full-time faculty posi- tions with specialties outside of our traditional offerings Specialization  . . F. We have hired new full-time faculty as re- quired by our mission or strategic plan Formalization  . . The part-time faculty domain results indicated a strong preference among respondents for a strategy of generalization with . answering “yes” to hiring part-time faculty in previously unrepresented subject areas. Approximately / of respondents indicated “yes” for items corre- sponding to decentralization () and specialization (.). Although full-time faculty hir- ing was found to be most consistently aligned with formalization, part-time faculty hiring trended toward generalization. Domain results for part-time faculty are displayed in table C. Table 5C: Domain: Part-Time Faculty Item Strategy N Yes () No () D. We have hired new part-time faculty as program directors, coordinators, or depart- ment heads Decentralization    G. We have hired part-time faculty in subject areas where we previously had no specialists Generalization  . . S. We have eliminated part-time faculty posi- tions with specialties outside of our traditional offerings Specialization  . . F. We have promoted one or more part-time faculty to full-time positions Formalization  . . ARTIVATE 11.1 12 Generalization was also dominant in the domain of admissions policies. A large portion of music units indicated the adoption of more musically inclusive audition policies (.). Dif- ferentiating policies by program, a decentralization strategy, was utilized by nearly ½ of music units (), and slightly less than ¼ of responding music units engaged in a reimagining of admissions policies for all programs (.), a formalization strategy. Domain results for ad- missions policies are displayed in table D. Table 5D: Domain: Admissions Policies Item Strategy N Yes () No () D. We have differentiated admissions policies based on students’ intended program Decentralization    G. We have altered our audition policies to in- clude greater varieties of musical style Generalization  . . S. We have narrowed our audition require- ments to become more selective for one or more current programs Specialization  . . F. We have created new admissions policies for all programs Formalization  . . Unique in this study, the leadership domain found music units favoring a strategy of spe- cialization () over the other strategies. However, a large percentage of respondents also in- dicated the adoption of strategies of generalization () and formalization (.). The de- centralization item received “yes” responses from . of respondents. Domain results for leadership are displayed in table E. Table 5E: Domain: Leadership Item Strategy N Yes () No () D. Our leadership has focused on creating new divisions, areas, or departments Decentralization  . . G. Our leadership has focused on expanding the curriculum Generalization    S. Our leadership has focused on reinforcing our existing strengths Specialization    F. Our leadership has focused on creating a new strategic plan Formalization  . . A clear preference for a generalization strategy was displayed in the domain of online cur- riculum with . of music units indicating they had created new online elective courses in ARTIVATE 11.1 13 subject areas where previously no instruction had been offered. The creation of entirely new online programs was indicated by . of respondents, and  of music units indicated they had transferred existing programs online. Results for online curriculum domain are displayed in table F. Table 5F: Domain: Online Curriculum Item Strategy N Yes () No () D. We have created new online programs Decentralization  . . G. We have created new online courses in subjects where we previously had not offered instruction Generalization  . . S. We have discontinued one or more online programs Specialization  . . F. We have transferred existing programs online Formalization    Music units favored generalization in the governance domain with . of respondents answering “yes” to the expansion of existing committee or administrative units’ functions. De- centralization also received a high percentage of “yes” responses (.), and approximately / of music units (.) indicated they had increased oversight of committees or administra- tive units, a formalization strategy. Music units indicated a reluctance to specialize in the gov- ernance domain as only  of respondents answered “yes” to narrowing the duties of commit- tees and administrative units. All results for the governance domain are shown in table G. Table 5G: Domain: Governance Item Strategy N Yes () No () D. We have created new committees or ad- ministrative units Decentralization  . . G. We have expanded the functions of existing committees or administrative units Generalization  . . S. We have narrowed the duties of committees or administrative offices Specialization    F. We have increased oversight of committees or administrative units Formalization  . . Music units were almost evenly split between generalization and decentralization in the co- curriculum domain, slightly favoring new programs (. “yes” responses) over expanding existing programs ( “yes” responses). Few music units indicated they had discontinued co- curricular programs (.), and approximately ¼ expressed a formalization strategy of in- creased administrative involvement (.). The results also more broadly suggest that about ARTIVATE 11.1 14 / of music units are adding or expanding co-curricular programing. Domain results for co- curriculum are displayed in table H. Table 5H: Domain: Co-Curriculum Item Strategy N Yes () No () D. We have created new co-curricular pro- grams, organizations, or activities Decentralization  . . G. We have broadened the activities of exist- ing co-curricular programs Generalization    S. We have discontinued co-curricular pro- grams Specialization  . . F. We have increased administrative involve- ment in co-curricular programs Formalization  . . Results in the domain of facilities were heavily skewed toward the generalization strategy (modification of existing facilities), which was selected by . of respondents. Decentraliza- tion in the form of creating new facilities was engaged in by only . of responding music units. The discontinuation of older facilities (specialization), and the acquisition of facilities ex- ternally (formalization) were both utilized by less than  of music units, . and ., re- spectively. Results from the facilities domain are displayed in table I. Table 5I: Domain: Facilities Item Strategy N Yes () No () D. We have built or created new facilities Decentralization  . . G. We have modified existing facilities in or- der to accommodate a broader range of activi- ties Generalization  . . S. We have discontinued the use of older facil- ities Specialization  . . F. We have acquired facilities from external organizations Formalization  . . Generalization was slightly favored in the external partnership domain, receiving . “yes” responses. Developing new partnerships, a strategy of decentralization, was also utilized by over half of music units (.). Music units demonstrated reluctance to discontinue part- nerships, a specialization strategy, with only . of respondents indicating “yes” on this item. Interestingly, . of units indicated increasing administrative involvement in external ARTIVATE 11.1 15 partnerships, a strategy of formalization. Results from the domain of external partnerships are displayed in table J. Table 5J. Domain: External Partnerships Item Strategy N Yes () No () D. We have developed new partnerships with external organizations Decentralization  . . G. We have expanded the scope of our exist- ing collaborations with external organizations Generalization  . . S. We have discontinued external partner- ships or collaborations Specialization  . . F. We have increased administrative involve- ment in external partnerships Formalization  . . The major feature of these results is the collective trend toward organizational adaptation strategies of generalization across the field of higher music education. This trend is most prom- inent in the domains of part-time faculty and facilities, but it is also favored in admissions pol- icies, online curriculum, governance, and external partnerships. Leadership was the only do- main in which specialization was the dominant strategy, and co-curriculum was the only domain in which decentralization was the dominant strategy. Formalization was indicated by the greatest number of respondents in both curriculum and full-time faculty domains. Im- portantly, even in domains where generalization was not the dominant strategy, it was indicated by a large percentage of music units, leading to the conclusion that generalization is omnipres- ent across the sample. Small differences separated generalization from the dominant strategy in curriculum and co-curriculum (<), and the most pronounced difference was in the leader- ship domain (). The full-time faculty domain showed greater formalization than generali- zation by .. Table  highlights the percentage of music units in each domain employing the dominant organizational adaptation strategy and the percentage employing generalization when generalization is not dominant. Table 6. Dominant Organizational Adaptation Strategies Domain Dominant Organiza- tional Adaptation Strategy Music Units Utilizing Dominant Strategy () Music Units Employ- ing Generalization () Curriculum Formalization . . Full-Time Faculty Formalization . . Part-Time Faculty Generalization . ARTIVATE 11.1 16 Admissions Policies Generalization . Leadership Specialization   Online Curriculum Generalization . Governance Generalization . Co-Curriculum Decentralization .  Facilities Generalization . External Partnerships Generalization . Furthermore, in nine of the ten domains, the generalization strategy was adopted by more than / of music units (µ = ). Full-time faculty was the only domain in which less than / of music units are employing a strategy of generalization. Table  demonstrates the prevalence of generalization across higher music education by domain. Table 7. Generalization Strategies by Domain in Higher Music Education Domain Generalization Item Percentile Curriculum We have created new courses in subjects where we previ- ously had not offered instruction . Full-Time Faculty We have hired full-time faculty in subject areas where we previously had no specialists . Part-Time Faculty We have hired part-time faculty in subject areas where we previously had no specialists . Admissions Policies We have altered our audition policies to include greater vari- eties of musical style . Leadership Our leadership has focused on expanding the curriculum  Online Curriculum We have created new online courses in subjects where we previously had not offered instruction . Governance We have expanded the functions of existing committees or administrative units . Co-Curriculum We have broadened the activities of existing co-curricular programs  Facilities We have modified existing facilities in order to accommo- date a broader range of activities . External Partnerships We have expanded the scope of our existing collaborations with external organizations . Field-Level Organizational Adaptation Strategies Multiple composite scores were created to address the organizational adaptation of higher mu- sic education at the field-level. Using the coding system described previously, the total Organi- zational Adaptation Strategy (OAS) score of each institution was summed to create a composite OAS score that was reflective of that institution’s overall organizational adaptation tendency. The OAS composite scale has a range from - to . Fixed points on the composite score scale correspond to theoretical nodes of organizational behavior. A score of  would be interpreted as extreme decentralization with extreme generalization (a music unit adopted every single ARTIVATE 11.1 17 decentralization and generalization strategy). Scores near  would signify a heavy reliance on decentralization while scores near  would correspond to a predominance of generalization. A music unit that responded “no” to all questions would score a perfect zero and would be asso- ciated with a strategy of extreme inaction. Mirroring the positive end of the scale, scores clus- tering near - would indicate heavy adoption of specialization strategies while scores near - would indicate a reliance on formalization. Finally, - would indicate a music program re- sponding “yes” to every specialization and formalization item on the survey. The absolute value of each of the institution’s responses was summed to create the absolute value of the organizational adaptation strategy score, |OAS|. This score, with a range of –, reflects an institution’s efforts at overall change, weighting more extreme strategies (i.e., greater amounts of organizational change) of decentralization and formalization (Cameron, ) more heavily than generalization and specialization. OAS scores that reveal overall tendencies, and |OAS| scores which reveal overall change, provided useful discipline-spanning data when averaged across institutions. Four individual OAS composite scores were also created for de- centralization, generalization, specialization, and formalization. These scores awarded a “” for “yes” answers to any of the items corresponding to that strategy and a “” for items correspond- ing to the other three strategies. Each individual OAS composite score had a range of -. An additional composite score for inaction was created by awarding a “” for each “no” response and a “” for each “yes” response. The inaction composite score had a range from - and represented the level of inaction employed by music units. The mean, minimum, maximum, mode, and standard distributions of each composite score were examined to assess the distri- bution of institutions’ responses across the organizational adaptation strategy typology and in- dividual strategies (Crocker & Algina, ). First, the  items on the OAS scale were evaluated for reliability (a = .). As Croker and Algina () recommend values >. for reliable scales, the OAS scale is in an acceptable range. OAS scores were then evaluated for the entire sample (n = ) on a histogram to confirm a normal distribution. Following this assessment, the composite scores for each organizational adaptation strategy—decentralization, generalization, specialization, formalization, and inac- tion—were also assessed for normality via histograms. Through prior coding in Qualtrics, all composite scales and scoring were already present in the exported data set. To gain a macro-understanding of the organizational adaptation strategy trends in higher music education, descriptive statistics for OAS, the organizational adaptation strategy compo- site, |OAS|, the total adaptive action composite, and individual organizational adaptation strat- egy composites are presented in table . Table 8: Descriptive Statistics for Organizational Adaptation Strategy Variables Scale N Min Max Mode µ s Total OAS  -   . . Absolute OAS     . . ARTIVATE 11.1 18 Decentralization Composite     . . Generalization Composite     . . Specialization Composite     . . Formalization Composite     . . Inaction Composite    a .  a Multiple modes exist;  and . Because organizational adaptation strategies are not exclusive constructs, these composite scores provide insight on the mixture of organizational adaptation strategies employed by music units. The OAS score mean (.) supports the individual domain results indicating an overall field-wide trend toward strategies of generalization. The relatively high mean of generalization (.) compared to decentralization (.) and formalization (.) additionally buttresses this large-scale trend. Specialization has the lowest mean (.), an indication that music leaders answered “yes” to fewer specialization items than any of the other strategies. The inaction com- posite was found to have a mean of . and a range nearly spanning all possible scores (-). The |OAS| score mean (.) was near the center of the range (-) but slightly skewed toward less total adaptive action and greater numbers of lower scoring items, generalization and spe- cialization. Following the descriptive assessment, OAS scores were evaluated using frequency plots to display how music units were arrayed across the organizational adaptation typology as indicated by their OAS scores. In addition to the nodes for fixed and extreme OAS scores, table  presents OAS scores across the entire range of organizational strategies along with the percentage of mu- sic units trending toward each strategy. Table 9. Distribution of OAS Scores on the Organizational Adaptation Typology Spectrum Organizational Adaptation Strategy OAS Composite Score Range Scores () Extreme Decentralization with Extreme Generalization   Exclusive Decentralization   Trending Toward Decentralization  -  . Exclusive Generalization  . Strategies Trending Toward Generalization  -  . Extreme Inaction  . Strategies Trending Toward Specialization - -  . ARTIVATE 11.1 19 Exclusive Specialization -  Strategies Trending Toward Formalization - - -  Exclusive Formalization -  Extreme Formalization with Extreme Specialization -  N =  The high percentage of OAS scores trending toward generalization (.) further indicate that on the field-wide level, music units are choosing adaptive actions that compose an organi- zational adaptation strategy consistent with generalization. Across the results, other strategies are utilized by music units while, as a field, higher music education’s response to the digital revolution in the music industry is firmly in the generalization range. Institutional and Music Leader Characteristics Analysis across institutional and music leader characteristics was performed for each of the composite scores. Scores for music unit region, public/private status, free-standing/embedded status, and music leader current position did not indicate significant differences. However, fol- lowing a recoding of the size groups—disaggregating the data by music units’ size and highest degrees conferred—yielded an important conclusion in this study. More complex music units had higher mean OAS scores and mean |OAS| scores than less complex music units. This im- plies that more complex units exhibited greater levels of adaptive action and greater levels of generalization when compared to less complex music units. Comparative composite scores for music unit size and degrees conferred are indicated in table . Table 10. Composite Scores by Music Unit Size and Highest Degree Conferred Size N µ OAS µ |OAS| µ D Sum µ G Sum µ S Sum µ F Sum µ I Sum <   .  . . . . . >   . . . . . . . Degree N µ OAS µ |OAS| µ D Sum µ G Sum µ S Sum µ F Sum µ I Sum BA  .  . . . . . MA  . . . . . . . PhD  . . . .  . . ARTIVATE 11.1 20 Discussion The Great Generalization Higher music education has responded to the digital revolution in the music industry by engag- ing in a great generalization over the past five years. Results from both individual responses and composite scales align to paint a portrait of a discipline clearly choosing generalization as a pre- ferred organizational adaptation strategy. Because higher music education exists within the broader music industry, any employment of adaptation strategies can be considered a response to this radical environmental change (Aldrich, ; Daft & Weick, ; Khandwalla, ; Smith & Telang, ; Tschmuck, ). Across many common domains within higher educa- tion, music units have expanded their scope, functions, and activities. This great generalization is consistent with contemporary entrepreneurial trends within the music industry and is con- gruent with several major tenants of organizational adaptation theory. Gumport and Snydman’s () research confirmed that the expansion of both “bureau- cratic and programmatic structures” (p. ) of academic organizations allow more areas of knowledge to be legitimized by the academy, “a major intellectual role for society” (p. ). The music industry has become eminently broad, encompassing new fields, technologies, corporate sectors, and entire professions that did not exist only a decade ago (Benett, ; Kel- man & Cashman, ; Krueger, ; Tschmuck, ). A corresponding generalization of higher music education is entirely appropriate to this radical environmental change. As musicians have learned new skills sets in art and business (Morris, ; Spilker, ), higher music education has broadened its curricula to expand what is taught. The need for “op- tion-rich curricula that involve student choice in tandem with carefully planned curricular op- tions” (Myers, , p. ) is directly addressed through these efforts by music units to add programs, courses, online courses, and full-time and part-time faculty. The great generalization spans beyond teaching and learning to encompass new co-curricular activities for students, an approach exemplified by projects such as Kellman’s () Youth Music Industries. More broadly still, the internal architecture of higher music education organizations has broadened to include more functions and roles played by faculty and staff. Importantly, music units show- case generalization beyond their institutions, with greater external engagement indicated by over / of respondents. As Hoeven and Hitters () write: “it is vital that live music organi- zations collaborate with local music schools and other educational institutions to provide in- ternships and performance opportunities” (p. ). This study’s results support the key role that higher music education may play in the construction and flourishing of music ecosystems. Musicians and scholars impress the importance of modern music students preparing for portfolio careers: the musical and non-musical skills needed to operate in many different areas of music simultaneously, creating a patchwork of income sources and activities (Bartlett & Tolmie, ; CMS, ; Kardos, ; Latukefu & Ginsborg, ; Myers, , NASM, ; Tschmuck, ). The great generalization in higher music education suggests that institutions are creating portfolio music programs wherein myriad musical and commercial instruction and ARTIVATE 11.1 21 activities occur. To understand the value of the great generalization, its impacts can be assessed from the perspectives of both individual music units and the discipline as a whole. While contemporary music students certainly reap the benefits of the great generalization through greater opportunities, music faculty, staff, and administrators must work to balance demand, costs, and overall mission to ensure success. An organizational adaptation strategy of generalization may carry significant risks. For an individual music unit, adopting a generaliza- tion strategy may cause the addition of too many options without requisite enrollment or reve- nue growth. New courses, degree programs, faculty, online infrastructure, or external partner- ships that are not met with enthusiasm from students or increased applications may be viewed as unsuccessful experiments and a drain on the entire music unit (Christensen & Eyering, ; Miller, ). As with all expansion in higher education, generalization risks incurring expenses more quickly than revenue (Bueller, ). On top of the additional resource requirements that a strategy of generalization incurs, significant questions remain about the quality of teaching and learning during periods of organizational expansion. If individual music units are able to support the many simultaneous additions to their activities, then learning outcomes may im- prove as a result of such programmatic broadening. However, expansion without quality could ultimately lead to less successful music units and unclear learning goals across too many pro- grams and activities. A deeper examination of the organizational adaptation strategy results elucidates potential ways that music units are working to balance the costs of generalization. Within domains com- monly regarded as the “core” of academic organizations, such as full-time faculty, curriculum, and co-curriculum (Hendrickson et al., ), music units exhibited more formalization and decentralization. Domains representing more “fringe” activities, such as external partnerships, online curriculum, and part-time faculty (Bok, ; Miller, ), exhibited comparatively more generalization. Generalization strategies across domains are more cost effective than de- centralization or formalization. Single online courses are cheaper to produce than entire online programs; adding an extra program with a trusted partner may be cheaper than creating a new relationship from scratch, and part-time faculty generally cost less than full-time faculty (Alex- ander, ). In the entrepreneurial work of remaking an organization (Callander & Cum- mings, ), music units may be employing generalization strategies to compete in new areas while controlling costs and accommodating existing resources. This behavior reflects a central tension within academic organizations; the need to experiment and innovate is often at odds with organizational and resource constraints (Manning, ; Sporn, ). Similar to Gumport and Snydman’s () research, higher education’s historically slow accrual of cur- ricula, activities, and functions is mitigated by human, budgetary, and traditional bulwarks (Bok, ; Bueller, ; Thelin, ). At the field-level, the great generalization may ultimately increase competition between music units. Bok () explains that “competition among institutions creates a constant pres- sure to respond to student needs, while also generating much effort to improve and excel” (p. ). Since all music units are facing the same digital revolution and working to respond to the same needs (Bennett, ; Sorensen, ), the risk of mission creep and isomorphic ARTIVATE 11.1 22 tendencies is exceptionally high (Bastedo, ; Bok, ; DiMaggio & Powell, ). As music units have historically defined themselves around narrow missions and goals (i.e., training or- chestral musicians or jazz composers) (Gandre, ; Stanley, ), expanding programmatic missions and activities to accommodate for the vast, new music industry may threaten tradi- tional core identities. While music units may be ultimately influenced by each other in this re- spect, organizational theory suggests that the influence of their environment (the music indus- try) is the driving factor for change (Aldrich, ; Khandwalla, ). The digital revolution in the music industry exhibits no signs of slowing down; from the evolution of the musical metaverse to new digital streaming platforms and social media apps (i.e., TikTok), the music industry environment for higher music education is fantastically dynamic (Smith & Telang, ). As long as the music industry continues to evolve new careers and new disciplines—as well as new genres of music for performance and scholarship—the great generalization in higher music education may only accelerate. The Great Generalization as Organizational Entrepreneurship The great generalization in higher music education represents a significant moment of organi- zational entrepreneurship for the discipline that not only highlights the unique nature of music in academe but portends the manner in which other areas of post-secondary education may adapt to the twenty-first century economy. When examined in combination with the decentral- ization found in this study, higher music education has tremendously diversified. Khandwalla () connects diversification to entrepreneurship in his findings: “Diversification became an important top management goal also when the top management was entrepreneurial and risk taking” (p. ). This is especially true in turbulent, dynamic environments such as the music industry (Tschmuck, ). Higher music education’s internally complex environments (Mil- ler, ; Manning, ) may also provide the catalyst for such broader diversification and generalization as Greenman’s () work illustrates: the need for “entrepreneurial action to resolve complexity in the inter-institutional system [and] to translate ideas into collective goals and organizational practices” (p. ). In Quinn and Cameron’s () “entrepreneurial stage”, “the success of an organization will tend to be associated with its flexibility, growth, resource acquisition and development of external support” (p. ). Indeed, this is exactly what this study demonstrates; music units have been investing in experimentation and expansion to grow and adapt. The most intriguing conclusion of this observed organizational entrepreneurship shaped by Quinn and Cameron’s () theory is the fact that music units’ response to the digital rev- olution may represent merely an early-stage response to an industrial shift only twenty years old. Although most music units date back decades or even centuries (Miller, ), the digital revolution may have produced a “reset” in their organizational behavior. In contrast to Cam- eron and Quinn’s () descriptions of mature organizations as occupying a formalization or decentralization-centric stage of organizational development, the “entrepreneurial” stage (the ARTIVATE 11.1 23 first stage of the organizational life cycle) more accurately describes music units in the post- digital era. They are experimenting with niche formation, innovation, and perhaps even iden- tity. Returning to previous stages is in fact predicted by life cycles theory whereby “organizations may recycle through the sequence [of organization stages] again as a result of unusual environ- mental events” (Cameron, , p. ). Consistent with this concept, the digital revolution has caused music units to organizationally regress toward their earlier, entrepreneurial state. In re- visiting the entrepreneurial state, music units are actively pursuing new organizational identities and markets in the hopes that such efforts will successfully sustain the organization in a chal- lenging environment. Callander’s () work in arts entrepreneurship demonstrates the value of multiple theoretical approaches to the subject. Specifically, she highlights how “emancipatory theory emphasizes change creation over wealth creation. In the emancipation model, the entre- preneur seeks to break up the status quo and break free of perceived constraints” (p. ). Indeed, music units working to emancipate their organizations from previous constraints and traditions (i.e., curricula, audition requirements, faculty expertise) represents entrepreneurial organiza- tional behavior. Resource dependence theory describes entrepreneurial behavior as firms work- ing to increase “product lines” (Pfeffer & Salancik, ). Broadening audition policies, adding new online courses, co-curricular activities, and elective courses all suggest efforts to attract stu- dents that may not have formerly been “customers” of each individual music unit. The goal ambiguity ubiquitous in higher education complicates any discussion of a purely output-based approach to analysis (Manning, ), however, this study demonstrates that the great general- ization has significantly increased higher music education’s inputs and outputs. Reluctant Entrepreneurs Although musicians are known for their entrepreneurial skills (Morris, ), scholarship and popular sentiment reflect the complex relationship that music makers hold with their own iden- tities as entrepreneurs. Haynes and Marshall () found that “[t]he views expressed by the musicians that they are entrepreneurial by necessity rather than choice echo the arguments found in existing critical accounts of creative labor” (p.). Music as a field has been hit hard by the market forces and neoliberal philosophies of employability (Butt, ). In contrast to tra- ditional attitudes about music as a core attribute of the enlightened and educated human (Gandre, ), the contemporary music student is aware of their own need to commercialize their skills (Bennett, ). Music units in this study may be Hayes and Marshall’s () “re- luctant entrepreneurs,” working to adapt even when adaptation necessitates unwanted change. From the population ecology perspective, music units adopting generalization strategies allows them to move outside of their narrow range of activities: “Some organizations that are unable to acquire enough resources by specializing in a limited range of products or services manage to survive by becoming generalists” (Aldrich, , p. ). If becoming a generalist allows an organization to survive at the expense of its core mission or identity, how might that organiza- tion evaluate the efficacy of such adaptation? Higher music education’s tendency to exhibit more generalization and entrepreneurial behavior in domains at the periphery of their ARTIVATE 11.1 24 organization’s activities (i.e., hiring part-time faculty) offers a clue. Similar to musicians who may develop new secondary or tertiary skills, such as digital marketing, while maintaining their core identity as musicians (Haynes & Marshall, ; Morris, ), music units may view en- trepreneurship as an attempt to branch out without substantial alteration in the main body of their work. The theories of cybernetics additionally expound that organizations can haphaz- ardly accrue activities simply through chasing possible opportunities (Ashby, ; Birnbaum, ). Music units in this study may be accidentally generalizing when faculty, staff, and stu- dents are all pushing the organization in new directions without strong centralized leadership (Birnbaum, ; Sporn, ). Complexity and Entrepreneurial Behavior In this study, two proxies for organizational complexity—music unit size and highest degree conferred—acted as de facto predictors of the total amount of organizational adaptation strate- gies adopted. Music units with higher complexity exhibited significantly more generalization overall. Contrary to Quinn and Cameron’s () theory in which the more nimble and younger organizations will exhibit more generalization and entrepreneurial behavior, this study illus- trates that larger, more complex music units that are often regarded as less nimble in higher education (Bastedo, ; Bok, ) were the more adaptable music units. This conclusion also challenges common practice thinking about larger organizations as difficult to change (Khand- wall, ). If large music units with multiple degree levels can adopt a variety of adaptation strategies in the face of the digital revolution, it presents a positive future for complex music units in the digital age. However, the opposite may be true for less complex music units. Those that are lacking in resources may be unable to adopt a successful concoction of adaptation strat- egies or sufficient entrepreneurship to survive and grow. This would be consistent with the his- torical research by Gandre () wherein music units were susceptible to failure when they were unable to adapt to their changing environment. In this respect, music units may resemble liberal arts colleges and other small institutions in their vulnerability to environmental change (Hilbun, ; Thelin, ). Because music units are so varied in their scope and mission (NASM, ), less complex music units may well have niche markets and less need to adapt to their changing environment. In this respect, demographic results must be interpreted carefully to be equitable to all types of music units. This study does imply that music units choosing to utilize fewer adaptive actions should have concrete reasons for such a choice so as not to reflect a cybernetic approach (Birnbaum, ; Child, ). Musical Foreshadowing A crucial consideration that results from this study is the importance of entrepreneurship as organizational behavior in all of higher education. Though much has been written on organiza- tional entrepreneurship in higher education (e.g., Buller, ; Christensen & Eyering, ; Seikkula-Leino & Salomaa, ), this study demonstrates that single discipline units facing ARTIVATE 11.1 25 extreme environmental change employ predominantly generalization behavior consistent with theories on organizational adaptation and entrepreneurship. In the early stages of the digital revolution, the changes in the music industry were recognized as a harbinger of trends to come in other economic sectors (Fisher, ). Similar digital revolutions have now transpired in the newspaper industry, the film industry, the book publishing industry, and increasingly, higher education itself (Alexander, ; Levine & Van Pelt, ; Smith & Telang, ). From its very inception, higher education as an organizational field has tended to general- ize and expand (Bastedo, ; Thelin, ), but now it may need to increase the pace of this change and engage in more dramatic and dynamic entrepreneurial behavior as digital technol- ogy advances. Just as in the music industry, technology firms and disruptive innovations are permeating the higher education ecosystem and adding to the turbulence of the environment (Alexander, ; Levine & Van Pelt, ). The risks that academic organizations take by not adapting to this digital revolution in higher education will far outweigh the risks of engaging in entrepreneurial experimentation. Higher education leaders in every field should examine higher music education’s efforts at evolution for insights on the entire enterprise; for while some music units will be successful, longevity is by no means assured. The history of post-secondary musical organizations is littered with closures, mergers, and missteps (Gandre, ; Miller, ). However, successful adaptations in the music discipline may point the way for all of higher ed- ucation as this study demonstrates that music is becoming more broad, more accessible, more outward facing, and more diverse. These are promising trends that should be emulated across the academy. Despite the potential for music units to adopt generalizing behavior in a reticent manner, it remains that they are remaking their organizations for the new era. From the per- spective of musicians themselves, music units are in fact imitating the enterprising, entrepre- neurial behavior of music creators (Morris, ; Young, ). In so doing, they will certainly continue to thrive in the post-digital age. Limitations The main limitation in this study was the small sample size (n = ). While this is common in the social sciences (Rea & Parker, ), higher music education is also limited by small popu- lation size. Limiting the scope of respondents to only higher music education in the United States also limited the potential for a larger sample size and comparative analysis between music units domestic and abroad. A further and natural limitation was the reduction of all possible organizational behaviors down to forty dichotomous items. Absent in this study was a “second level” of detail on music units’ behavior. While the results showed that they are adding new courses, which courses was not measured. An additional limitation was the homogeneity of the survey respondents themselves. Each respondent was an academic leader in higher music edu- cation, thereby eliminating the perspectives of other organizational members such as faculty, staff, and students. Because higher music education leaders are internal stakeholders in higher music education, a natural limitation of this study was the lack of perspectives from those out- side higher music education, especially those in the music industry environment. A final ARTIVATE 11.1 26 limitation of this study was the lack of outcome variables. Although this study measured organ- izational adaptation strategy, perhaps imperfectly, no outcome variables were assessed. Follow- up research could easily utilize a similar methodology for organizational adaptation strategy and include outcome variables common in higher music education such as enrollment, philan- thropic success, or learning outcomes and graduation data. Such work could broaden the scope of this research to include other areas of higher education. Suggestions for Future Research and Recommendations for Practice The most logical follow-up to this study would be qualitative research on how higher music education is adapting to the digital revolution. The complex nature of entrepreneurship in aca- deme validates such an approach (Seikkula-Leino & Salomaa, ). Organizational adaptation studies should be conducted on other disciplines within higher education, and on higher edu- cation organizations in the digital age. Similar to the music industry, the most valuable ques- tions will concern the interplay between higher education and technology firms entering or looking to enter the post-secondary landscape (Krueger, ; Levine & Van Pelt, ). All entrepreneurship comes with risk, and the great generalization in higher music educa- tion represents individual and collective risk for music units. Drawing from multiple organiza- tional theories (e.g., Aldrich, ; Quinn & Cameron, ), this study suggests music units should practice “strategic generalization”: ensure that efforts to generalize and decentralize do not inadvertently lead to expansion in all areas. Once a new environmental niche has been dis- covered, music units should focus resources on those successful adaptations and cease further exploratory expansion. The sooner music units arrive at new market areas through the great generalization, the more quickly they will be able to stay ahead of the higher education landscape and the more likely their success will be in the digital music era. Gandre’s () work demonstrated that higher music education is always on the edge. Adapting to the evolving digital revolution may continue to pose challenges for higher music education. However, musicians and music units show remarkable resilience when faced with the need to adapt. 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