Value-adding services provided to companies by academics in business schools Nabil Amara Laval University, Faculty of Business, Québec City, QC, Canada, G1V 0A6 E-mail:
[email protected]
Réjean Landry Laval University, Faculty of Business, Québec City, QC, Canada, G1V 0A6 E-mail:
[email protected]
Abstract: The findings of this paper show that 74% of the business scholars surveyed are involved in the supply of expert advice in the different value creation activities of companies. They tend to specialize in activities linked to their discipline. A minority offered customized solutions to companies; 60% provided expert advice to companies located within 100 km of their university; 40% frequently forged close ties with companies; and a minority developed explicit strategies to compete with consulting firms and other scholars on the market of expert advice offered to companies. In short, they do not apply what they teach.
Keywords: value chain; faculty members of business schools; business models; expert advice, companies.
Introduction Problem A company’s value chain is part of a larger system that includes the value chains of upstream suppliers and downstream clients. The services and expert advice supplied by faculty members of business schools are part of the supplier value chains. According to Porter (1985), such series of value chains constitute a value system. More specifically, such a value system includes the value chains of suppliers providing various types of inputs, including raw material, services and expert advice supplied by faculty members in business schools, to the company’s value chain. Such inputs are transformed by the company to create value added products which themselves pass through other value chains on their way to the ultimate buyers. The study of the linkages that connect the value creation activities inside a company, and the services and expert advice supplied to companies by faculty members in business schools, have not received much attention until now. This paper aims to advance knowledge by connecting services and expert advice supplied to companies by faculty members in business schools to the primary and support value generating activities of companies included in Porter’s generic value chain model.
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Current understanding The review of prior studies on knowledge transfer suggests major methodological and theoretical difficulties that need to be taken into account in order to advance knowledge. They are related to the study population, the measurement of dependent variables and explanatory factors. Difficulties associated with the unit of analysis and the composition of the study population: Most studies on university research transfer target the number of technologies commercialized using university technology transfer offices as their unit of analysis (Harmon et al., 1997; Agrawal, 2001; Hanel, 2006; Landry et al., 2007). In this paper, we adopt the researcher's perspective by making our unit of analysis the knowledge transferred by individual researchers. This unit of analysis is especially appropriate to investigate the knowledge transfer activities of faculty members in business schools because a majority of their knowledge transfer activities is not part of the mandate of university technology transfer offices. Furthermore, such a unit of analysis is especially well suited for the study of knowledge transfer activities involving the supply of services and expert advice to companies. Difficulties related to the measurement of the dependent variable knowledge transfer: Most studies on knowledge transfer are based on data on patents, licensing and spin-offs because this offers a perfect tool for an objective, quantitative analysis of knowledge transfer (Agrawal, 2001). This paper contributes to the advancement of knowledge in university research transfer by taking into account a more comprehensive picture of the mechanisms by which knowledge moves from researchers to companies. In this paper, we assume that faculty members in business schools deliver value to companies through the provision of a large variety of services and expert advice, such as helping firms to develop strategy and plans, assess markets, etc. In this paper, the services and expert advice provided by faculty members to companies are used as the dependent variable. Difficulties associated with the identification of factors explaining the knowledge transfer activities of faculty members in business schools: The explanation of knowledge transfer is still problematic. There is no general theory for the field (Landry et al., 2007; 2009). This paper builds from prior studies on knowledge and technology transfer to develop an integrative framework of the factors that affect the knowledge transfer activities and service offering of faculty members in business schools. Following Phan and Siegel (2006), we claim that the resource-based view, the institutional-based view and the social network theories provide excellent integrative foundations for deriving insights and hypotheses on knowledge transfer activities of faculty members in business schools. Research question This paper addresses the following questions: 1) To what extent do academics in business schools supply services and expert advice in the value generating activities composing the value chain of companies? 2) How are academics in business schools positioned on the different interdependent elements that constitute a business model? 3) How do faculty
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members of business schools from different disciplinary backgrounds come to develop differentiated business models of provision of expert advice to companies? 4) What implications do the results of this study indicate for the management of business schools, and the development of public policies supporting knowledge transfer in business schools and value generating activities of the value chain of companies?
Studied population and data collection To answer these four questions, we relied on data collected from the population of the tenured faculty members based in 35 different business schools from all across Canada. A web-based survey was used in combination with a telephone survey to collect data from these faculty members. The data were collected between December 2009 and March 2010. In a first stage, the respondents were contacted by email to answer a web-based survey. In order to improve the response rate, the survey was designed according to the principles formulated by Gaddis, 1998; Dillman, 2000; Dillman, Tortora and Bokwer, 1998; Dillman and Bowker, 2001. In a second stage, and again to increase the participation rate, a survey firm contacted, by phone, faculty members who did not participate in the web-based survey, to request their participation in a phone-based survey version of the questionnaire. This two stage procedure generated 807 usable questionnaires for a response rate of 62%.
Integrating knowledge and technology transfer expert advice provided by faculty members in business schools in a value chain conceptual framework
Knowledge moves from university to industry with the help of intermediaries (Howells, 2006). Most studies on the transfer of academic knowledge to companies focus their attention on university technology transfer offices as intermediary agents between academics and companies ( ). This study shifts the emphasis from the organizational level to the individual level and adopts the individual faculty member as its unit of analysis. Two arguments justify this choice of unit of analysis. First, academics are not required to disclose to their university administrators activities that do not lend to the commercial exploitation of their inventions and discoveries. Moreover, as shown by Siegel et al. (2003), Thursby et al. (2007), and Hall et al. (2003), many academics do not disclose commercial knowledge transfer activities to their university administrators, although prescribed by law. In order to capture as comprehensively as possible the services and expert advice provided by individual faculty members to companies, we conducted a survey asking faculty members in business schools to report various types of services and expert advice they provided to companies. Second, we assume, as pointed out by Searle Renault (2006), that academics make many key decisions about how to transform their research results and expertise into marketable product innovations. Therefore, understanding how faculty members in business schools make decisions is
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critical to shed light on the contribution of university knowledge in the value chains of companies. Furthermore, prior studies on the transfer of academic knowledge to companies usually use patents, licensing and spin-offs as their dependent variables because such data offer a perfect tool for an objective, quantitative analysis of knowledge transfer (Agrawal, 2001). The use of these easily available quantitative data has come at the expense of investigations into other forms of knowledge and technology transfer. We suggest that focusing on patents, licensing and spin-off launching captures only a small fraction of economically valuable services provided by faculty members in business schools. Indeed, faculty members in business schools deliver value to companies through a much larger variety of ways. Howells (2006) has developed a more comprehensive perspective in approaching the ways intermediaries help companies at the different stages of their innovation process by focusing on functions and roles such as foresight and diagnostics, scanning and information processing, knowledge processing and combination/recombination, gatekeeping and brokering, testing and validation, accreditation, validation and regulation, protection of results, commercialization, and evaluation of outcomes. Following Lundquist (2003) and Phan and Siegel (2006), we assume that knowledge and technology transfer occurs within value chains. In knowledge and technology transfer, a generic value chain involves a chain of value-adding activities undertaken to transform knowledge and expert advice into commercialized product innovations sold to customers. Therefore, we disaggregated the services and expert advice provided to companies into a sufficient level of detail, in order to better understand how individual academics may help companies in the different value-adding activities of their value chains. Then, we integrated the services and expert advice provided to companies into the technologically and economically distinct value creation activities of Porter’s value chain because his value chain is both generic and well known in the milieu of faculty members in business schools (Figure 1). The value chain framework of Michael Porter provides a model to analyze how the expert advice provided by faculty members in business schools may help companies to create value and create competitive advantage.
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Figure 1 Potential contribution of faculty members of business schools in the valueadding activities of the value chain of companies
External expert advice
External expert advice
External expert advice
External expert advice
External expert adique
External expert ad
External expert advice
Eternal expert advice
External expert advice
In providing expert advice to companies, faculty members of business schools have to figure out in what value-adding activities of the value chains of companies they can add value, and then develop and provide expert advice that match the realities of these companies. To collect information on this issue, we conducted a survey in which we asked faculty members in business schools the following question: «Over the past 3 years, how frequently have you provided expert advice to companies on: management, accounting, finance, strategic planning, quality control, information systems (activities that are part of company infrastructure activities), human resources management, technology development, procurement, inbound logistics, operations, outbound logistics, marketing and sales, and service (installation, after-sales service, complaint handling, training and so on?)», where 1= never and 5= very often. As can be seen from Table 1, the computation of the frequency of the answers for the questions on the expert advice offerings of business school faculty members to companies shows that, overall, a small proportion of faculty members provided often or very often expert advice to companies in the different activities of their value chain. More specifically, with the exception of the support activities regarding the company infrastructure, management and strategic planning, between 73% and 91% of the respondents never provided any expert advice in the other primary and support activities
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of the value chain of companies. The results reported in the last two rows of Table 1 also show that 26.4%, or 213 faculty members, never provided any expert advice linked to the value chain of companies, while not even one respondent provided often or very often expert advice to companies in all the primary and support activities comprised in the value chain of companies. These results suggest that the expert advice offerings of faculty members of business schools are very specialized. We will now look into this issue by considering how the specialization in the expert advice offerings might be linked to disciplinary boundaries. To compare the level of expert advice provided by the business school faculty members to companies across disciplines, we used Duncan’s post hoc test on ranked data because the variables referring to experts advice provided by faculty members to companies have a categorical and ordinal nature (Ouimet et al., 2006). This test was used to group the different disciplines into homogeneous subsets, that is disciplines between which the differences of means are not statistically significant, and hence, to compare the means of the different subsets. The null hypothesis tested is the equality of means for the expert advice offerings between the different disciplines. The results of Duncan’s post hoc test are reported in Table 2 for expert advice offerings related to the firm’s primary activities, and in Table 3 for expert advice offerings related to the firm’s support activities. The results of Duncan’s tests regarding expert advice offerings related to the primary activities of companies are reported in Table 2. They indicate that, for each of the five primary activities, there are three homogeneous subsets of disciplines that are statistically different with regard to the level of expert advice offerings. Overall, we can see that faculty members in operational research more frequently provided expert advice to companies than their colleagues in the other disciplines on inbound logistics, operations and outbound logistics (Tables 2a, 2b, 2c). Likewise, faculty members in marketing more frequently provided expert advice to companies than their colleagues in other business disciplines in marketing and sales and in service (Tables 2d and 2f). No statistically significant differences were found between the other disciplines with respect to the level of expert advice offerings. As for the expert advice offerings related to the company’s support activities, the results of Duncan’s tests reported in Table 3 indicate that there are many differences between disciplines. With one exception regarding the provision of expert advice on quality control (Table 3e), overall, faculty members have the tendency to more frequently provide expert advice on activities of the value chain of companies that are closely linked to their disciplines.
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Table 1 Frequency of expert advice offerings by business schools faculty members Over the past 3 years, how frequently have you provided expert advice to companies on...? Never
Rarely
Sometimes Often Very Often In % of business schools faculty members (Number of faculty members)
Total
PRIMARY ACTIVITIES : Inbound logistics Operations Outbound logistics Marketing and sales Service
90.0 (726) 84.0 (678) 91.1 (735) 73.5 (593) 83.8 (676)
4.3 (35) 7.4 (60) 3.8 (31) 7.1 (57) 5.3 (43)
3.5 (28) 6.1 (49) 3.5 (28) 10.8 (88) 8.3 (67)
2.1 (17) 1.6 (13) 1.4 (11) 5.6 (45) 1.4 (11)
.1 (1) .9 (7) .2 (2) 3.0 (24) 1.2 (10)
100.0 (807) 100.0 (807) 100.0 (807) 100.0 (807) 100.0 (807)
SUPPORT ACTIVITIES : Firm infrastructure activities Management Accounting Finance Strategic planning Quality control Information Systems
44.8 (362) 85.6 (691) 81.7 (660) 56.3 (454) 87.1 (703) 81.5 (658)
18.5 (149) 5.2 (42) 7.2 (58) 12.6 (102) 6.2 (50) 6.8 (55)
22.2 (179) 7.1 (57) 7.1 (57) 19.7 (159) 5.0 (40) 7.1 (57)
10.5 (85) 1.5 (12) 3.1 (25) 8.7 (70) 1.2 (10) 3.5 (28)
4.0 (32) .6 (5) .9 (7) 2.7 (22) .5 (4) 1.1 (9)
100.0 (807) 100.0 (807) 100.0 (807) 100.0 (807) 100.0 (807) 100.0 (807)
73.2 (590) 79.4 (641) 91.0 (734)
9.0 (73) 7.6 (61) 4.3 (35)
11.6 (94) 9.8 (79) 3.3 (26)
4.3 (35) 3.0 (24) 1.2 (10)
1.9 (15) .2 (2) .2 (2)
100.0 (807) 100.0 (807) 100.0 (807) N 213
Other support activities Human resources management Technology development Procurement
Percentage and number of business school faculty members that Never provided any expert advice in the 14 activities of the value chain of companies Percentage and number of business school faculty members that Often or Very often provided expert advice in all the 14 activities of the value chain of companies
% 26.4 0.0
0
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Table 2 Means of the variables referring to expert advice related to the firm’s primary activities provided to companies by business school faculty † members for groups of disciplines in homogeneous subsets: Duncan’s test Disciplines • • • • • • • •
(2a) Expert advice to companies on: Inbound logistics Subsets for α =0.05 N 1 2
Human Resources Management Economics Finance Accounting Information Management Marketing Management Operational research
116 58 78 103 64 119 221 48
373.61 377.16 383.82 390.71 395.05 403.84
383.82 390.71 395.05 403.84 421.71 501.95
.157 .067 Significance †† (2c) Expert advice to companies on: Outbound logistics Subsets for α =0.05 Disciplines N 1 2 • • • • • • • •
Finance Human Resources Management Economics Accounting Information Management Marketing Management Operational research
78 116 58 103 64 119 221 48
Significance †† (2f) Expert advice to companies on: Service Disciplines N • • • • • • • •
Economics Finance Accounting Operational research Information Management Human Resources Management Management Marketing
Significance ††
58 78 103 48 64 116 221 119
3
372.91 374.60 374.60 387.35 393.16 408.93
1.000
3
387.35 393.16 408.93 424.06 506.63
.069
.052
1.000
1
2
3
345.65 365.36 372.43 379.38 381.26 383.34
Disciplines • • • • • • • •
(2b) Expert advice to companies on: Operations Subsets for α =0.05 N 1 2 3
Finance Economics Human Resources Management Marketing Accounting Information Management Management Operational research
78 58 116 119 103 64 221 48
• • • • • • • •
419.56 440.09
.124
.356
(2d) Expert advice to companies on: Marketing and Sales Subsets for α =0.05 N 1 2
Economics Accounting Information Management Human Resources Management Finance Operational research Management Marketing
Significance ††
383.28 385.44 419.56
532.79 .308
Significance ††
Disciplines
359.51 359.52 363.24 383.28 385.44
58 103 64 116 78 48 221 119
4
1.000
3
315.06 327.05 339.34 342.62 353.35 366.87 421.47 624.28 .055
1.000
1.000
379.38 381.26 383.34 427.49 483.75
.148
.050
1.000
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Table 3 Means of the variables referring to expert advice related to the firm’s support activities provided to companies by business school faculty † members for groups of disciplines in homogeneous subsets: Duncan’s test Disciplines • • • • • • • •
(3a) Expert advice to companies on: Management Subsets for α =0.05 N 1 2 3 4
Economics Finance Operational research Accounting Information Management Marketing Human Resources Management Management
58 78 48 103 64 119 116 221
.051
Significance ††
Disciplines • • • • • • • •
229.63 291.60
Human Resources Management Information Management Marketing Operational research Management Economics Accounting Finance
Economics Finance Human Resources Management Marketing Information Management Accounting Management Operational research
Significance ††
.166
338.27 383.42
.154
382.42 417.84
.277
(3c) Expert advice to companies on: Finance Subsets for α =0.05 N 1 2 3 116 64 119 48 221 58 103 78
341.27 348.23 356.43 364.01
58 78 116 119 64 103 221 48
348.23 356.43 364.01 391.81
6
417.84 478.12
478.17 499.03
.057
4
.509
5
391.81 411.71 467.00 585.84
.353 Significance †† (3e) Expert advice to companies on: Quality control Disciplines N 1 • • • • • • • •
291.60 314.89 338.27
5
365.76 367.06 384.03 390.10 407.44 409.94
.064
.070
.365
1.000 2
390.10 407.44 409.94 434.49 435.23 .053
1.000
Disciplines • • • • • • • •
(3b) Expert advice to companies on: Accounting Subsets for α =0.05 N 1 2
Marketing Human Resources Management Operational research Economics Information Management Management Finance Accounting
119 116 48 58 64 221 78 103
• • • • • • • •
58 78 48 103 64 116 119 221
58 78 119 116 221 103 48 64
3
323.85 323.98 372.83 380.15 457.43 514.68 .090
(3f) Expert advice to companies on: Information System N 1 2
Economics Finance Marketing Human Resources Management Management Accounting Operational research Information Management
Significance ††
295.70 305.45 323.85 323.98
.402
Significance ††
• • • • • • • •
.181
(3d) Expert advice to companies on: Strategic planning Subsets for α =0.05 N 1 2
Economics Finance Operational research Accounting Information Management Human Resources Management Marketing Management
Disciplines
598.27 .058
Significance ††
Disciplines
355.94 357.00 363.33 372.98 376.18 391.42 397.24
350.60 355.77 366.93 369.32 396.44 396.97
.059 3
396.44 396.97 424.18 665.22
.057
.224
1.000
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Table 3 (Continued) Means of the variables referring to expert advice related to the firm’s support activities provided to companies by business school faculty † members for groups of disciplines in homogeneous subsets: Duncan’s test Disciplines • • • • • • • •
(3g) Expert advice to companies on: Human Resource Management Subsets for α =0.05 N 1 2 3
Economics Finance Operational research Accounting Information Management Marketing Management Human Resources Management
58 78 48 103 64 119 221 116
306.93 316.54 319.70 334.77 356.13
Finance Economics Human Resources Management Accounting Marketing Information Management Management Operational research
78 58 116 103 119 64 221 48
319.70 334.77 356.13 371.39 443.81 591.72
.075 Significance †† (3i) Expert advice to companies on: Procurement Disciplines N 1 • • • • • • • •
4
372.82 381.81 388.07 394.69 400.73 404.78
.055
1.000 2
1.000
Disciplines • • • • • • • •
(3h) Expert advice to companies on: Research and Development Subsets for α =0.05 N 1 2 3
Human Resources Management Accounting Finance Economics Marketing Management Operational research Information Management
Significance ††
116 103 78 58 119 221 48 64
343.83 352.66 362.81 373.92 390.07
390.07 431.44
4
431.44 474.80 551.20
.089
.087
.073
1.000
3
381.81 388.07 394.69 400.73 404.78 420.40 471.48
.120 .058 1.000 Significance †† Duncan’s post hoc test compares means for groups in homogeneous subsets. We performed Duncan’s test on ranked data because the variables referring to expert advice by business school faculty members to companies have a categorical and ordinal nature and they were measured on a Likert scale of frequency ranging from 1= Never to 5= Very often. †† When the significance test is above the threshold = 0.05, the null hypothesis (non differences of means) cannot be rejected. †
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Linking the expert advice provided to companies to business model elements There is no general theory for the field of knowledge and technology transfer (MolasGallart et al., 2002; Reisman, 2005). Furthermore, Howells (2006) concluded his literature review of intermediaries, such as faculty members in business schools, in the innovation process by pointing out that existing studies have not generally been wellgrounded theoretically. Once an academic has chosen the type of expert advice he should offer to companies, the next question is to figure out how to create value for companies, what types of companies to reach, how to relate to companies, through which resources, with what strategies and finally, how to make money? Each of these choices involves different elements of business models. For individual academics, the formulation of a business model is a key decision because once the model is set, the expertise well developed, it becomes difficult to change the business model due to entry costs, forces of inertia and resistance to change (Zott and Amit, 2009). Ostenwalder et al. (2005) have reviewed the most common building blocks of business models. In this paper, we propose to rely on the Chesbrough (2007; 2009) approach to the business model concept because it provides generic components to analyze the different sources of value rather than specific sources of value for particular types of companies. However, as pointed out by Teece (2009), the business model concept constitutes a conceptual framework, not a theory. Therefore, as pointed out by Rasmussen (2007), it does not enable to derive predictions about choices to be made by individual academics, but, however, it helps to identify factors that could influence the choices to be made. Therefore, we have integrated, into a business model framework, six building blocks likely to influence decisions regarding academics’ expert advice offerings: customer value proposition, market segment, revenue generation mechanisms, key resources, positioning within the value network, and strategies. The business model conceptual framework is still developing, and until now, has been used in case study research and theoretical papers, as exhibited in the forthcoming issue of Long Range Planning on business models. To the extent of our knowledge, this study is the first to operationalize, for a quantitative study, the business model framework in order to examine the interaction between academics’ expert advice and the other building blocks of their business models.
Customer value proposition In the business model conceptual framework, the starting point is the development of a value proposition. Companies do not want expert advice from academics, they want expert advice that help them to solve problems, get jobs done more effectively, conveniently, and affordably (Teece, 2009). However, academics cannot expect to meet firms’ needs and requirements with generic expert advice because of the large variety of their situations in terms of industry, resources, capabilities, etc. The value created by the expert advice provided to companies is likely to vary according to the degree of customization of the expert advice provided. Hence, the best customer value proposition that an academic may offer is to tailor custom-made expert advice solutions for a single client company. It is likely an excessive expectation, given that most academics have
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limited time and intellectual resources. A possible compromise for academics is to offer expert advice that provides half customized solutions. Hence, one might hypothesize that expert advice which corresponds mainly or almost only to basic research results creates less value for client companies than expert advice which corresponds to problem-specific solutions customized for the needs and requirements of a single client company. In this study, customer value proposition was measured on a 5-point scale to capture the degree of customization of expert advice that faculty members provided to companies where: 1= Almost only customized solutions; 2= Mainly customized solutions; 3= Half customized solutions and half basic research; 4= Mainly basic research; 5= Almost only basic research. As can be seen in the upper part of Table 4, the distribution of faculty members’ answers with regard to this scale shows that 31.7% provided almost only or mainly customized solutions to companies. In contrast, 38.6% provided almost only or mainly basic research to companies. The lower part of Table 4 reports the results of Duncan’s post hoc test on ranked data that compares the degree of customization of expert advice that faculty members provided to companies across disciplines. The results of this test indicate that there are three homogeneous subsets of disciplines that are statistically different. Faculty members in economics have a highest mean rank score than faculty members in marketing, management, human resources management, accounting, and information management. This indicates that the faculty members in these last five disciplines provided more customized solutions to companies than their colleagues in economics. Likewise, faculty members in marketing provided more customized solutions to companies than faculty members in operational research and finance.
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Table 4 Frequency distribution of the categorical variable measuring the degree of customization of expert advice that faculty members provided to companies and comparison of means of this variable across disciplines (post hoc—multiple comparisons, Duncan’s Test) Which of the following statements best describes the expert advice you provided to companies over the last three years..? Almost only customized solutions
Mainly customized solutions
Half customized Mainly basic Almost only basic Total solutions and half research research basic research In % of business school faculty members (Number of faculty members) 15.2 16.5 22.9 6.2 39.2 100.0 (123) (133) (185) (50) (316) (807) Degree of customization of expert advice (1= Almost only customized solutions to 5= Almost only basic research) Subsets for α =0.05 Disciplines
N
1
• • • • • • • •
119 221 116 103 64 48 78 58
338.19 390.23 404.75 408.13 410.98
Marketing Management Human Resources Management Accounting Information Management Operational research Finance Economics
Significance ††
.060
2
390.23 404.75 408.13 410.98 436.68 445.04 .171
3
436.68 445.04 492.71 .127
Duncan’s post hoc test compares means for groups in homogeneous subsets. We performed Duncan’s test on ranked data because the variable referring to degree of customization of solutions provided by business school faculty members to companies have a categorical and ordinal nature and they were measured on a scale of frequency ranging from 1= Almost only customized solutions to 5= Almost only basic research. †
††
When the significance test is above the threshold = 0.05, the null hypothesis (non differences of means) cannot be rejected.
Market segment Faculty members of business schools cannot avoid identifying a market segment (Chesbrough, 2007; 2009). They must ask themselves for what groups of companies the expert advice provided is useful and creates value. One might hypothesize that large companies are less likely than SMEs to suffer from a lack of in-house expert advice in the different value-adding activities of their value chain. One might also hypothesize that it is easier for academics to provide services to companies that are located in their region rather than in other provinces or other countries. Hence, one may hypothesize that the expert advice provided by academics will be positively associated with the provision of services to SMEs located in the region where their university is located. With regard to the sizes of companies for which business school faculty members provided expert advice, the results reported in Table 5 indicate that nearly half of the faculty members provided expert advice to companies with more than 100 employees, 19% to companies with less than 10 employees, 17.27% to companies with between 10
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and 49 employees, and 14.27% to companies with between 50 and 100 employees. The figures in Table 5 also show that the highest percentages of faculty members that provided expert advice to companies with more than 100 employees are in information management systems and operational research (65.73% and 59.82% respectively), whereas the highest percentages of faculty members that provided expert advice to companies with less than 10 employees are in marketing and finance (24.19% and 22.93% respectively). To compare the percentages of the different types of companies for which business school faculty members provided expert advice across disciplines, we performed Duncan’s post hoc test. The results of this test reported in Table 6 indicate that, for each type of companies, there are two homogeneous subsets of disciplines that are statistically different. More specifically, the percentage of faculty members that provided expert advice to companies with less than 10 employees is higher in finance and marketing than in operational research. Conversely, there is no significant statistical difference between faculty members in the other disciplines (Table 6a). Likewise, the percentage of faculty members that provided expert advice to companies with between 10 and 49 employees is higher in economics and accounting than in information management, whereas there is no significant statistical difference between the other disciplines (Table 6b). For the companies with between 50 and 100 employees, the results reported in Table 6c show that there is no significant statistical difference between the disciplines with regard to the percentage of faculty members that provided to them expert advice, except between economics and operational research. More faculty members in operational research provided expert advice to companies with between 50 and 100 employees than their colleagues in economics. Finally, for the companies with more than 100 employees, the percentage of faculty members that provided expert advice to companies in human resources management, economics, operational research, and information management, is higher than in finance, accounting, marketing, and management (Table 6d).
When we consider the geographical areas of localization of companies to which business school faculty members provided expert advice, it can be seen from Table 7, that overall and regardless of the disciplines, 58% of faculty members provided expert advice to companies localized in their immediate area (within 100 km), 13.13% to companies localized between 100 and 250 km from their university of affiliation, 8.32% elsewhere in their province, 7.74% elsewhere in Canada, and 12.85% in other countries. Moreover, the comparison of the percentages of different geographical areas where faculty members provided expert advice shows very little differences between disciplines. Hence, the results of Duncan’s post hoc test show that faculty members in marketing, accounting and human resources management are more present at the regional level (within 100 km) than their colleagues in finance (Table 8a), while there is no significant statistical difference between the disciplines with regard to the percentage of faculty members that provided expert advice to companies localized in the other four geographical areas (between 100 and 250 km from the university of affiliation of the faculty member, elsewhere in his/her province, elsewhere in Canada, and in other countries). (Tables 8b8e).
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Table 5 Percentage of different types of companies for which faculty members provided expert advice Disciplines Management Human Finance Marketing Information Accounting resources management Sizes of companies Management
Operational research
Economics
All disciplines
Average percentage of companies Companies with less than 10 employees Companies with between 10 and 49 employees Companies with between 50 and 100 employees Companies with more than 100 employees
21.08
15.03
22.93
24.19
12.81
19.58
8.75
13.97
18.98
17.18
18.40
18.72
17.13
8.44
21.22
12.50
22.65
17.27
13.55
16.17
14.88
13.33
13.02
14.75
18.93
8.23
14.27
48.19
50.40
43.47
45.35
65.73
44.45
59.82
55.15
49.48
Total
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
Number of faculty members
168
88
46
87
48
60
28
34
559
NOTE: Faculty members that did not provide expert advice to companies (N = 213) and those that answered Do Not Know (N = 35) are excluded from this computation.
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Table 6 Means of the variables referring to the percentages of the different types of companies for which business school faculty members provided expert advice for groups of disciplines in homogeneous subsets: Duncan’s test Disciplines • • • • • • • •
(6a) Percentage of companies with less than 10 employees Subsets for α =0.05 N 1 2
Operational research Information Management Economics Human Resources Management Accounting Management Finance Marketing
28 48 34 88 60 168 46 87
8.75 12.81 13.97 15.03 19.58 21.08
12.81 13.97 15.03 19.58 21.08 22.93 24.19
.063 .098 Significance † (6c) Percentage of companies with between 50 and 100 employees Subsets for α =0.05 Disciplines N 1 2 • • • • • • • •
Economics Information Management Management Marketing Accounting Finance Human Resources Management Operational research
Significance †
34 48 168 87 60 46 88 28
8.23 13.02 13.55 13.33 14.75 14.88 16.17 .086
13.02 13.55 13.33 14.75 14.88 16.17 18.93 .243
(6b) Percentage of companies with between 10 and 49 employees Subsets for α =0.05 Disciplines N 1 2 • • • • • • • •
Information Management Operational research Marketing Management Human Resources Management Finance Accounting Economics
48 28 87 168 88 46 60 34
.083
Significance †
Disciplines • • • • • • • •
8.44 12.50 17.13 17.18 18.40 18.72
12.50 17.13 17.18 18.40 18.72 21.22 22.65 .092
(6d) Percentage of companies with more than 100 employees Subsets for α =0.05 N 1 2
Finance Accounting Marketing Management Human Resources Management Economics Operational research Information Management
46 60 86 168 88 34 28 48
Significance †
43.48 44.45 45.35 48.19 50.44 55.15 59.82 .079
50.44 55.15 59.82 65.73 .082
NOTE: Faculty members that did not provide expert advice to companies (N = 213) and those that answered Do Not Know (N = 35) are excluded from this comparison. †
When the significance test is above the threshold = 0.05, the null hypothesis (non differences of means) cannot be rejected.
16
Table 7 Percentage of different geographical areas for which faculty members provided expert advice Disciplines Management Human Finance Marketing Information Accounting resources management Sizes of companies Management
Operational research
Economics
All disciplines
Average percentage of companies Companies in my region (within 100 km) Companies between 100 and 250 km Companies elsewhere in my province Companies elsewhere in Canada
57.46
65.64
41.81
59.44
55.04
65.52
52.82
53.82
57.97
14.10
12.13
16.81
9.78
17.00
11.85
13.33
11.03
13.12
8.09
5.88
11.57
9.61
9.45
6.52
11.43
6.97
8.32
7.52
6.07
13.57
8.07
6.06
7.18
7.78
7.57
7.74
Companies in other countries
12.83
10.28
16.24
13.10
12.45
8.93
14.64
20.61
12.85
Total
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
Number of faculty members
167
89
47
87
47
60
28
33
558
NOTE: Faculty members that did not provide expert advice to companies (N = 213) and those that answered Do Not Know (N = 36) are excluded from this computation.
17
Table 8 Means of the variables referring to the percentages of the different geographical areas for which business school faculty members provided expert advice to companies for groups of disciplines in homogeneous subsets: Duncan’s test Disciplines • • • • • • • •
(8a) Companies in my region (within 100 km) N 1 2
Finance Operational research Economics Information Management Management Marketing Accounting Human Resources Management
47 28 33 47 167 87 60 89
Significance †† (8c) Companies elsewhere in my province Disciplines N • • • • • • • •
Human Resources Management Accounting Economics Management Information Management Marketing Operational research Finance
Significance †† (8e) Companies in other countries Disciplines • • • • • • • •
Accounting Human Resources Management Information Management Management Marketing Operational research Finance Economics
89 60 33 167 47 87 28 47
41.81 52.82 53.82 55.04 57.46
.063
52.82 53.82 55.04 57.46 59.44 65.52 65.64 .146
Disciplines • • • • • • • •
Marketing Economics Accounting Human Resources Management Operational research Management Finance Information Management
Disciplines
5.88 6.52 6.97 8.09 9.45 9.61 11.43 11.57
• • • • • • • •
N
1
60 89 47 167 87 28 47 33
8.93 10.28 12.45 12.83 13.10 14.64 16.23 20.61
87 33 60 89 28 167 47 47
9.78 11.03 11.85 12.13 13.32 14.10 16.81 17.00 .214
Significance ††
1
.194
(8b) Companies between 100 and 250 km N 1
(8d) Companies elsewhere in Canada N 1
Information Management Human Resources Management Accounting Management Economics Operational research Marketing Finance
Significance ††
47 89 60 167 33 28 87 47
6.06 6.07 7.18 7.51 7.57 7.78 8.07 13.57 .101
.055 Significance †† NOTE: Faculty members that did not provide expert advice to companies and those that answered Do Not Know are excluded from this comparison. † When the significance test is above the threshold = 0.05, the null hypothesis (non differences of means) cannot be rejected.
18
Revenue generation mechanisms How are academics compensated for the expert advice they provide to companies? The revenue streams of faculty members in business schools come primarily in the form of research grants and consulting services. One might hypothesize that the willingness of companies to pay for the expert advice they acquire from faculty members in business schools measures, at least in part, the value created for companies. Hence, one might hypothesize that increases in revenues from consulting services raise the offering of expert advice in value-adding activities of the value chains of companies, while increases in revenues generated from research grants decrease the offering of expert advice in value-adding activities of the value chains of companies. To address this issue, we asked the business school faculty members to indicate the total amount of research funding (for research projects and infrastructure) of all their research projects during the past 12 months. The distribution of faculty members with regard to the amounts of their total research funding is reported in Table 9. It can be seen that when we do not distinguish between disciplines, 41.4% of faculty members have less than 10 000$ as their total research budget; 25.8% between 10 000$ and 30 000$; 18.5% between 30 000$ and 100 000$; and 14.3% more than 100 000$. Table 9 also shows that the highest percentages of faculty members that have less than 10 000$ as total research funding are in accounting and finance (60.2% and 44.8% respectively), while the lowest proportions of faculty members with less than 10 000$ of research budget are in economics and information management systems (29.3% and 29.7% respectively). The largest proportion of faculty members with research budgets between 10 000$ and 30 000$ are in economics and operational research (46.6% and 39.6% respectively), while the lowest percentages are in human resources management (19.8%) and management (20.8%). Likewise, the highest proportions of faculty members with research funding between 30 000$ and 100 000$ are in human resources management and information management systems (25.9% and 25.0% respectively), while the lowest percentages are in marketing (11.8%), operational research (12.5%), and accounting (12.6%). Finally, the highest percentages of faculty members with more than 100 000$ of research budget are in information management (21.9%), management (17.7%), and human resources management (17.2%), while the lowest percentages are in accounting (3.9%) and finance (9.0%). Surprisingly, 48.7% of the surveyed faculty members did not generate any personal income from consulting activities provided to companies, while 8.3% generated more than 20% of their income from consulting activities. Faculty members who generate more than 20% of their income through consulting activities are more frequently associated with management, human resources management and marketing than with the other research fields. Faculty members in human resources management, information management and operational research are the most likely, compared to others, to generate no income from consulting. Overall, faculty members of business schools do not derive a significant proportion of their personal income from consulting activities. Moreover, those faculty members who generate less than 10% of their income from consulting do
19
not generate enough income to consolidate their expertise to build a portfolio of clients at a level where they could be considered as professional consultants.
20
Table 9 Distribution of faculty members according to their total research funding Disciplines Management Human Finance Marketing Information Accounting resources management Total research funding Management Percentage of faculty members (Number of faculty members) Less than 10 000$ 41.6 37.1 44.8 42.0 29.7 60.2 (92) (43) (35) (50) (19) (62) Between 10 000$ and 30 000$ 20.8 19.8 23.1 31.1 23.4 23.3 (46) (23) (18) (37) (15) (24) Between 30 000$ and 100 000$ 19.9 25.9 23.1 11.8 25.0 12.6 (44) (30) (18) (14) (16) (13) More than 100 000 $ 17.7 17.2 9.0 15.1 21.9 3.9 (39) (20) (7) (18) (14) (4)
Operational research
Economics
All disciplines
33.3 (16) 39.6 (19) 12.5 (6) 14.6 (7)
29.3 (17) 46.6 (27) 13.8 (8) 10.3 (6)
41.4 (334) 25.8 (209) 18.5 (149) 14.3 (115)
Total
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
Number of faculty members
221
116
78
119
64
103
48
58
807
21
Table 10 Distribution of faculty members according to their personal income generated from consulting activities Disciplines Management Human Finance Marketing Information Accounting Operational resources management research Personal income from consulting Management Percentage of faculty members (Number of faculty members) 0% 44.8 53.4 47.4 44.5 54.7 53.4 50.0 (99) (62) (37) (53) (35) (55) (24) Between 1 and 10% 28.5 30.2 23.1 31.1 31.2 25.3 33.3 (63) (35) (18) (37) (20) (26) (16) Between 10 and 20% 16.3 6.9 21.8 12.6 12.5 12.6 14.6 (36) (8) (17) (15) (8) (13) (7) More than 20% 10.4 9.5 7.7 11.8 1.6 8.7 2.1 (23) (11) (6) (14) (1) (9) (1)
Economics
All disciplines
48.3 (28) 34.5 (20) 13.8 (8) 3.4 (2)
48.7 (393) 29.1 (235) 13.9 (112) 8.3 (67)
Total
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
Number of faculty members
221
116
78
119
64
103
48
58
807
22
Key resources Knowledge is the key resource for academics who are in the business of providing expert advice to companies. In theory, academics have equal access to the pool of open science. In practice, however, one might hypothesize that academics who publish articles in scholarly journals dispose of additional knowledge and expertise likely to help them make their business model work. Therefore, as the number of publications of academics increases, so does their level of provision of expert advice in the value chains of companies. Furthermore, we also hypothesize that academics whose publications are more cited than other academics benefit from a reputation premium that is associated with a higher provision of expert advice to companies (see Tables 11-14 for the operational definitions of key resources). In order to generate data on knowledge resources, we have collected a series of statistics using the Publish or Perish (PoP) database, which uses Google Scholar to retrieve and analyze academic citations, and to calculate a series of citation metrics. Our reliance on the PoP is justified by the fact that WoS includes only citations of articles published in ISI listed journals in social sciences disciplines. Hence, citations of books, book chapters, conference papers, dissertations, theses, working papers, reports, conference papers, and articles published in non-ISI journals are not included (Meho and Young, 2007). Moreover, journal articles published in a language other than English (LOTE) are also discarded from ISI listed journals. Despite its notoriety, WoS is more and more criticized by academics for its underestimation of academics’ performance, especially in social sciences. Hence, Butler (2006) showed that, whereas for Natural sciences and Health sciences, between 69% and 85% of publications are published in ISI listed journals, for social sciences, especially Management and History Education and Arts, only 4.5% to 19% of the publications are published in ISI listed journals. The PoP database takes into account all types of publication outputs, including those discarded by WoS. We suggest that in a project on formal and informal knowledge transfer activities of faculty members in business schools, WoS data have to be complemented by the reliance on PoP data. This second exercise of data collection shows that: • 7.4% of respondents have no contribution (on a lifetime period) reported in Publish or Perish;
•
25% have between 1 and 5 contributions;
•
9.4 % have more than 51 contributions;
•
17.5% have no citation;
•
22.6% have between 1 and 20 citations;
•
12% have more than 500 citations;
•
Faculty members in accounting have less contributions and less citations than their colleagues in other fields;
23
•
Faculty members in information management have more contributions and more citations than their colleagues in other research fields.
It suggests that a majority of faculty members provide expert advice that derives from the application of existing scientific knowledge to specific problems rather than from new research knowledge that they have created.
Table 11 Distribution of faculty members according to their total number of contributions in PoP Number of faculty members
%
0 contribution
60
7.4
Between 1 and 5 contributions
200
24.8
Between 6 and 20 contributions
296
36.7
Between 21 and 50 contributions
175
21.7
Between 51 and 100 contributions
46
5.7
More than 100 contributions
30
3.7
807
100,0
Total
Table 12 Means of the total number of contributions of faculty members, according to PoP, by business school faculty members for groups of disciplines in homogeneous subsets: Duncan’s test† Subsets for α =0.05 Disciplines N 1 2 3 • • • • • • • •
Accounting Marketing Human Resources Management Management Finance Operational research Economics Information Management
Significance †† †
103 119 116 221 78 48 58 64
10.32 18,79 18.84 20.18
.075
18,79 18.84 20.18 27.18 27.23
.140
27.18 27.23 32.98 33.03 .298
Duncan’s post hoc test compares means for groups in homogeneous subsets. When the significance test is above the threshold = 0.05, the null hypothesis (non differences of means) cannot be rejected.
††
24
Table 13 Distribution of faculty members according to their total number of citations in PoP Number of faculty members
%
0 citation
141
17,5
Between 1 and 20 citations
183
22,6
Between 21 and 100 citations
204
25,3
Between 101 and 500 citations
182
22,6
Between 501 and 1000 citations
54
6,7
More than 1000 citations
43
5,3
807
100,0
Total
Table 14 Means of the total number of citations of faculty members, according to PoP, by business school faculty members for groups of disciplines in homogeneous subsets: Duncan’s test† Subsets for α =0.05 Disciplines N 1 2 3 • • • • • • • •
Accounting Human Resources Management Marketing Management Operational research Economics Finance Information Management
Significance ††
103 116 119 221 48 58 78 64
88.08 167.52 170.49 270.24 300.48 328.17
.177
167.52 170.49 270.24 300.48 328.17 483.36
.072
300.48 328.17 483.36 616.39 .059
Duncan’s post hoc test compares means for groups in homogeneous subsets. †† When the significance test is above the threshold = 0.05, the null hypothesis (non differences of means) cannot be rejected. †
Positioning in the value network Are the working relationships forged between academics in business schools and their partners in industry crucial to satisfy companies’ expectations? Do academics and their partners forge very close working relationships, practically like if they were in the same work group? Conversely, at the other extreme of the continuum, do academics and their partners forge very distant working relationships, practically like people that academics do not know well? One might hypothesize that the strength of ties (closeness) between academics and their client companies generates a common understanding that is
25
positively associated with a higher involvement of academics in the different valueadding activities of the value chain of companies. Likewise, one might hypothesize that academics’ higher degrees of collaboration with other types of experts that are part of the ecosystem linked to the production and delivery of expert advice or services for companies are positively associated with a higher involvement of academics in the different value-adding activities of the value chain of companies. The measure of strength of ties used for this variable was adapted from Hansen (1999). More specifically, we measured the value network and collaboration with experts that are part of the production and delivery of expert advice or services to companies on a 5-point scale. This scale captures the degree of closeness between faculty members and managers/employees in companies in the past three years: 1= Very close (practically like being in the same work group); 2= Somewhat close (like discussing and solving issues together); 3= Somewhat distant (like with people that you do not know well); 4= Distant (like a working group with which you can only have a quick exchange of information); 5= Very distant (practically like with people that you do not know at all). The upper part of Table 15 reports the distribution of faculty members’ answers with regard to the degree of closeness between them and managers/employees in companies, and shows that 40.1% forged very close or somewhat close working relationships with managers and employees in companies. In contrast, 38.7% forged, with managers and employees in companies, distant or very distant working relationships. The lower part of Table 15 reports the results of Duncan’s post hoc test on ranked data that compares the degree of closeness between academics and managers/employees in companies across disciplines. The results of this test indicate that there are three homogeneous subsets of disciplines that are statistically different. It can be seen from these results that faculty members in management forged more close working relationships with managers and employees than their colleagues in accounting and economics. Moreover, faculty members in economics were found to be more distant in their working relationships, from managers and employees in companies than their colleagues in information management systems, marketing, operational research, and human resources management.
26
Table 15 Frequency distribution of the categorical variable measuring the strength of ties between faculty members and managers/employees in companies and comparison of means of this variable across disciplines (post hoc—multiple comparisons, Duncan’s Test) Which of the following statements best describes the working relationship between yourself and managers/employees in companies in the past three years..? Very close = practically like being in the same work group
9.5 (77)
Somewhat close = like discussing and solving issues together
30.6 (247)
Somewhat distant = like with people that you do not know well
Distant = like a working group with which you can only have a quick exchange of information
Very distant = practically like with people that you do not know at all
In % of business school faculty members (Number of cases) 21.2 10.3 28.4 (171) (83) (229) Degree of strength of ties (1= Very close to 5= Very distant)
Total
100.0 (807)
Subsets for α =0.05 Disciplines
N
1
• • • • • • • •
221 64 119 48 116 78 103 58
366.38 387.50 390.62 395.31 396.59 429.72
Management Information Management Marketing Operational research Human Resources Management Finance Accounting Economics
Significance ††
.116
2
387.50 390.62 395.31 396.59 429.72 462.35 .060
3
429.72 462.35 476.79 .206
Duncan’s post hoc test compares means for groups in homogeneous subsets. We performed Duncan’s test on ranked data because the variables referring to the degree of strength of ties between business school faculty members and managers/employees in companies have a categorical and ordinal nature and they were measured on a scale of frequency ranging from 1= Very close to 5= Very distant.
†
††
When the significance test is above the threshold = 0.05, the null hypothesis (non differences of means) cannot be rejected.
Strategies
Strategy refers to a set of decisions and actions that aims to give a superior performance and ultimately a competitive advantage over rivals (Porter, 1996; Porter, 2008). Developing a strategy helps to understand what to do, what to become and how to plan to get there. A strategy defines the scope of intentions, in particular in relation to how academics will mobilize professional knowledge in order to develop and improve the provision of expert advice to companies. We hypothesize that, like companies, academics in business schools may attempt to gain and hold an advantage over rivals by developing three generic competitive strategies: competing by developing and exploiting niches or specialized markets linked to their field of expertise (specialization strategy); competing by developing new fields of expertise (diversification strategy); and competing by
27
providing expertise at lower costs than consulting companies and other academics (low cost strategy). The computation of the frequency of the answers to the questions on the strategies devised by business school faculty members in their offerings of expert advice to companies is reported in Table 16. As can be seen from this Table, 64.8% of faculty members never or rarely developed a specialization strategy linked to their offering of expert advice to companies, whereas 15.5% often or very often did so. Moreover, the data show that 66.4% of faculty members never or rarely developed a diversification strategy in order to compete in the market of expert advice offered to companies. At the other extreme, 10.9% of faculty members relied often or very often on a diversification strategy in order to compete with rivals on the market of expert advice offered to companies. Finally, as for the reliance on low cost strategies, the results reported in Table 16 show that 71.7% and 86.1% of faculty members never or rarely provided expert advice to companies at lower costs than consulting companies or other researchers, respectively. Conversely, 14.8% and 5.7% of faculty members often or very often provided expert advice to companies at lower costs than consulting companies or other researchers, respectively. Are there differences on competitive strategies devised by faculty members across disciplines? Table 17 reported the results of Duncan’s post hoc tests on ranked data that compare across disciplines the degree of frequency with which faculty members relied on the four strategies mentioned above. These results indicate that faculty members in marketing and management relied more frequently than their other colleagues on the specialization strategy in order to compete with their rivals on the market of expert advice to companies (Table 17a). The diversification strategy is more frequently used by faculty members in management than in accounting, economics, finance, information management, and operational research. Moreover, the diversification strategy is more frequently devised by faculty members in marketing than in accounting and in economics (Table 17b). Likewise, faculty members in information management systems rely less frequently on low cost strategies to compete with consulting companies than their colleagues in other disciplines, with the exception of their colleagues in accounting who rely as frequently as those in information management systems on such low cost strategies (Table 17c). Finally, faculty members in economics and in management more frequently rely on low cost strategies to compete with other researchers than is the case for faculty members in accounting. However, there are no significant statistical differences between faculty members in the other disciplines with respect to how they compete on costs with other researchers (Table 17d).
28
Table 16 Strategies devised by business school faculty members to compete with rivals on the market of expert advice to companies How frequently have you engaged in the following activities over the last three years in order to provide expert advice to companies..? Never Rarely Sometimes Often Very Often Total In % of business school faculty members (Number of faculty members) SPECIALIZATION STRATEGY : Developing and exploiting niches 52.2 12.6 19.7 11.6 3.9 100.0 or specialized markets linked to (310) (75) (117) (69) (23) (594) your field of expertise DIVERSIFICATION STRATEGY : Developing new fields of 50.2 16.2 22.7 8.2 2.7 100.0 expertise (298) (96) (135) (49) (16) (594) LOW COST STRATEGIES : providing expertise at lower costs 58.1 13.6 13.5 9.4 5.4 100.0 than consulting companies (345) (81) (80) (56) (32) (594) providing expertise at lower costs 73.1 13.0 8.2 4.4 1.3 100.0 than other researchers (434) (77) (49) (26) (8) (594)
29
Table 17 Means of the variables referring to the strategies devised by business school faculty members when they provide expert advice to companies for groups of disciplines in homogeneous subsets: Duncan’s test (a)Specialization strategy: Developing and exploiting niches or specialized markets linked to your field of expertise Subsets for α =0.05 Disciplines N 1 2
(b) Diversification strategy: Developing new fields of expertise Disciplines
N
• • • • • • • •
• • • • • • • •
68 25 51 53 29 90 95 183
Accounting Human Resources Management Economics Information Management Finance Operational research Management Marketing
68 90 25 53 51 29 183 95
228.77 253.41 259.12 268.48 272.19 294.36
294.36 338.90 349.54
Accounting Economics Finance Information Management Operational research Human Resources Management Marketing Management
Subsets for α =0.05 1 2 230.34 247.20 259.31 270.02 271.52 290.88
3
259.31 270.02 271.52 290.88 322.14
290.88 322.14 342.51
.055 .082 Significance † (c) Low cost strategy: Providing expertise at lower costs than consulting companies Subsets for α =0.05 Disciplines N 1 2
.083 .066 .110 Significance † (d) Low cost strategy: Providing expertise at lower costs than other researchers Subsets for α =0.05 Disciplines N 1 2
• • • • • • • •
• • • • • • • •
Information Management Accounting Marketing Finance Operational research Economics Management Human Resources Management
Significance †
53 68 95 51 29 25 183 90
238.72 266.04
.363
266.04 302.17 307.10 307.67 307.70 309.44 315.13 .164
Accounting Information Management Operational research Human Resources Management Finance Marketing Management Economics
Significance †
68 53 29 90 51 95 183 25
258.78 268.24 280.76 293.47 296.19 303.27
.140
268.24 280.76 293.47 296.19 303.27 319.35 319.56 .092
NOTE: Faculty members that did not provide expert advice to companies (N = 213) are excluded from this comparison. † Duncan’s post hoc test compares means for groups in homogeneous subsets. We performed Duncan’s test on ranked data because the variables referring to strategies used by business school faculty members to provide expert advice to companies have a categorical and ordinal nature and they were measured on a Likert scale of frequency ranging from 1= Never to 5= Very often. ††
When the significance test is above the threshold = 0.05, the null hypothesis (non differences of means) cannot be rejected.
30
Table 18 Summary results on key business model attributes by business discipline Disciplines Identification of business model elements Elements of value chain of companies
Customer value proposition Market segment
Revenue generation mechanisms
Human resources management Focus on human resources management and management
Economics
Finance
Accounting
Information management
Marketing
Management
Operational research
Focus on finances but to a lesser extent than accounting and finance
Focus on finance
Focus on accounting and finance
Focus on information management systems
Focus on marketing and sales, services and strategic planning
Focus on management and strategic planning
Higher customized solutions Serve less frequently companies within region than finance
Lower customized solutions Serve more frequently companies of 1049 employees than information management systems
Lower customized solutions Serve more frequently companies within region than marketing, accounting, and human resources management Serve more frequently companies 100 employees than finance
Higher customized solutions Serve less frequently companies within region than finance Serve more frequently companies
