Developing collaboration between research-oriented and practice-oriented experts in public administration: How does expert participation make a difference in public policy making and governance practice?

Abstract

The contradiction between experts’ research (or theory) and practitioners’ practice has plagued public administration for over a century. However, this study emphasizes that experts themselves are not exactly the same. To address the contradiction between research and practice and to improve the role of experts, we need not only to improve the collaboration between experts and practitioners but also to strengthen the collaboration between research-oriented and practice-oriented experts. Using desertification control experiences in 12 counties in northern China as policy examples and through case studies and analysis of a survey of more than 4000 individuals, the study finds that the collaboration with high participation of both research-oriented and practice-oriented experts had the highest governance performance, due to reducing information and knowledge asymmetry, enhancing trust, and strengthening expert participation in public governance. The study also reveals that there are eight institutional design principles that are important for the success of experts’ participation. These principles emphasized knowledge and experts themselves, experts’ relationship with other social actors, and external support (support from laws and regulations and financial support). The study is enlightening to policy makers and public administrators in their endeavor to integrate research (theory) and practice, design public policy, and maximize the use of their knowledge and expertise to advance the cause of public administration.

Keywords

Collaboration institutional design principles practice practice-oriented experts research-oriented experts

Introduction

Since the groundbreaking publication by Wilson (1887), the discipline of public administration (PA) has often been plagued by one “perennial” problem (Reed, 2009: 685): how can we reduce the gap between experts’ research and practitioners’ practice (Buick et al., 2016; Henry, 1975; Martin, 2016; Newland, 2000; Orr and Bennett, 2012)? Experts bring in their knowledge and expertise, but they are often criticized for the disconnection between their research and practice (Radin, 2013; Schön, 1991). Meanwhile, practitioners often highlight that “in the messy swamplands of practice, problems are not often amenable to solutions using law like formulae” (Miller and King, 1998: 46).

Therefore, in the process of the development of PA, many studies have sprung up to solve the disconnection and contradiction between experts’ research (or theory) and practice. For example, some examined the interaction between theory and practice, connectedness between academicians and practitioners, and integration of research and practice, and called for building a reciprocal relationship between theory and practice through dialogue (Christensen et al., 2017; Comfort, 1994; Englehart, 2001; Evans, 2007; Graffy, 2008; Miller and King, 1998; Newland, 2000); some explored the roles of think tank (e.g., Li, 2015; Stone, 2007; Wells, 2012; Zhu and Xue, 2007) and the ways to bridge the gap between knowledge and policy (e.g., Boswell, 2008; Daviter, 2015; Dunlop, 2010, 2014; Dunlop and Radaelli, 2018); some studied knowledge utilization as well as policy uptake and policy utilization (e.g., Albæk, 1995; Dunlop, 2009, 2014; Dunlop et al., 2020; Weiss, 1979); others called for a practitioner perspective, a practical approach, and a practice-oriented theory for PA and claimed that “there is nothing more practical than a good theory” (Radin, 2010: 289; also see Martin, 2016; Streib et al., 2001; Vangen, 2017; Wagenaar, 2004); and still others directly focused on practical wisdom, best practices, and evidence-based practices in PA (Entwistle and Downe 2005; Hall and Jennings Jr., 2008; Holzer and Callahan, 1998; Raadschelders, 2008; Rooney and McKenna, 2008). Furthermore, many scholars also suggested academic–practitioner research collaboration or coproduction as a promising mechanism for making the connection between researchers and practitioners, although they also realized that the research coproduction also raises some dilemmas (Buick et al., 2016; Metzenbaum, 2013; Orr and Bennett, 2012; Ospina and Dodge, 2005).

However, in this study, I want to emphasize that, in fact, experts themselves are not exactly the same. Even from the perspective of research and practice, some experts (research-oriented experts) may be more inclined to research and theory or more concerned with rigor (Buick et al., 2016; Dodge et al., 2005; Radin, 2013), while some (practice-oriented experts) may be more inclined to solve practical social problems or more concerned with relevance (Metzenbaum, 2013; Radin, 2013; Reed, 2009). Some researchers even argue that research is an activity of practitioners or that both scholars and practitioners are researchers (Buick et al., 2016; Schön, 1991). This brings forward a new research topic for us: how does the collaboration between these two types of experts affect the actual results of expert participation in public governance and help us solve the contradiction between research and practice? If this assumption is valid, to improve the role of experts and solve the contradiction between research and practice, we need not only to improve the collaboration between experts and practitioners but also to strengthen the collaboration between these two types of experts. Although some studies have examined various types of knowledge comanagement, coproduction, integration, exchange, and collaboration as well as the collaboration of different experts in various types of public governance, scholars so far have failed to pay attention to how collaboration between the above two types of experts influence their roles in public governance and help us reduce the gap between research and practice (Egeberg, 1994; Newman, 2001; Powell et al., 1996; Wescott, 2009).

Thus, this paper, using desertification control in northern China as an example, studies how the two types of experts collaborated to provide policy consultancy and assisted with policy implementation. The research questions are:

Did the collaboration between research-oriented and practice-oriented experts affect governance results or performance?

What factors influenced experts’ collaborative participation?

Theory, analytical framework, and research hypotheses

Theories and analytical framework

Participation levels of experts and four types of collaboration. Existing literature has suggested that stakeholder (such as experts) participation (Reed, 2008) or involvement is “a key principle of collaboration” (Margerum, 2008). The level of participation refers to an extent that indicates the degree of stakeholders’ engagement (participation) in various governance activities, and it can be deemed as “a spectrum which stretches from no participation at one end” to full joint activities of governance (such as decision-making and actual implementation of concrete programs) at the other, “with intermediate stages between” (Wellens, 1975). Because a regression can only study a relationship between two parameters (Davis and Marquis, 2005), while developing a taxonomy for experts’ collaboration and governance results can help us explore the inner structure of experts’ collaboration and governance arrangements and build blocks for theory and practice in PA (Hill et al., 2012; Moore and Koontz, 2003). Thus, based on the classification of research-oriented experts (mainly focusing on scientific research and studies) and practice-oriented experts (mainly focusing on governance practices) and the division of experts’ participation levels into high and low levels, we can classify expert collaboration into four types: (high participation of research-oriented experts with high participation of practice-oriented experts; shortened to high research–high practice), (low research–low practice), (high research–low practice), (low research–high practice) (Figure 1). Collaboration here refers to when two or more stakeholders co-labor or work together or are formally organized to solve problems that cannot be solved, or “solved easily” (McGuire, 2006: 33), by individuals or single organizations and to accomplish a common goal (Ansell and Gash, 2008; O’Leary et al., 2006).

Figure 1.

Analytical framework.

Four kinds of governance results

To understand the results of expert participatory governance, the first and most important step is to figure out exactly how much experts’ participation there is and what the governance performance is, because we can hypothesize that the participation levels of experts influence governance performance. Thus, based on the division of both experts’ participation and governance performance into high and low levels, we can classify governance results into four kinds: high participation–high performance, high participation–low performance, low participation–high performance, and low participation–low performance (Figure 1).

Factors, institutional design principles, and analytical framework

Expert collaboration itself may be affected by a variety of factors. By a systematic review, I found that the current literature indicates that the roles of experts in PA have often been influenced by eight factors: (1) the level of their scientific knowledge (Streib et al., 2001; Vangen, 2017; Yang, 2019), (2) the application and extension of scientific knowledge (Buick et al., 2016; Campbell, 1992; Coplin et al., 2002; Landry et al., 2003), (3) the relationship among different types of knowledge such as science and local knowledge (Ansell and Gash, 2008; Buick et al., 2016; Graffy, 2008; Orr and Bennett, 2012), (4) the sustainable endeavors of the experts (Buick et al., 2016; Emerson et al., 2012; Graffy, 2008; Landry et al., 2003), (5) their relationships with other social actors (Graffy, 2008; Kotlarsky and Oshri, 2005; Landry et al., 2003; Vangen, 2017), (6) support and guidance by local and central governments (Campbell, 1992; Yang, 2019), (7) external support from laws, and regulations, and contracts (Buick et al., 2016; Campbell, 1992; Yang, 2019), and (8) external financial support (Campbell, 1992; Yang, 2019).

By comparatively analyzing the factors listed above, I found that these eight factors could be divided into three groups: knowledge and the experts themselves, their relationships with other social actors, and external support (such as institutions and financing) (Campbell, 1992). Through analyzing the satisfaction of the above eight factors, I further explored the eight institutional design principles (or key factors and mechanisms) of collaboration among experts (Landry et al., 2003; Ostrom, 1990). According to the theories by Ostrom (1990), institutional design principles here meant essential elements and conditions accounting for the success of the institutions for the participation and collaboration among experts in governance. Corresponding to the eight factors, the eight design principles also successively emphasized (1) high levels of experts’ scientific knowledge, (2) effective application and extension of experts’ scientific knowledge, (3) high cooperation and coordination between experts’ science and local knowledge, (4) high capability and sustained endeavors of experts, (5) effective communication and collaboration among experts and other social actors, (6) reliable and sustained government support, (7) sustained institutional support from laws and regulations, and (8) adequate financial support. Thus, based on the above comparative analysis, the study’s analytical framework is shown in Figure 1.

Research hypotheses

According to the two research questions and the above analytical framework, the two fundamental research hypotheses of this study are:

Collaboration between research-oriented and practice-oriented experts improves governance performance.

Collaborative participation among research-oriented and practice-oriented experts is influenced by eight groups of factors.

Policy area, methods, and data

Policy area

Why desertification control in China

Desertification is one of the great global environmental challenges of our time. Desertification refers to land degradation in arid, semiarid, and dry subhumid areas caused by both climate change and human factors (Goudie, 2009; UN, 1992; Yang, 2019). Previous studies (MEA, 2005) have demonstrated that approximately 41% of the world’s land surface is drylands, which have affected the lives of more than 38% of the world population. Thus, desertification control, combating desertification to reduce land degradation, becomes an important part of PA and governance in many countries (UN, 1992; Yang, 2019). China is among the countries suffering serious desertification in land productivity. Although land degradation remains a serious problem, desertification control efforts through numerous public programs over the 60 years since 1949 have generally alleviated its severity in China, and this is often deemed as a typical case of the success of Chinese PA and governance (Cao et al., 2009; Wang, 2003; Yang, 2019). Furthermore, because of the complexity of desertification control, many expert groups have been invited to participate in desertification control, and there is national government support to solicit expert participation. Recent research also suggests that experts often play important roles in desertification control (Yang, 2019), although many existing studies have emphasized the advantages and disadvantages of Chinese politics and governance structure in desertification control (e.g., Cao et al., 2009; Fan and Zhou, 2001). It is therefore a good policy area for this study.

Research-oriented and practice-oriented experts in desertification control

An expert is often defined as a person who has “a special skill or special knowledge of a subject, gained from training or experience” (Longman Dictionary of Contemporary English (LDCE), 2015). In this study, experts are broadly defined as those who have advantages in knowledge in desertification control, including professors, scholars, researchers, professionals, technicians, intellectual elite, and any stakeholders who have learned knowledge derived from training or experience, compared with other social actors (Yang, 2019).

According to the Chinese system of desertification control, experts in desertification control are affiliated with five organizations: (1) the Chinese Academy of Forestry (CAF), (2) the Chinese Academy of Sciences (CAS), (3) colleges and universities, (4) local antidesertification bases, including local desert control stations, local desert experimental or experimental research stations, and local centers for sand dune fixation and forestry, and (5) academic societies (also called public academic groups in China), including various national, provincial, prefecture, and county organizations and their branches or subsidiary organizations such as China National Sand Control and Desert Industry Society and Chinese Society for Soil and Water Conservation (Yang et al., 2004). Thus, in this study, I also divide experts into the five groups of experts, who are affiliated to the above five organizations.

These five groups of experts can also be divided into two types. The first three groups are research-oriented experts. The experts of the CAS as well as colleges and universities mainly focus on basic research on the drivers, processes, and impact of desertification, while the experts of the CAF devote more time and energy to green projects (mainly focusing on forestry development and ecological improvement). The last two groups of experts are practice-oriented experts. Because antidesertification bases are located in some regions where desertification is serious, by carrying out local experimental tasks and providing local services for desertification control, the experts of these bases often take part in local policymaking and implementation. Meanwhile, because academic societies are a series of spontaneously established nonprofit organizations, the experts of these societies usually function as knowledge propagators, policy advisers, and supervisors of policy implementation.

It is worth noting that some ones who have been involved in desertification control for long time and been very familiar with the practical issues of desertification control as well as some lawyers or policy experts whose purpose is to help or influence the local practice of desertification control can also be deemed as practice-oriented experts, although some of them are not usually called “experts.” However, because these experts have their own formal organizations and are very scattered in desertification control, it is not very easy to contract them and collect data. Thus, they could not been included in the current study.

Site selection

The study included two steps. The first step was based on a field study of 12 counties (Supplemental File Table S1a) in Gansu, Ningxia, and Inner Mongolia provinces in northern China, where desertification and its consequences have been the most severe (Figure 2). According to climate divisions, these selected counties could be divided into two groups. The first group included Linze, Minqin, Zhongwei, Yanchi, and Dengkou, which are located at 99°51'–107°47'E, 36°59'–40°57'N around the Hexi Corridor and are landlocked by desert or severely desertified land, where arid climate is dominant. Another group is located at 108°58'–121°35'E, 38°56'–49°47'N, where the climate is semiarid because it is closer to the east coast of China, and includes Xilinhot, Naiman, Duolun, Wengniute, Aohan, and Xinbaerhuzuo. These two groups provided an overall picture of the desertification situation in northern China.

Figure 2.

The 12 research sites and their jurisdictions.

In the second step of the study, due to the limited financial support and the abundant archives and literature, I chose a comparative document analysis to test the generalizability of the findings from the 12 field study counties. To change coverage and locations, 21 more cases (the unit of analysis is still the “county”) were chosen in eight provinces in northern China with different climates, population densities, annual average temperatures, annual average precipitation, and annual average evaporation. Furthermore, according to climate divisions, these 21 cases could be divided into three groups. The first group included 11 cases, where arid climate is dominant. The second group included six cases, where the climate is semiarid. The third group included four cases, where the climate is extremely arid (Supplemental File Table S1b). Thus, the 12 field study counties and the 21 document analysis cases that comprised this study were representative of all important desertification control efforts in different climates in northern China.

Data acquisition

The first step of the study combined multiple methods including document analyses based on grounded theory (Strauss and Corbin, 1998), semi-structured interviews, observations, and field surveys to collect data, while the second step used only document analyses. Many years of experience studying desertification problems in northern China have proved that this is a hands-on, economic, and scientific multi-method approach for collecting data in rural China considering the fragmentation and incoherency among existing records and the noncomparability among different types of existing records. Previous studies (e.g., Patton, 1987; Poteete et al., 2010; Yang, 2019) have also indicated that these complementary and crosschecked data could form an evidence or data triangle to improve the validity of the study.

Document analysis was conducted prior to surveys, interviews, and observations to set the foundations for these methods as well as afterwards to complement the data from the 12 field study counties in the first step of the study and to test the generalizability of the findings from the field study through the 21 cases of document analysis in the second step. Furthermore, various types of documents, including county annals, ecological annals, laws and regulations, government bulletins, monographs, academic papers, dissertations, theses, news reports, conference summaries, published reviews and comments, and other relevant materials were collected and analyzed.

The process of face-to-face interviews was conducted from June 2006 to October 2011. There were 110 interviewees in total, including local residents (farmers, herders, and citizens), government officials, managers, and experts ranging in age from 20 to 60 (Supplemental File Table S2a), who were interviewed on the problems of the participation and collaboration of different groups of experts in desertification control. In county seats, research assistants and I asked the offices of agencies such as county bureaus and research institutes to recommend the interviewees, while in villages, we found volunteers by ourselves. To complement the survey data, the interview questions in the second stage were consistent with former survey questionnaires, but they were designed in an open-ended way. Furthermore, most interviews were between half an hour and two hours long, and interviewees’ anonymity and confidentiality were strictly protected.

During the same period as the interviews, to obtain more detailed information to understand local issues and verify some problems raised by interviewees, both participatory and nonparticipatory observations were conducted in 52 sites in the 12 counties, including desert control stations, experimental desertification control areas, natural reserves, demonstration plots combating desertification, government bureaus, and other related areas (Supplemental File Table S2b). Depending on necessity and convenience, each observation lasted from approximately 1–8 hours.

Following a sampling strategy that considered the representativeness of samples based on each county’s population (Supplemental File Table S2c), we distributed a total of 5410 questionnaires from March 2011 to December 2011 and ultimately obtained 4406 valid responses from the 12 counties, with a response rate of 81.44% (Supplemental File Table S2d). These responses came from diverse sources, such as farmers, officials, businessmen, and researchers (Supplemental File Table S2e). As some older farmers might lack education and even be illiterate, we randomly distributed the questionnaires to students from different high schools, who were carefully trained to guide their family members, neighbors, and relatives to complete the questionnaires fully and correctly. When respondents forgot (it had been a long time) or did not fully know the related information, they were encouraged to talk to other people. Because all chosen students were from different areas of the same county, this survey could cover almost all the residents of the studied counties, although not all households would have had a child in high school. Moreover, with the help of the local people and high school teachers, we distributed questionnaires in every high school if there was more than one in the county in question, or we chose a high school with students from throughout the county. These procedures were used to account for the sample’s representativeness, and the structure of the data showed that the survey respondents were quite diverse (Supplemental File Table S2e). Furthermore, although the data from the respondents include these people’s perceptions only, previous studies (e.g., Yang, 2019; Yang et al., 2013) have indicated that when a study uses only the percentages of survey respondents’ answers to evaluate related research variables for each county, responses from surveys with large sample size, scientific design, diverse biophysical conditions, and various respondents could be highly consistent with each county’s actual situation of combating desertification.

Variables and measurements

Measurements in the first step of the study

Based on the research questions and the analytical framework discussed above, the major research variables in this study include (1) the participation levels of five groups of experts, (2) the performance of desertification control, and (3) the factors (and institutional design principles) influencing the collaborative participation of the experts. In the first step of the study, I combined the data from document analyses, semi-structured interviews, observations, and field surveys to extract and measure key variables. That is, based on the data from the qualitative methods (semi-structured interviews, observations, and document analyses) and literature reviews, I first extracted the key variables listed in my analytical framework, used the surveys to collect quantitative data (e.g., the percentages of survey respondents’ answers) to measure these variables, and finally used the qualitative data from interviews, observations, and document analyses to complement the quantitative survey data.

Based on the survey data, I used a six-point scale—“very large,” “large,” “medium,” “small,” “very small,” and “unknown”—to evaluate the participation levels of the five groups of experts and desertification control performance. For instance, I asked the survey respondents to give an overall evaluation of the levels of collaborative participation based on their evaluations of the frequency (the number of times participation happened during a particular period such as one year), depth (the number of participants, levels of problems involving participation, and the duration of one occasion of participation), and breadth (the number of problems concerning participation) of the participation of experts in desertification control, and then I used the accumulated percentages of the survey respondents who indicated that the participation level of one group of experts was “very large” and “large” in a county to evaluate the participation level of the group of experts in the county. Meanwhile, I asked the survey respondents to give an overall evaluation of the performance of desertification control based on their evaluations of land amelioration, vegetation increment, and dust storms’ reduction in their counties (Goudie, 2009; UN, 1992; Yang, 2019), and then I used the accumulated percentages of the survey respondents who indicated that desertification control performance was “very large” and “large” in a county to evaluate the desertification control performance of the county.

I used the average participation levels of the experts of the CAF, the CAS, and colleges and universities to evaluate the participation levels of the research-oriented experts, and used the average participation levels of the experts of academic societies and antidesertification bases to evaluate the participation levels of the practice-oriented experts.

Furthermore, the participation levels of both the research-oriented and practice-oriented experts, the participation levels of all experts, and the desertification control performance were also divided into two levels: “high” (if the level of one county was higher than the average level of all 12 counties) and “low” (if the level of one county was lower than the average level of all 12 counties). Based on the analytical framework, I then analyzed the types of expert collaboration and the kinds of governance results.

Because some major factors influencing experts’ participation and collaboration might include several sub-factors, I designed 16 problems (including one choice for “other”) in total for the survey to evaluate them (see Supplemental File Table S4). For example, to evaluate the “endeavors of experts” factor, I evaluated the “experts’ inadequate knowledge of local conditions” problem and the “experts’ sabotage” problem. For each of these 16 problems, I used a six-point scale—“very large,” “large,” “medium,” “small,” “very small,” and “unknown”—for evaluation purposes.

Measurements in the second step of the study

I used the qualitative data from document analyses to test the generalizability of the findings from the first step. Based on synthetically analyzing the frequency, depth, and breadth (as defined above) of expert participation of each case, the overall participation levels of the five groups of experts were divided into two levels: “high” (if the frequency of one case was higher than the average frequency of all the 21 document analysis cases) and “low” (if the frequency of one case was lower than the average frequency). Based on the evaluation of the participation levels (“high” and “low”) of research-oriented experts and practice-oriented experts, I finally determined the types of collaboration among experts of each case. For the eight institutional design principles, if the data indicated that the principles were satisfied and based on the differences between the support and comparative analysis, I divided their satisfaction in to three levels: “high,” “medium,” and “low”. If the data showed that the principles were not satisfied, they were coded “no.” If I could not find enough data to decide whether the principles were satisfied, they were coded “no data.” Finally, through synthetically analyzing levels of land amelioration, vegetation increment, and dust storms’ reduction as the results of desertification control based on the collected data, I divided the performance of desertification control into three types. If the collaboration among experts was significantly correlated with the performance, it was coded “successful”; if the collaboration was only partly correlated with the performance, it was coded “semi-successful”; and if collaboration was not correlated with the performance, it was coded “unsuccessful.” Furthermore, to avoid subjectivity in coding results and personal errors, the variables were first jointly coded by three research assistants and they were then independently rechecked by the author. If the checked result by the author was different from the result coded by the three research assistants, the result was jointly recoded by the author and the research assistants. To avoid the influence of prior knowledge of the research hypotheses, the three research assistants were blind to the research purpose, questions, and hypotheses during the coding process.

Data analysis and validity

The reliability analysis using SPSS (Statistical Product and Service Solutions) Statistics V17.0 showed that Cronbach’s Alpha for the survey data in the first step of the study and for the data of the document analysis in the second step was both over 0.97 (number of items was 18). This indicated that the reliability of both survey and document data was relatively high. Furthermore, survey respondents’ anonymity and confidentiality were strictly protected; the items in the survey were specific, clear, and easily understood by respondents; the evaluations of experts’ participation and the performance were placed in two different parts of the questionnaire to prevent respondents from linking the two groups of questions; and multiple methods were combined to reduce the threat from common method bias, along with other adopted remedies (Jakobsen and Jensen, 2015; Podsakoff et al., 2012). All these helped us reduce common method bias of the survey data. Thus, although this study is not free from common method bias, the bias may not pose a serious threat to the validity of the study results.

Results

Participation of experts and four kinds of governance results

Among the six choices (from “very large” to “don’t know”), on average, over 20% of the survey respondents indicated that the participation of all five groups of experts was “very large” and “large” in the 12 counties; only the participation of the experts of the CAF was over 30%, ranking as the most important participation of the five, and the experts of colleges and universities ranked last (Table 1a). However, the order of the correlations of the levels of participation with desertification control performance was quite different from the order of their levels of participation. Among the five groups, the experts of academic societies had the highest and most significant correlation coefficients, although they were only deemed third according to their levels of participation. Meanwhile, although the experts of the CAF had the highest level of participation, they only ranked fourth according to the correlation with desertification control performance (Table 2). Furthermore, the experts of antidesertification bases had the second highest correlation with desertification control performance, even though their levels of participation only ranked fourth. Only the experts of colleges and universities had the same rank (fifth) for their levels of participation and their correlation with desertification control performance, which was not very significant.

Table 1.

Governance results and types of collaboration based on experts’ participation and desertification control performance as rated by survey respondents (n = 4406) in the 12 counties in northern China (2011).

	Average(%)	Linze(%)	Zhongwei(%)	Dengkou(%)	Xinbaerhuzuo(%)	Aohan(%)	Ejin Horo(%)	Xilinhot(%)	Minqin(%)	Yanchi(%)	Naiman(%)	Duolun(%)	Wengniute(%)
a. Governance results
Research-oriented experts
(1) Experts of CAF	30.3	34.6	45.3	34.0	37.2	32.1	32.4	26.7	23.3	27.9	12.3	29.1	28.3
(2) Experts of CAS	28.6	31.5	45.3	32.2	37.1	28.7	24.8	25.6	25.4	27.1	12.6	28.1	24.2
(3) Experts of colleges and universities	22.9	18.4	35.7	24.3	31.7	30.7	32.6	21.8	14.9	23.4	10.4	20.7	10.6
Practice-oriented experts
(4) Experts of academic societies	27.8	33.6	41.8	28.4	39.9	27.0	27.3	31.9	19.9	27.2	14.4	29.0	13.6
(5) Experts of antidesertification bases	27.2	34.0	37.6	32.4	32.7	30.1	27.2	29.7	18.2	25.1	12.6	25.5	21.4
Average participation	27.4	30.4	41.1	30.3	35.7	29.7	28.9	27.1	20.3	26.1	12.5	26.5	19.6
Relative levels		H	H	H	H	H	H	L	L	L	L	L	L
Desertification control performance	30.5	46.2	45.7	27.3	55.2	18.4	28.0	29.6	14.3	28.9	20.8	34.3	17.1
Relative levels		H	H	L	H	L	L	L	L	L	L	H	L
Four kinds of governance result		I^a	I	II	I	II	II	IV	IV	IV	IV	III	IV
b. Types of collaboration
Average of research-oriented experts	27.3	28.2	42.1	30.2	35.3	30.5	29.9	24.7	21.2	26.1	11.8	26.0	21.0
Relative levels		H	H	H	H	H	H	L	L	L	L	L	L
Average of practice-oriented experts	27.5	33.8	39.7	30.4	36.3	28.6	27.3	30.8	19.1	26.2	13.5	27.3	17.5
Relative levels		H	H	H	H	H	L	H	L	L	L	L	L
Four types of collaboration		I^b	I	I	I	I	II	III	IV	IV	IV	IV	IV
Average performance of types I–IV				38.6 [1]^c			28[3]	29.6[2]			23.1 [4]

^aKind I (high participation–high performance), Kind II (high participation–low performance), Kind III (low participation–high performance), and Kind IV (low participation–low performance).

^bType I (high research–high practice), Type II (high research–low practice), Type III (low research–high practice), and Type IV (low research–low practice).

^c[1]–[4] refers to the order of performance from high to low.

Table 2.

Correlation coefficients of research-oriented and practice-oriented experts with desertification control performance (Pearson’s).

	Desertification control performance
Groups of experts	Coefficients	Sig.
Research-oriented experts
(1) Experts of CAF	.660*^a [4]^b	0.020
(2) Experts of CAS	.700* [3]	0.011
(5) Experts of colleges and universities	.523 [5]	0.081
Practice-oriented experts
(3) Experts of academic societies	.863** [1]	0.000
(4) Experts of antidesertification bases	.707* [2]	0.010
Average	.691*	0.024

^a*P < 0.05 (two-tailed); **P < 0.01(two-tailed).

^b[1]–[5] refers to the rank.

Based on the experts’ average levels of participation and the performance of desertification control, as rated by the survey respondents in the 12 counties, governance results by experts were divided into four kinds (Table 1a): Kind (high participation–high performance), Kind (high participation–low performance), Kind (low participation–high performance), and Kind (low participation–low performance).

Types of collaboration between research-oriented and practice-oriented experts

Based on the classifications for research orientation and practice orientation and their average levels of participation, as rated by the survey respondents, the study indicated that all 12 counties could be divided into four types of collaboration (Table 1b): Type (high research–high practice), Type (low research–low practice), Type (high research–low practice), and Type (low research–high practice). The study also indicated that Type (high research–high practice) had the highest average desertification control performance; Type (low research–low practice) had the lowest; and Type (high research–low practice) and Type (low research–high practice) fell in the middle.

The document analysis results also indicated that the 21 cases could be divided into the four types of collaboration (Supplemental File Table S3). The results also showed that Type (high research–high practice) had the highest average desertification control performance; Type (low research–high practice) had the second highest average; Type (high research–low practice) had the third highest average; and Type (low research–low practice) had the lowest average (Table 3).

Table 3.

Types of collaboration among experts and desertification control performance of the 21 cases of document analysis in northern China.

Performance	Number of counties
Performance	High research–high practice	High research–low practice	Low research–high practice	Low research–low practice
Successful	5	2	1	2
Semi-successful	1	1	1	4
Unsuccessful	1	1	0	2
Average values^a	2.57[1]^b	2.25[3]	2.5[2]	2[4]

^aThe average values were calculated by giving 3, 2, and 1 to “successful,” “semi-successful,” and “unsuccessful.”

^b[1]–[4] refers to the order of performance from high to low.

Factors influencing experts’ roles and institutional design principles for successful collaboration

Based on the survey respondents’ evaluations (“very large” and “large”), the results indicated that the Chi-square values of the factors influencing the roles of experts in desertification control were relatively high and significant (Supplemental File Table S4). Thus, based on the factors rated by the survey respondents, I used eight design principles to explain the successful expert participation and collaboration in desertification control (Table 4). The document analysis results of the 21 cases also indicated that the more these principles were satisfied, the higher desertification control performance was (Table 3). This suggested that the eight principles developed from the study in the 12 counties were applicable in the 21 document analysis cases.

Table 4.

Eight design principles for the successful participation and collaboration among experts in desertification control and their correlation coefficients (Spearman’s) with desertification control performance in the 21 cases of document analysis (2014).

Eight design principles	Coefficients(significance)
Group 1: Knowledge and the experts themselves
P1^a. Adequate development and continued improvement of science	0.748**^b (0.000)
P2. The effective refinement, transformation, adaption, application, and extension of science	0.799** (0.000)
P3. High cooperation and coordination between science and local knowledge	0.773** (0.000)
P4. High capability and sustained endeavors of experts in the application and extension of science	0.735** (0.000)
Group 2: Relationships with other social actors
P5. Effective communication and collaboration among experts and other social actors; enough support from other social actors; and continued improvement in their attention, understanding of science, and capabilities	0.643** (0.002)
P6. Reliable and sustained support and guidance by governments at different hierarchical levels	0.726** (0.000)
Group 3: Reliable and sustainable external support
P7. Sustained institutional support from laws and regulations	0.599** (0.004)
P8. Adequate financial support	0.554* (0.011)

^aP1–P8 refer to Principles 1–8.

^b*P < 0.05 (two-tailed); **P < 0.01(two-tailed).

Discussion

The relatively high percentages indicated that all five groups of experts significantly participated in desertification control, but the different order of the correlation coefficients of the five expert groups with desertification control performance and the four kinds of governance results showed that high expert participation did not necessarily lead to high desertification control performance. These indicated that, in addition to the level of participation, other factors (such as types of collaboration and institutional design principles) influenced the roles of experts.

Four types of expert collaboration, the collaboration type with the highest performance, and the reasons of high performance

Although existing literature has emphasized the collaboration of different experts in public governance (e.g., Orr and Bennett, 2012) and especially in desertification control (Yang, 2019), the typology of expert collaboration has not been sufficiently studied. The findings on collaboration type suggested that, based on the participation levels of research- and practice-oriented experts, we can divide the expert collaboration into four fundamental types: (high research–high practice), (high research–low practice), (low research–high practice), and (low research–low practice). These types are not only theoretically reasonable, as shown in the analytical framework, but they also correspond with the actual situation, as shown in the 12 counties in northern China. For instance, in Zhongwei, known as the “capital of deserts” (Yang 2019), both the research-oriented experts (the experts of the CAF, the CAS, and colleges and universities) and the practice-oriented experts (the experts of academic societies and antidesertification bases) highly participated in desertification control, and the performance of desertification control was also high; while in Minqin, Yanchi, Naiman, Duolun, and Wengniute, the participation of both the research- and practice-oriented experts was low, so their performance was also low. Thus, this classification provides us not only with a useful tool to theoretically study the collaboration of different groups of experts but also with a map for policymakers and executors to design and improve expert collaboration in practice, especially for some social activities involving various types of experts. For example, researchers could use this classification to study expert collaboration in other types of environmental and public affairs such as grassland degradation, shortage of water resources, and environmental pollution; policymakers and executors could use this classification as a concrete instruction to design or amend expert collaboration in these types of affairs.

There might be three reasons why the collaboration between the two types of experts is effective for better governance. First, the collaboration between the two types of experts reduces information and knowledge asymmetry and improves the collaboration among different types of knowledge, especially between local or practical knowledge and scientific knowledge, and then improves the function of knowledge in public governance. For example, the collaboration between the practice-oriented experts from the Comprehensive Desert Control Experimental Station in Minqin and the research-oriented experts from the Cold and Arid Regions Environment and Engineering Research Institute (CAREERI) of CAS not only improves the application of new knowledge and technology in desertification control but also extend the use of straw checkerboard barriers innovated by local people in desertification control in Minqin. Second, the collaboration between the two types of experts enhances the trust between government officials and experts as well as between farmers and experts, which improves the collaboration among officials, experts, and farmers and then improves the role of experts in desertification control. Because many practice-oriented experts are often local experts who are usually highly trusted by both officials and farmers, they are more likely to develop a trust chain (Yang, 2018) between government officials and research-oriented experts who come from outside of the counties such as Minqin, Zhongwei, and Naiman as well as between these research-oriented experts and local farmers as a trust bridge. Third, the collaboration between the two types of experts strengthens the participation of experts in desertification control. For instance, Linze have two county-owned stations of desertification control: one is Linize Experimental Station of Desertification Control, which cooperates with CAS; another is Linze Inland River Basin Comprehensive Research Station, which also works together with CAS and its CAREERI. Thus, the head of Linze Experimental Station of Desertification Control is also selected to be the vice head of the county in charge of ecological protection, because of the contribution of the station and its collaboration with CAS. This arrangement significantly strengthens the actual participation of experts in desertification control, because we all know that Chinese society is an “official standard” society. Certainly, there might be other reasons, but these three ones might be the most important, and all of them should be further studied in the future.

Eight institutional design principles for the participation and collaboration of experts

The study indicated that experts’ collaborative participation was influenced not only by experts themselves and their knowledge levels, capabilities, and sustained endeavors, as emphasized in previous research (Buick et al., 2016; Graffy, 2008; Streib et al., 2001; Vangen, 2017; Yang, 2019) but also by their relationships with other social actors, especially with local people, local and central governments (Bunker, 1978; Cairl and Gallagher, 1968; Denny, 1967; Graffy, 2008; Lambright, 2008; Vangen, 2017), and some external institutional and financial support (Campbell, 1992; Yang, 2019). Collaboration among different scientific groups for improving the participation performance of these groups in various types of public governance has been emphasized by numerous studies (e.g., Newman, 2001; Powell et al., 1996). Buick et al. (2016: 45) proposed a set of lessons for practitioners and scholars, both as researchers embarking on partnerships, and noted that “there will need to be enough time to undertake rigorous research and implement findings.” Amabile et al. (2001) even argued that successful cooperative projects might share three characteristics—project-relevant skills, collaboration skills, and positive attitudes and motivation. This study emphasized not only knowledge and the experts themselves but also the importance of experts’ relationships with other social actors (including local and central governments) and institutional (laws and regulations) and financial support. Thus, to improve collaboration among experts and practitioners, more attention should be paid to the exogenous variables in addition to the endogenous variables (Agranoff, 2005), such as experts themselves (including their knowledge, motivations, capabilities, and collaborative skills). In particular, their relationships with other social actors (including governments) could not only reduce the obstacles of the experts’ internal collaboration but also improve their internal collaboration because of the bridge built on collaboration and trust (Vangen and Huxham, 2003). For example, if two groups of experts trust and have a good collaborative relationship with the government but have no trust or collaboration between themselves, then the experts could build trust and a good relationship through their trust in and collaboration with the government (Kotlarsky and Oshri, 2005). Furthermore, laws and regulations provide sustainable institutional support for experts’ collaboration, while financial support provides adequate resources for experts’ participation and collaboration. For example, many interviewees indicated that unstable and contradictory institutions as well as limited and discontinuous financial support were very important obstacles to improving experts’ participation and collaboration.

Implications for PA

The readers can find consistencies between the above findings and one of the primary themes in PA, the relationship between research and practice, or the relevance of PA (Perry, 2013, 2015). Ostrom (1980: 309) pointed out that “scholarship is concerned with the development of theory as basic knowledge and practitioners are primarily concerned with applications: theory comes first and applications derive from theory.” Graffy (2008: 1099) even argued that “practice without theory leads to ad hoc action, and theory without any connection to practice may fail to be relevant.” PA as an independent academic discipline, however, is also facing the serious conflict between theory and practice. As the researchers or professionals in the field of PA, we often complain about the limited influence of our academic findings and theories on public policy making, public policy implementation, and other practices of PA. Thus, Coplin et al. (2002: 699) suggested that, to resolve the old conflict between theory and practice, professional researchers need to function as “change agents,” “using a variety of strategies to gain acceptance and understanding of the strengths and limitations of performance measurement.” This study, however, suggests that in addition to changing the behaviors of professional researchers, we need to have both our research-oriented and practice-oriented experts and develop both our research-oriented and practice-oriented organizations. Ospina and Dodge (2005: 409) identified “three research roles for practitioners: as sources of knowledge, as producers of knowledge, and as active consumers who inform the research process.” This study showed that practice-oriented experts can not only play these three research roles to help us “create useful knowledge of high quality” (Ospina and Dodge, 2005: 409) but also help us better influence public policy making, implementation, and results of public governance and reduce the gap between research and practice.

In the field of PA, although many societies or associations about PA have been developed for many years, and some associations together with their journals, such as the American Society for Public Administration (ASPA) and the Public Administration Review as well as the UK Joint University Council (JUC) Public Administration Committee (PAC) and its journal Public Policy and Administration, have also addressed theoretical and practical significance simultaneously and “sought to link practitioners and academicians across subfields and varied levels of activities” (Newland, 2000: 20), most of our societies or associations are still too academic to function as practice-oriented organizations focused on social services and PA knowledge application and extension in practice. That is, the following series of improvements must also be undertaken. First, we should pay much attention to improving the collaboration between our research-oriented and practice-oriented experts and organizations. For example, the required section of Evidence for Practice of Public Administration Review is a highly commendable endeavor not only to bridge the research-practice gap but also to improve the collaboration between research-oriented and practice oriented experts, professors, researchers, and organizations. Second, if possible, many practice-oriented societies and associations should be developed in the field of PA; or some of our existing academic or professional societies or associations should also develop more practice-oriented features and functions. For example, in addition to the creation of new academic or professional associations, more associations involving both professionals and practitioners should be sustainably developed and collaboratively managed. Gazley (2014: 736) also pointed out that “governance practices are shaped by many forces but that public employees do indeed carry their public values into the associations they join, and these values, in turn, are positively related to board behavior.” Third, it is worth pointing out that although some studies have realized the important role of our professional associations or societies in PA, scholars have also highlighted “the dearth of empirical research on associations” (Gazley, 2014: 736). Thus, the findings of this study can not only contribute to narrow the old gap between theory and practice in PA or improve the relevance of PA (Perry, 2013) through their policy implications but also be a new starting point to call for more fresh studies on PA experts, their organizations, their structural relationships, and their collaboration.

Conclusions

Existing literature has studied the important roles of experts, knowledge utilization, and collaboration between research and practice as well as between experts and practitioners in public policy making and implementation, but the types of collaboration between research-oriented and practice-oriented experts in PA are essentially neglected and have received little attention in the mainstream discourse concerning contemporary public governance. By studying the roles of experts in desertification control in China, this study indicated that there were four kinds of results of expert participatory governance defined by high or low participation and governance performance; high participation did not necessarily lead to high performance, and of the four types of collaboration among experts, the type of high participation of both research-oriented and practice-oriented experts had the highest governance performance, while the type of low participation of both research-oriented and practice-oriented experts had the lowest. Furthermore, although existing literature has emphasized different factors influencing the roles of experts in public governance, the institutions for successful collaboration among experts have not been sufficiently explored. The study found that successful collaboration among experts might share eight institutional design principles. Institutionalizing the rules to ensure the presence of these factors should be meaningful for successful expert participation in policy making and governance practice.

In summary, the study is enlightening to public administrators in their endeavor to integrate research (or theory) and practice, design public policy, and maximize the use of their knowledge and expertise to advance the cause of PA. Furthermore, these findings served as a foundation for further study regarding the roles of experts in public governance and an empirical reference for promoting the global mechanisms of expert participatory governance. Researchers might also use them as theoretical references to design new research exploring collaboration among experts in other types of public governance in other countries. However, the aforementioned eight principles are still quite speculative and not a “panacea,” and more work is definitely needed to further explain the causal mechanisms of each of these principles and test their generalizability and repeatability for other problems and other countries using more data in the future.

Supplemental Material

sj-pdf-1-ppa-10.1177_0952076720958468 - Developing collaboration between research-oriented and practice-oriented experts in public administration: How does expert participation make a difference in public policy making and governance practice?

Supplemental material, sj-pdf-1-ppa-10.1177_0952076720958468 for Developing collaboration between research-oriented and practice-oriented experts in public administration: How does expert participation make a difference in public policy making and governance practice? by Lihua Yang in Public Policy and Administration

Footnotes

Acknowledgments

The author would like to thank the late Professors Elinor Ostrom and Vincent Ostrom, Professor Zhiyong Lan, and Professor Zhiren Zhou for their comments on and suggestions for this study. In particular, the author would like to thank Ms. Yifan Chen for her contribution to data collection and analysis. Furthermore, special thanks go to the anonymous reviewers and the editor whose suggestions and comments have significantly improved the article.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Key Project of the National Social Science Fund of China (14ZDB143) and the Key Project of the National Social Science Fund of China (18VZL001).

ORCID iD

Lihua Yang

Supplemental material

Supplemental material for this article is available online.

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