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Campus spatial organization has potential impact on the multidisciplinary innovation (MDI). This research aims to establish an evaluation theory and a method for MDI adaptability of campus spatial organization. Based on the literature review, it first proposes theoretical assumptions regarding the correlation among MDI abilities, social networks, and spatiotemporal behaviors of individuals on campus. Then, data on the three aspects mentioned above was collected through online survey which was conducted from February 4th to March 15th, 2020. A total of 524 valid responses were obtained from 572 undergraduates and graduates of various majors in Zijingang Campus (East) of Zhejiang University. The correlation mechanism of these aspects was empirically analyzed by structural equation model (SEM). By integrating individual data, the research generated three overall networks whose nodes are linked according to spatial proximity, behavior routine, and disciplinary connection, respectively. Furthermore, an evaluation method is proposed for MDI adaptability of the campus spatial organization, which is based on the comparison of the structural similarity of the nodes in different networks. Accordingly, the evaluation results were validated by expert ratings and user satisfaction. Finally, design strategies for the renovation of an innovative campus are discussed. Keywords multidisciplinary innovation, campus, adaptability, social network, behavioral characteristics, spatial layout information sharing, R&D cooperation, and technological Introduction complementarity (Jokisaari & Vuori, 2014; Lombardi et al., Multidisciplinary innovation (MDI) indicates knowledge 2020; Yu et al., 2020). Innovation networks are formed through creation and dissemination activities involving two or more communicative activities (Iturrioz et al., 2015); Besides, indi- disciplines that go beyond a single subject boundary. which vidual behavior has certain spatiotemporal characteristics. In has become an important way to enhance the innovation abil- different spatial layout patterns, there are differences in the ity of universities (Hero & Lindfors, 2019; Leahey & frequency and degrees of group communicative activities, Barringer, 2020; Turner et al., 2015). Nevertheless the cor- which then leads to changes in the composition of social net- responding campus design theory is obviously lagging works of individuals or their groups (Doreian & Conti, 2012; behind, and scientific basis is insufficient for the design strat- Mousavinia et al., 2019). Therefore, the society, the behavior, egies for the innovative campus (Magdaniel et al., 2018; Qiu, and the space on campus are interconnected. The analysis of 2014; Taylor, 2010). One of the key issues that need to be scientifically recognized is whether or how the organization of functional blocks on the campus could affect MDI—that Zhejiang University, Hangzhou, China is, the spatial organization design of the innovative campus. South China University of Technology, Guangzhou, China The difficulty in understanding this issue is that innovation College of London, UK activities cannot be directly related to space design. It needs to Polytechnic University of Milan, Italy be observed through the perspective of space users and should Corresponding Author: also include social and behavioral aspects. Innovative social Jindong Wu, College of Civil Engineering and Architecture, Zhejiang networks are not only a measure of a group’s ability to inno- University, Room 113, Yueya Building, No. 866 Yuhangtang Road, vate (Desmarchelier et al., 2020; Gewehr et al., 2017), but are Hangzhou, Zhejiang 310058, China. also the basis of social relationships among individuals for Email: firstname.lastname@example.org Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 SAGE Open Figure 1. Interaction of innovative society, behavior, and space on campus from the network perspective. the relationship among the three elements is a prerequisite for (2) On the basis of network structure similarity, propose exploring the space layout of an innovative campus. and verify an MDI adaptability evaluation method Concerning the innovative campus design, previous stud- for campus spatial organization. ies have mainly focused on four aspects. Some researchers (Coulson et al., 2018; Hoeger, 2007; Qiu, 2014; Taylor, 2010) Through the literature review, the study established the have analyzed and summarized design strategies based on theoretical framework of linking MDI performance and case studies. Other researchers have discussed the use of campus space layouts, and it proposed the hypotheses of “diagnosis” and “coordination” to create an “organic order” the correlation among MDI ability, social networks, and for campus planning based on built environment evaluation behavioral characteristics of individuals on campus. Next, (e.g., Alexander, 1975; ASSHE, 2019; Eckert, 2014). From with an online questionnaire survey, relevant data of the perspective of the social network, Sailer et al. showed that respondents in the Zijingang campus were collected. spatial layout is an important factor in forming interpersonal Subsequently, the hypotheses were verified and analyzed relationships (Sailer, 2011; Sailer & McCulloh, 2012), by the structural equation model (SEM). Based on the whereas Wineman et al. (2009) analyzed the influence mech- superposition of individual data, three overall campus net- anism of spatial layout on social network structure and inno- works of space organization, behaviors, and disciplinary vation performance in innovative organizations. Furthermore, connections were established and analyzed. Furthermore, studies on environmental behaviors have summarized the based on the structural similarities of nodes among differ- environmental requirements compatible with the behavioral ent networks, we proposed an evaluation method for the characteristics of collaborative innovation (e.g., Kamruzzaman MDI adaptability of campus spatial organization. Finally, et al., 2011; Li, 2016). Given the advantages of these studies, the evaluation results of the Zijingang campus were veri- the following deficiencies were identified and could hardly be fied by expert scoring and user satisfaction. ignored. Firstly, there is a lack of in-depth and focused research on the association mechanism of innovative space Literature and social community at mesoscale. Secondly, the social net- work is ignored as the basis for MDI activities. Thirdly, more Theoretical Framework quantitative analysis should be carried out in balance with much qualitative analysis which has been conducted in Research on campus MDI adaptability requires two inter- previous studies. Fourthly, the conclusions of empirical mediary factors to form a complete logical chain—namely, research are not well translated into design methods, tools, or social networks and behavioral features. Their interaction strategies. is reflected in two interrelated levels (Figure 1). For indi- Thus, the aims of this research are as follows: viduals, interpersonal relationships need to be developed in various types of communication activities. Furthermore, (1) T o analyze the relationship between MDI and the MDI behavior is affected by established social relation- campus spatial organization. ships. These two aspects are mutually causal. In addition, (2) To propose a method to evaluate the MDI adaptabil- the quality and the scale of an innovative social network ity of university campuses. can determine individuals’ innovation ability. From a global perspective, the overall social networks and behaviors on The innovation and contributions of this research are as campus are composed of individual social relationships and follows: behavioral features. The general spatial organization of a campus may restrict or promote the overall social network (1) Establish a theoretical framework which links MDI development and innovation capability by affecting the and campus spatial organization by integrating the behavioral features of different disciplinary groups on the individual and overall networks perspectives. campus. Xia et al. 3 et al. (2016) found that the presence of network size, network Individuals’ MDI Ability, MDI Network, and tie-strength, and network centrality determined the level of Spatiotemporal Behavior knowledge transfer performance. Bai et al. (2021) indicated MDI ability. MDI ability refers to the comprehensive qual- that establishing a long-term fixed relationship with some- ity of a person to engage in interdisciplinary research activi- one more competitive was the basis for interdisciplinary ties (Lattuca et al., 2013; Zhang et al., 2020;). This ability innovation activities. is mainly reflected via two aspects—as achievements and potential (Hero et al., 2021). For individuals (students and H3: The scale of the individual multi-disciplinary social researchers) on campus, MDI achievements include pub- network improves the level of one’s MDI potential. lished papers, and participation in scientific research proj- H4: The quality of the individual multi-disciplinary social ects, and involvements in activities of different disciplines. network improves the level of one’s MDI potential. The potential of MDI can be examined in terms of innova- tion desire, enthusiasm, or motivation (Hunter et al., 2011). Similarly, people with more interdisciplinary collaborators, According to Claus and Wiese (2019), interdisciplinary work owing to better support, generally have better innovations offers both innovative potential and challenges in collabora- and higher quality of innovations than people with fewer tion. Thus, the performance of interdisciplinary innovation interdisciplinary collaborators (Assimakopoulos, 2007; is considered to have a positive effect on one’s potential for Stark et al., 2020). If people carry on long-term scientific MDI. research cooperation with many collaborators at a level higher than themselves, their level of achievements is H1: Individual MDI achievements promote one’s MDI improved further (Bai et al., 2021; Rafols & Meyer, 2010 ). potential. Through an empirical study, Cho et al. (2007) demonstrated that the social network considerably influences the learners’ MDI network. MDI network refers to a person’s interper- performance in collaborative learning. The studies of Stark sonal relationship basis for interdisciplinary research, which et al. (2020), Yan et al. (2018), and Shu et al. (2012) showed is mainly manifested in two aspects: the scale and quality that good social relationships can be conducive to efficient of the network (Rafols & Meyer, 2010; Zhang et al., 2020). output both in terms of interdisciplinary innovative individu- A study by Zhang et al. (2020) indicated that researchers als and collaborative innovative enterprises. Hero and with more co-authors in different fields tend to have a higher Lindfors (2019) believed that innovation was promoted by centrality in multidisciplinary collaborative network. For teamwork, multidisciplinary collaboration and external part- individuals, the scale of their MDI network, that is, the num- nerships. Zhang et al. (2020) argued that a scientific entity’s ber of network nodes, includes the number of existing and status of co-author network could reflect its significance, potential research collaborators, whereas the quality of the capacity of knowledge integration, and knowledge network can be measured by the ratio of high-level and long- diffusion. term collaborators as well as the overall benefits derived from the network (Assimakopoulos, 2007). When one has H5: The scale of the individual multi-disciplinary social more interdisciplinary relations, his or her network is more network improves the level of one’s MDI achievements. likely to evolve into a high quality one (Rafols & Meyer, H6: The quality of the individual multi-disciplinary social 2010). Numerous studies (e.g., Castilla et al., 2020; Slotte– network improves the level of one’s MDI achievements. Kock & Coviello, 2010;) have shown that the quantity and the quality of MDI network nodes are highly correlated. Spatial and temporal behavior. The temporal and spatial characteristics of human behavior are correlated, which may H2: The scale and quality of individual multi-disciplinary also be involved in innovative activities. For example, the social networks are positively related. studies of Farber et al. (2014) and Jiron and Carrasco (2020) argued that the longer an individuals’ social activity time Relationship between MDI ability and the network. Per- was, the more spatial points that they could reach, and the sons with more interdisciplinary collaborators have a higher more diverse spatial types that these activities could reach. potential for innovation than those with fewer interdisciplin- In most universities, the division of space is based on dis- ary collaborators (Assimakopoulos, 2007). Through inter- ciplines, and different faculties are set up in relatively inde- disciplinary collaborators, people gain more information, pendent spaces. Thus, MDI activities take place in different beliefs, and support (Castilla et al., 2020), have a better time and space. Different discipline spaces provide different research atmosphere, and consequently are more enthusiastic equipment and information to stimulate innovative activities about innovation. Accordingly, they will consider MDI to be and people’s network connections (Qiu, 2014). Within a cer- of more value and feasibility (Cho et al., 2007). In particu- tain time and space, the temporal characteristics of behavior lar, when the quality of MDI network nodes is higher, this can be expressed by total time length, frequency, and uni- tendency is more distinctive (Rafols & Meyer, 2010). Xie formity, whereas the spatial characteristics of behavior can 4 SAGE Open be expressed by the total amount of space reached, num- means, and travel duration to the meeting. By investigating ber of types, and uniformity of spatial distribution (Jiron & the campus space users, Yun et al. (2018) argued that the pos- Carrasco, 2020; Zhang & Thill, 2017). sibility of open innovation creativity based on tacit knowl- edge arose when meeting and communicating with others. H7: The intensity of individual spatial and temporal He held the opinion that it was possible to activate open behavior of MDI are positively related. innovation on campus with good use of space design. On a campus, MDI activity allows for the formation of quality ties Relationship between MDI network and spatiotemporal with senior students or professors. If it is a case of spontane- behavior. Li and Lin (2018) confirmed the relationship ously seeking to form an interdisciplinary cooperation net- between human social network and spatiotemporal behavior work, instead of activities organized by the administration patterns through simulation research. Previous studies (e.g., (e.g., an interdisciplinary course program), these high-level Doreian & Conti, 2012; Jiron & Carrasco, 2020; Wineman collaborators will often not appear due to their own initia- et al., 2009; Zhang & Thill, 2017) have also confirmed a cor- tive, but they can only be encountered on varied occasions at responding relationship between the spatial distribution of different places. In contrast, having more important figures people’s activities and their social network. On a university in the network motivates an individual to spend time and campus, interdisciplinary partnerships usually need to be energy to keep in touch with them or to develop relationships generated and developed through face-to-face interactions with new high-quality collaborators (Moore et al., 2013). (Farber et al., 2014; Qiu, 2014). Winks et al. (2020) stated that spaces on campus were made social through their various H10: The quality of the individual multi-disciplinary uses, and many spaces required activating through the pro- social network and intensity of individual MDI spatial motion of activities conducive to peer support and network- behavior are positively related. ing. Magdaniel et al. (2018) argued that the development of H11: The quality of the individual multi-disciplinary physical infrastructure connecting the different functions social network and intensity of individual MDI temporal on campus allows opportunities to have more and diverse behavior are positively related. people with chances for interaction and sharing knowledge. Hence, the more interpersonal activities a person participates Relationship Between the Overall Networks of in, the more friends they are likely to make (Chang & Zhu, Social, Route, and Space 2011). People with different subject backgrounds on cam- pus tend to study and socialize in certain areas. To exchange Previous studies on space layouts (e.g., Burger et al., 2014; information and establish contact with people from distinct He, 2014; Suckley & Dobson, 2014) have demonstrated that disciplines, one needs to a frequent visitor to these places to spatial nodes, such as cities, urban blocks, and rooms, can be have opportunities to increase the number of potential col- connected in different ways, such as proximity, transporta- laborators in the exchange. Therefore, the characteristics of tion, and social relations to form different network struc- individual MDI networks on campus will also be reflected tures. The network analysis method allows for the comparison in the time and space distribution patterns of participating of the characteristics of the overall structure of different net- in MDI activities. Conversely, when a person has more net- works and the structural characteristics of the same node in worked friends, to maintain these cooperative relationships, different networks (Everett, 2002). Since the behavioral the time and space intensity of participating in interdisciplin- activities and social relations as node connections are typi- ary activities will be more and higher. cally based on the statistics of the group sum, the overall network obtained based on the above method can reflect cer- H8: The scale of the individual multi-disciplinary social tain characteristics of the group in the spatial dimension (Liu network and intensity of individual MDI spatial behavior & Derudder, 2013; Sevtsuk & Kalvo, 2018). Thus, networks are positively related. analysis can bring a new paradigm to research on innovative H9: The scale of the individual multi-disciplinary social campuses from the perspective of linking “macro society” network and intensity of individual MDI temporal behav- with “micro individuals.” ior are positively related. Existing studies have shown that the spatial network, its social relationship network, and its behavioral network influ- Similarly, when a person spends more time and effort to par- ence each other. Sailer and McCulloh (2012) believed that ticipate in various activities, one is more likely to establish spatial layout is a critical factor in forming interpersonal long-term and stable cooperative relationships, and gradu- relationships. Wineman et al. (2009) analyzed the influence ally establish contact with the elites in the industry through mechanism of spatial layout on the social network structure network expansion (Kim et al., 2018). Hagiladi and Plaut and innovation performance in innovative organizations. (2021) found that five variables were related to predicting Doreian and Conti (2012) used five empirical networks to the tie strength of social network, including meeting dura- posit that both social and spatial contextual features can tion, distance of meeting place from residence, transport impact the network formed within them. Suckley and Dobson Xia et al. 5 Figure 2. Location of Zijingang campus. (2014) identified key changes in the social connections that great influence on campus design in China. The campus is can be defined by spatiality in a university research about 2.2 km long from north to south and 1 km wide from department. east to west. Currently, more than 25,000 students are living The similarity of the network structure can be used to at this location (Zhu, 2011). Nevertheless, it is continually illustrate the adaptability of the spatial structure to certain criticized from the experts and space users because its large group characteristics. In previous studies, the comparison size and strict functional zoning created long traveling dis- method of overall network similarity has been used more tances (Yu & Wang, 2008). In fact, due to the long distances often at the macro scale, such as the matching degree of a and strict zoning, an obvious phenomenon of traffic “waves” trade level and regional centrality of different cities (Burger appeared (Zhu et al., 2004). This research takes the Zijingang et al., 2014; Pflieger & Rozenblat, 2010). Some urban campus as an example because it is typical of current Chinese designers also argue that the matching between networks of university campuses. social connections, public spaces, and public activity is an aspect that cannot be ignored in space design (He, 2014; Survey Method Sevtsuk & Kalvo, 2018). Therefore, the similarity compari- Questionnaire. According to the literatures and hypoth- son of network structures may also be used to measure the eses mentioned in Section 2.2, measurable variables for innovative adaptability of land use patterns. each latent variable and the measurement method of each measurable variable were set. The MDI potential includes Methodology four items: the degree of importance, easiness, enthusiasm as well as the innovative atmosphere. The MDI capabili- Case Description ties include three items: the quantity of research projects After the university enrollment expansion policy at the along with the quantity and quality of achievements. beginning of this century (MOE of PRC, 2020), China expe- The scale of the MDI social network includes two items: rienced a short-term and large-scale construction boom. the quantity of existing and potential collaborators. The Extensive development has brought about many problems in quality of the MDI social network includes three items: terms of resource utilization, function configuration, and the proportion of long-term and high-level collaborators spatial scale (Chen & Ren, 2015). At present, many univer- along with the value of networks. The temporal charac- sity campuses lack vitality due to their rigid and scattered teristics of MDI behavior include three items: the total functional zoning. It is difficult to meet the needs of multi- duration and times of MDI activities each week and the disciplinary communication and innovation. evenness of time distribution. The spatial characteristics Located in the northwest of Hangzhou, the Zijingang of MDI behavior include three items: the quantity and Campus (east) of Zhejiang University (Figures 2 and 3) is types of MDI spots frequently reached, and the evenness one of the most important milestones of the initial develop- of spatial distribution of these places on campus. The ment of China’s new campus construction. Zhejiang measurement methods for each measurable variable are University is one of the top research universities in China. shown in Table 1; accordingly, the data were normalized So, the Zijingang campus was set as an example and has before the SEM analysis. 6 SAGE Open Figure 3. The spatial layout of Zijingang campus, adpated from the design scheme of Architecture Design & Research Institute of Zhejiang University Co. Ltd. The questionnaire survey consists of two parts: question Sampling Technique answer and map drawing. The question answer part is The questionnaire survey was divided into two phases with divided into five sections, with a total of 26 multiple- the pre-investigation being carried out from November 15th choice questions and fill-in-the-blanks questions. The first to November 30th, 2019. A total of 111 questionnaires were section concerns the backgrounds of respondents, includ- distributed, and 75 valid questionnaires were recovered. The ing gender, grade, major, and living duration on campus. questionnaire settings were adjusted according to the feed- The second, third, and fourth questions all correspond to back. Then, from February 4th to March 15th, 2020, a formal the measurable variables. The fifth part is the satisfaction survey was conducted and a total of 572 questionnaires were evaluation of campus space for respondents, and a fill-in- recovered, 524 of which were valid. The investigators were the-blank question to explain their evaluation. In the sec- three senior architecture students who had received relevant ond part, on the campus map where each block is coded in questionnaire survey training in advance. The questionnaire advance, the respondents are asked to draw the location distributing method was to firstly contact the counselors of and path of their daily activities and the frequent MDI each college who had helped assemble the students in the spots, which are represented by circles, lines, and crosses, WeChat group. WeChat is a free messaging and calling app respectively (Figure 4). for mobiles. The counselors selected students randomly, Xia et al. 7 Table 1. Measurable and Latent Variables in the Questionnaire. Aspect Latent variables Measurable variables Assignment MDI Ability A Potential A Feel important 0 (general)~10 (important) A Feel easy 0 (difficulty)~10 (easy) A Enthusiasm 0 (low)~10 (high) A Environment of discipline 0 (low)~10 (high) B Achievements B Quantity of program 0~10 B Quantity of achievements 0 (little)~10 (much) B Quality of achievements 0 (low)~10 (high) MDI Network C Scale C Quantity of cooperators 0,1~3,4–10,10~20,>20 C Quantity of possible 0,1~3,4–10,10~20,>20 cooperators D Quality D Ratio of long term cooperators 0%–100% D Ratio of high level cooperators 0%–100% D Practice value of network 0%–100% MDI Behavior E Temporal E Total duration per week 0 hour~10 hour E Total times per week 0 time~10 times E Evenness of time distribution 0 (uneven)~10 (even) F Spatial F Quantity of MDI places 0~10 F Diversity of MDI places 0 (sole)~10 (diverse) F Evenness of spatial distribution 0 (uneven)~10 (even) Figure 4. Examples of answer on behavior mapping. while they kept the number of graduate students and under- undergraduates (49.21%); 211 were male (39.62%) and 313 graduates from every college equal. For each college, the were female (60.38%); 120 (20.94%) were within 1 year of sampling number was based on the proportion of students in living in the Zijingang campus; 279 (48.69%) had 1 to 3 years the college to the total number of students on campus. Then living there; 135 (23.56%) had 3 to 5 years; and 39 (6.81%) in the WeChat group the investigators instructed the students had more than 5 years. on how to complete the online survey. Questionnaires and map drawings were completed online utilizing platforms SEM including the Wenjuanxin (https://www.wjx.cn) and Mugeda (https://www.mugeda.com/) platforms. SEM is a tool for multivariate complex relationships. Finally, students from 11 colleges participated in the sur- Compared with other statistic methods, it is particularly vey and contributed to a total of 524 valid questionnaires, good at analyzing the relationship between multiple causes with an average of 47.6 respondents per college. Among and effects as well as the relationship between latent vari- them, 263 students were graduates (50.79%); 261 were ables. In this research there are a number of variables, and 8 SAGE Open their relationships are relatively complex. So, we use SEM— examined by fitness estimation—to analyze and explain the proposed theoretical assumptions. According to the defini- tion of SEM, the research model with latent variables is com- posed of a measurement model and a structural model. And it integrates the analysis of variance, regression analysis, path analysis, and factor analysis. The SEM analysis in this research is conducted by AMOS 26.0 for IBM SPSS 15.0 which is commonly used among the similar researches (Blunch, 2013) Network Analysis Data integration. Three types of overall networks were generated. They all that use campus blocks as nodes but adopt different connection methods. The space layout network is connected according to proximity of blocks (edge-to-edge) (Figure 5); the behavioral network is connected by the indi- vidual daily path routines; and the discipline collaboration network is connected according to the statistics of Question No. 14 (Which disciplines are your cooperator from?) in the questionnaire. The connections of nodes of behavioral and Figure 5. Network generation method of campus land. discipline collaboration network are calculated by integrat- ing individual data collected from the survey. That is to say, when the total amount of individual links between two nodes is over a certain threshold, a kind of connection will be gen- erated in these two overall networks (Figure 6). Indexes of network analysis. Network analysis is a well- developed and commonly used method for the relationship analysis in the fields as social science and geography (Ever- ett, 2002). In this research, the following indexes were used to compare different overall networks: Density (D) which reflects the closeness of network nodes and indicates the ratio of the number of edges which are present in the network to the upper limit of the edges. The betweenness centrality ( C ) ABi measures how much a node can control other nodes in the network—that is, how much a point is in the middle of other “point pairs” in the graph. The betweenness centrality of the graph (C ) indicates the extent to which a graph shows a ten- dency to concentrate to a certain point. The correlation degree of the graph (C ) characterizes the reachability between all points in the graph. Average Distance means the average shortest steps between two reachable nodes. In this research the above network indexes are calculated and compared by UCINET 6.0 software (Analytic Technologies, 2002). Evaluation method of MDI adaptability. Generally, the evaluation method compares the structural attributes of each node (according to campus land blocks) in a spatial layout network with those in behavioral and interdisciplinary net- Figure 6. Generation method of behavior and disciplinary works and the proportion of distribution of MDI locations network. to obtain MDI adaptability value. The average value of all nodes represents the degree of MDI adaptability of the spa- Since the space layout network, behavior network, and tial organization of the campus. interdisciplinary network have the same nodes, we use the Xia et al. 9 distances from one node to all other nodes to indicate its one involved inviting five architecture and urban planning structural property. The distance between two nodes on the experts with living experience in the Zijingang Campus to network is the length of the shortest path between the two score each block of the campus. The scores were 5 points nodes, which shows the closeness of them. The comparisons (very good), 4 points (good), 3 points (normal), 2 points of the average difference of the distance of one node to all (poor), and 1 point (very poor). The correlation analysis is other nodes in the two networks can reflect the structural conducted on the results of expert scoring and the established similarity ( S () i ) of this node in the two networks. The cal- evaluation method. The second part was about the compari- mn culation method is as follows: son of the satisfactory degree value of students in each dis- cipline and the evaluation result of each disciplinary block. Di (, jD )( − ij ,) mn The proposed evaluation method would be recognized as S () i = nj −≠ 1, i mn ∑ (1) Di (, jD )( + ij ,) reliable when the correlation analysis shows a certain degree mn j=1 of significance. where, D (, ij) and Di (, j) are the shortest distances m n between nodes i and j in networks m and n, respectively. Results and Analysis When the two nodes are not connected, the distance is con- sidered to be infinite. Therefore, Si () ∈ 0,1 . Descriptive Statistics  mn As for network structure attributes and MDI site distribu- For all respondents, their average scores of MDI potential tion, we compared the difference of the normalized between- were 8.2 ± 1.7 (feel important), 4.2 ± 2.2 (feel easy), ness centrality of a node in the space layout network and the 5.3 ± 2.3 (enthusiasm), and 5.8 ± 2.3 (environment of disci- normalized MDI site distribution value at that node (block). pline); the average scores of MDI achievements were The difference ( Qi () ) reflects the consistency of the MDI site 3.8 ± 2.6 (quantity of program), 3.6 ± 2.3 (quantity of distribution with the control power of the node (block) in the achievements), and 4.5 ± 2.4 (quality of achievements); the space layout network. The calculation method is as follows: average scores for network scale were 2.4 ± 1.1 (quantity of n n cooperators) and 2.7 ± 1.1 (quantity of possible coopera- () CC − () VV tors); the average scores for network quality were 28.8 ± 24.3 ABi ABi ii ∑∑ (2) i== 11 i (ratio of long term cooperators), 30.2 ± 25.9 (ratio of high Q() i = n n level cooperators), and 46.2 ± 27.4 (practice value of net- () CC + (VV )) ABi ∑∑ ABi ii work); the average scores for temporal behavior were i== 11 i 2.6 ± 2.3 (total duration per week), 2.1 ± 1.9 (total times per week), and 4.1 ± 3.6 (evenness of time distribution); the In the formula, C and V are the betweenness centrality ABi i average scores for spatial behavior were 3.4 ± 2.1 (quantity of node i and the spatial distribution value of MDI spots, of MDI places), 4.0 ± 3.0 (diversity of MDI places), and Qi () ∈ 01 , respectively. Similarly,  4.5 ± 3.0 (evenness of spatial distribution). The study divided all campus blocks into disciplinary and For the respondents who lived at the Zijingang Campus non-disciplinary types. For MDI adaptability of non-disci- for different durations (Figure 7), the innovative ability is plinary blocks ( AMDI () i ), we compared the structural nd different. For respondents who lived for a relatively short attributes of nodes in the space layout and behavioral period (<1 year), their MDI enthusiasm was slightly higher networks: than that of the other respondents—but their quantity of MDI AMDI () iS =+ ββ () iQ() i (3) nd 13 sb activities as well as the quantity and quality of results were significantly inferior. Those who had the most MDI partici- For MDI adaptability of disciplinary blocks ( AMDI () i ), in pation and achievements were the ones living for 3 to 5 years addition to two items, the structural attribute difference on the campus; those with the highest quality of achievement between space layout and interdisciplinary networks is also had lived more than 5 years. For respondents in different dis- considered. ciplines, the highest enthusiasm belong to individuals from the School of Management (6.11), and the lowest was gener- AMDI () iS =+ ββ () iS () iQ + β () i (4) ds 12 bsd 3 ated from the School of Medicine (4.91). The best achieve- where, β , β , and β are the weights given by experts. ments were from the Agriculture School (4.43), and the worst 1 2 3 Campus design is a very professional work, and the adapt- from the College of Civil Engineering and Architecture ability cannot be judged by perceptual knowledge of space (3.54). users. Hence, the weight values in this research only referred The average score of respondents’ satisfaction with the to the opinions of experts instead of space users. current campus was 3.56. The ratings which were most com- mon picked were “general” and “acceptable.” At the same Validation of the evaluation method. The verification of time, 9.6% of respondents thought it was “good,” and 2.96% the evaluation results was divided into two parts. The first indicated “bad” or “not good.” There was a difference in the 10 SAGE Open .70), and the Sig value is .000 < .05, indicating that the valid- ity of the scale is better. After factor analysis, the factor load of each measurable variable to the latent variable is greater than .5, which has a good convergence validity as a whole. SEM Analysis of Individual Networks Model fitting and modification. The maximum likelihood estimation method (MLE) is used to estimate the specifi- cations of the parameters of the “MDI capability-network- space-time behavior” correlation model. The results show Figure 7. MDI Ability and potential of respondents of different that the χ /df value of the model is 2.48, and the fitting duration in campus. indexes of the root mean square residual (RMR), goodness of fit index (GFI), and adjusted goodness of fit index (AGFI) degree of satisfaction between graduates and undergradu- are all greater than .90; the Root Mean Square Error of ates; accordingly, the overall satisfaction of undergraduates Approximation (RMSEA) value is .053. The overall fitting was lower than that of graduates. Subsequently, it could be index of the original model mostly meets the basic require- seen that 52.13% of undergraduate students and 41.92% of ments, but the RMSEA value is slightly higher. This shows graduate students thought that the satisfaction degree of the that the model fit is acceptable. It indicates that the settings Zijingang Campus was general, while 37.94% of undergrad- of the measurement model and the structural model are rea- uates and 42.96% of graduates considered it as acceptable sonable. In the terms of measurement model, the standard- (Figure 8). For different disciplines, students in the School of ized estimate values of the observed variable of each latent Pharmacy have the highest satisfaction degree (3.73), while variable are .401 to .880, which are significant in statistics. students of the School of Public Administration and Overall, the measurement model is ideal, showing that the Environment have the lowest (3.41). observation indicators in the model have a considerable impact on specific structural variables, which can be very effective in explaining the corresponding latent variables. Reliability and Validity of the Questionnaires Consequently, there is no need to eliminate any observed The study first conducted a composite reliability analysis of variables. In the structural model, the causal path coeffi- all variables, and then calculated the Cronbach’s alpha reli- cients partially support the research hypothesis. Except for ability coefficient. The criteria for variable selection were as one path, all of the others reach a significant level, and the follows: (1) the skewness and kurtosis of the variables are standardized path coefficients range from .407 to .491. All within a reasonable range; (2) the correlation between the the correlations of latent variables reach significant levels, individual variables and the whole is greater than .3; and (3) with correlation coefficients ranging from .364 to .505. the composite reliability coefficient is greater than .5. The In the original model, although both the standardized variables of MDI ability, MDI Network scale, and MDI tem- regression path coefficient and the fitness are acceptable— poral behavior feature all met the requirements. However, the and the overall model fits well—a further revision is required skewness and kurtosis values of variable D in the question- 4 to obtain the standard model. The revision of the model is naire exceeded 10, and the reliability coefficients of A , D , 1 1 mainly based on the value of the modification index (MI). and E were all low. After checking for the data cleaning, no 3 Through analysis, it was found that among the six latent vari- problem was found in the data collection procedure, and the ables in the original model, the MI value between the MDI timing did not affect the accuracy of responses in this research. capability and the MDI temporal behavior was 17.535 (>4), For A , it is because most of the respondents realized MDI 1 so a path could be added. Finally, the fitting indexes of the was highly important, so it cannot measure the differentiation modified model are listed in Table 3. While the degree of fit- between individuals. For D and E , the respondents might 1 3 ness was somehow affected due to some reasons, including have difficulties in evaluating what was a long-term coopera- the large number of samples, various hypotheses and com- tion and how their time was distributed, which caused the low plex model structure in this study. Nevertheless, all indexes reliability coefficients. So, these four variables were excluded met the recommended values for successful fitting (Wu, from the analysis. 2017). It can be seen from Table 2 that the Cronbach’s alpha of each level of scale is between .543 and .836, and the reliabil- Hypothesis validation and interpretation of SEM. The study ity of the total scale reaches .853, indicating that the data uses the significance of the path coefficient to determine generated from the questionnaire is consistent and stable. whether a hypothesis is true. Among the 11 hypotheses, 10 The Bartlett’s test of sphericity shows the Kaiser-Meyer- of them were proved to be reasonable by SEM, and only one Olkin (KMO) value of the total scale is .831 (greater than failed verification (Table 4). That is, the potential of the MDI Xia et al. 11 Figure 8. Satisfaction on the campus of graduates and undergraduates. Table 2. Reliability and Validity of the Questionnaires. Latent variables Measurable variables Cronbach’s α KMO value Bartlett’s sig MDI potential A ~A .543 2 4 MDI achievements B ~B .836 — — 1 3 MDI network scale C , C .749 — — 1 2 MDI network Quality D , D .714 — — 2 3 MDI spatial feature E , E .755 — — 1 2 MDI temporal feature F ~F .826 — — 1 3 Total A ~A , B ~B , C , .853 .831 .000 2 4 1 3 1 C , D , D , E , E , 2 2 3 1 2 F ~F 1 3 was not determined by the scale of the network, but mainly of time and extensive space for MDI activities in a frequent by the quality of the network and existing MDI abilities. The way. Hence, the MDI adaptability of campus space layout quantity and quality of the network together determine the is the degree to which it satisfies the users’ overall spatio- MDI abilities. For the correlation analysis, four latent vari- temporal behavior characteristics and makes the spatial and ables of network quality, network quantity, behavior time, temporal distribution of MDI activities more reasonable as a and behavior space all have pairwise significant positive cor- whole. This can be mainly reflected in three aspects, which relations. This means that for an individual on campus, the are presented in the following paragraphs. more time and frequency of one’s MDI activities, the more First, the spatial layout should improve the behavior effi- MDI locations one could use. Correspondingly, the quantity ciency of space users. The improvement of behavior effi- and the quality of the MDI network will increase, resulting ciency means improving the time and space distribution of in stronger MDI capabilities and potential. In the modified MDI innovation activities. This can be evaluated by the model, the added path between MDI participation time and structural similarity between the space layout network and MDI capability indicates that there is a direct correlation the behavioral network. between the them. Second, the spatial layout should meet the requirement of convenience of the disciplinary connection. Disciplines Reflection on the results of the individual network. The SEM which connected more tightly with each other should be spa- analysis confirmed the relationship between individual inno- tially closer, allowing the MDI to be more convenient. This vation on campus and their spatiotemporal behavior. Nev- can be evaluated by structural similarity between the space ertheless, individuals’ MDI innovation capabilities or needs layout and the discipline connection networks. are different. So are their temporal and spatial behaviors. Third, the space layout should make the MDI sites conve- Individuals or groups with strong innovation capabilities nient to be reached. These places should be located as rea- and high innovation demands tended to devote a long period sonably as possible to maximize their efficiency and 12 SAGE Open Table 3. Fitness of SEM. GFI AGFI CFI RMSEA Chi-square/df Original .954 .929 .959 .053 2.48 Revised .962 .942 .972 .044 2.02 Reference (Wu, 2017) >.90 >.90 >.90 <.05 1.00~3.00 Table 4. Hypothesis Validation. Hypothesis Correlation Estimate Sig. Results H (MDI Ability) → (MDI Potential) .530 *** True H (Network Scale) ↔ (Network Quality) .358 *** True H (Network Scale) → (MDI Potential) −.128 .078 False H (Network Quality) → (MDI Potential) .445 *** True H (Network Scale) → (MDI Ability) .289 *** True H (Network Quality) → (MDI Ability) .258 *** True H (Spatial Behavior) ↔ (Temporal Behavior) .384 *** True H (Network Scale) ↔ (Spatial Behavior) .364 *** True H (Network Scale) ↔ (Temporal Behavior) .445 *** True H (Network Quality) ↔ (Spatial Behavior) .446 *** True H (Network Quality) ↔ (Temporal Behavior) .447 *** True ***Sig.<.001. algorithm forms the number of groups where connections are maximized within, and minimized connections are between the groups. When the graph was divided into two groups, the fit value was the highest (fitness = 466.00). Compared with the campus map, the red group almost corresponds to the north blocks. While the blue corresponds to the south blocks. This indicates that the north-south division of the Zijingang Campus is very remarkable. The average centrality of all the nodes was 47.057 ± 43.745. The most central nodes were No. 29 (Alumni Forest, centrality = 159.886); No. 21 (Nan- hua Garden, centrality = 152.696); and No. 30 (grass, cen- trality = 139.092). Blocks with the least centrality are No. 14 Figure 9. Network of spatial layout. (Medicine College, centrality = 1.533); No. 18 (Biological Experiment Center, centrality = 1.450); and No. 10 (Agricul- convenience in usage. This can be evaluated by comparing ture College, centrality = 0.000). The centrality of the nodes the structural characteristics of the space layout network with corresponding to the disciplinary blocks is not high. the distribution of the MDI sites. Figure 10 shows the behavioral network on the campus. In order to facilitate the comparison with the space layout network, we set the threshold at five for connections. This Comparison of Whole Networks will be done when there are more than five path connections Network features. Figure 9 shows the space layout net- between two nodes. In this case, they are considered to be work for the Zijingang Campus. The number of each node connected—otherwise, they are not. In this way, the densities is coded according to Figure 2; their shapes represent dif- of the two networks are relatively close and are consequently ferent functions. Diamonds represent disciplinary blocks, suitable for analysis and comparison. In general, the space while squares mean non-disciplinary blocks. The size of a layout network is indeed different from the behavior net- node represents the betweenness centrality of the node in the work. First, all the nodes in the space layout network are network. The higher the centrality, the larger the nodes in the connected, but there are 12 nodes isolated in the behavioral graph. Then, base on a “faction” algorithm the nodes were network. The cohesive subgroup analysis divides the net- analyzed by cohesive subgroup analysis. A faction is a part of work into two groups (fitness = 314), with all connected a graph in which the nodes are more tightly connected to one nodes in a group and unconnected nodes in the other one. another than they are to members of other factions. Hence, the The blocks that are not connected in the network are totally Xia et al. 13 number of connections between Schools of Public Administration and Management is the largest; and the number of connections between schools of pharmacy and public management is the least. Similarly, we set the thresh- old at 28 so that the density of the disciplinary network can be close to that of the space layout and behavior network. As shown in Figure 11, there are 11 nodes in the disciplinary network, which are divided into four groups (fitness = 4.00). Node 10 (Agricultural College) and Node 20 (College of Civil Engineering and Architecture) are not connected with other nodes. This shows that there are two groups of tight linked nodes. One group includes Node 5 (Public Figure 10. Network of behaviors. Administration), Node 6 (Foreign Languages), and Node 25 (Management). The other group includes Node 8 (Food), Node 9 (Environment), Node 11 (Life Science), Node 13 (Pharmacy), and Node 14 (Medicine). Among all of them, Node 9 has the highest network control power (betweenness centrality = 7). Network comparisons. As shown in Table 5, the densities of the three networks are relatively close after the thresholds are set, but obvious differences can be found in between- ness centrality and correlativity. For the average value of betweenness centrality, the space layout network is the highest (47.057 ± 43.745) and the disciplinary network is the minimum (1.636 ± 2.568). The centralities of the three Figure 11. Network of disciplines. networks are 20.70%, 8.16%, and 13.11%, respectively. As for the correlativity of the network, the nodes of the space not isolated in the real sense, but they are very weakly layout network are all connected, and the average distances related. Second, among the remaining nodes in the behav- between them is 3.768. There are many isolated nodes in ioral network, the average distance between the nodes is the behavioral network. But the remaining nodes are more smaller; moreover, they have a closer relationship than those closely connected, and the average distance between them is in a space layout network. Third, the degree (number of con- 1.826. The disciplinary network has the most subgroups in nections) of the nodes greatly varies as some nodes are con- the three networks—or the node with the weakest degree of nected to many other nodes. For example, No. 24 is connected association—with an average distance between nodes to be to 17 nodes, but the highest degree in the space layout net- 2.000. In short, there are obvious differences in the structures work is only 6. In the behavioral network, the average value of the three networks. of betweenness centrality is 5.971 ± 12.783. The three blocks Adaptability of spatial layouts. Based on the method with the highest betweenness centrality are No. 32 (cafeteria, established in Section 4.4, we conducted an MDI adapt- centrality = 50.453); No. 24 (west teaching buildings, cen- ability evaluation on the blocks within the Zijingang trality = 49.481); and No. 23 (east teaching buildings, cen- Campus (Table 6). The campus’s blocks are divided into trality = 31.584). Similar to simulation in the space layout two categories: disciplinary and non-disciplinary. For the network, the betweenness centrality of the disciplinary disciplinary blocks, the structural similarity of nodes (S blocks is also low in the behavioral network. Among them, sb and S ) and the consistency of the betweenness centrality No. 5 (Public Administration College) has no connection sd of the nodes in the space layout network were considered with others. together with the distribution of MDI sites (Q) on them. The data of the disciplinary network connection is According to experts’ opinions, the weights are 0.4, 0.4, obtained from the question, “Which colleges do your MDI and 0.2 for S , S , and Q, respectively. For non-disciplin- collaborators come from?,” which has been displayed in the sb sd ary blocks, S and Q were considered and their weights questionnaire. The number of connections between two col- sb were 0.6 to 0.4. leges is the sum of the connections of one college to the It can be seen from Table 6 that the MDI adaptability of other. According to the statistical results, the college having blocks is not good in general. As for S , the most MDI the largest number of connections with the other colleges is sb adaptable block is No. 26, and the score of which is 0.46. the School of Foreign Languages; the one with the least con- The rest are all above 0.5. There are 12 blocks with a score nections is the School of Construction Engineering; the 14 SAGE Open Table 5. Comparison of Three Networks. Density Centrality of nodes Centrality of network (%) Correlativity Distance Avg. NS 0.1109 47.057 ± 43.745 20.70 1.00 3.768 NB 0.1261 5.971 ± 12.783 8.16 0.4252 1.826 ND 0.1273 1.636 ± 2.568 13.11 0.3273 2.000 Note. NS = network of spatial layout; NB = network of behavior; ND = network of disciplines. Table 6. Evaluation Results of Each Network Nodes (Campus as for S , No. 9 has the best adaptability, with a score of sd Blocks). 0.53. The rest are mainly from 0.50 to 0.80, except for two blocks with a score of 1.00. The average S value was 0.72. sd No. of Non- As for Q, the blocks with better adaptability (the evaluation nodes S S Q disciplinary Disciplinary sb sd value is less than 0.20) include blocks 5, 8, 11, 12, 14, 15, 1 0.66 — 0.63 0.65 — and 28. Among the non-disciplinary blocks, No. 26 has the 2 1.00 — 0.51 0.88 — best overall MDI adaptability, and a total of three blocks 3 1.00 — 1.00 1.00 — are less than 0.5. Among the disciplinary blocks, No. 8 and 4 0.51 — 0.72 0.56 — No. 14 are of the best adaptability. The average scores of 5 1.00 0.80 0.08 — 0.74 the final MDI adaptability evaluation values of non- 6 0.53 0.80 0.59 — 0.65 disciplinary and disciplinary blocks are 0.74 to 0.60, 7 1.00 — 0.62 0.90 — respectively. 8 0.63 0.56 0.03 — 0.48 9 0.62 0.53 0.53 — 0.57 Validation of Evaluation 10 0.63 1.00 0.28 — 0.71 11 0.61 0.57 0.00 — 0.47 The study examines the proposed evaluation method based 12 0.59 — 0.14 0.48 — on two aspects. First, we conducted a correlation analysis 13 0.56 0.55 0.34 — 0.52 between the calculated values and the average scores of six 14 0.59 0.62 0.07 — 0.49 experts for the 35 blocks. The average value of the experts’ 15 0.60 0.65 0.16 — 0.53 evaluations on all blocks was 2.7. The results in Figure 12 16 1.00 — 0.69 0.92 — showed that the two sets of data had a strong correlation (cor- 17 1.00 — 0.82 0.96 — relation coefficient = −.623, significance = .000). Second, we 18 1.00 — 0.48 0.87 — compared the satisfaction degrees of students from 11 col- 19 1.00 — 0.36 0.84 — leges with calculated values (Figure 13). The average satis- 20 0.58 1.00 0.49 — 0.73 faction degree of students from 11 colleges was 3.57. The 21 1.00 — 0.77 0.94 — results of the correlation analysis showed that the two sets of 22 1.00 — 0.37 0.84 — data have a certain correlation (correlation coefficient = −.702, 23 0.64 — 0.74 0.66 — significance = .016). Above all, it implies that the proposed 24 0.63 — 0.74 0.66 — method is reliable in evaluating the MDI adaptability of the 25 0.55 0.80 0.80 — 0.70 campus space. 26 0.45 — 0.47 0.45 — 27 0.55 — 0.86 0.63 — 28 0.57 — 0.18 0.47 — Discussion 29 1.00 — 1.00 1.00 — 30 1.00 — 0.37 0.84 — Reflections on the Research of Innovative 31 0.56 — 0.78 0.61 — Campus Design 32 0.61 — 0.56 0.60 — In the past 10 years, campus construction and renewal in 33 0.56 — 0.93 0.65 — Europe and the United States have provided many excellent 34 0.61 — 0.93 0.69 — cases. Scholars have analyzed and summarized practical 35 0.64 — 1.00 0.73 — cases based on the perspective of improving innovation abil- Average 0.73 0.72 0.54 0.74 0.60 ity and multidisciplinarity (Coulson et al., 2018; Hoeger, Note. (1) or non-discipline the weighs β and β are .6 to .4. (2) For 1 3 2007; Taylor, 2010). The aim of these researches is to reveal discipline the weights β , β , and β are .4, .4, and .2. (3) The figures 1 1 1 the spatial prototype of the innovative campus. They provide in gray are for the disciplinary blocks. (4) The no. of blocks are outlines, frameworks, and strategies for decision-making corresponding to Figure 2. with good practical application value (e.g., Qiu, 2014). However, these normative case studies used to interpret the- of 1.00 which means that these nodes cannot be compared ory are different from the empirical case studies used to test in the two networks. The average S of 35 blocks is 0.73; sb Xia et al. 15 of these studies use post-occupancy evaluations, which rely more on people’s subjective feelings, resulting in less objec- tive and predictable conclusions. Moreover, research on the correlation between subjective feelings and spatial environ- mental factors often stops at data description and analysis. In this sense, the innovations and contributions of this research are significant. It explains how the spatial layout affects MDI by social activities and relationship, which is ignored in the existing research. It links the MDI and campus design by integrating the individual and overall network per- spectives. Through quantitative analysis, it forms a new sci- entific theoretical basis for innovative campus design, and provides references for design practice. Spatial Design Strategies for MDI Campus From the overall network perspective, while most of the Figure 12. Evaluations of expert and method proposed. behavioral network nodes on the Zijingang Campus are closely connected, the current space layout is relatively loose. Besides, the nodes in the disciplinary network are divided into multiple subgroups, indicating that the disci- plinary network reflects a lack of disciplinary connections and an unreasonable disciplinary setting. Although the space layout network and disciplinary network have significant correlation, the MDI adaptability of all the blocks is still low due to large-scale zoning. To improve travel efficiency, one approach is to increase the public transportation service (e.g., shared bicycle and mini-bus). The other is to increase the quantity and distribution of public facilities such as cafete- rias, study areas, and discussion spaces, to reduce the routine and time of necessary behaviors and thus solve the traffic problems (Zhu et al., 2004). Figure 14 shows the MDI adaptability values of the blocks in Zijingang. The color saturation indicates the score, with eight levels from 0 to 1. Disciplinary and non- disciplinary blocks are represented by green and yellow, Figure 13. Satisfactory degree of students and evaluations of respectively. Compared with Figure 3, it can be seen that method proposed. the blocks of grasslands, open spaces, administrative build- ings, and staff accommodation areas are of less adaptabil- theory (e.g., Coulson et al., 2018; Taylor, 2010). The qualita- ity. This is because these function blocks have no MDI sites tive induction method they use is not statistical but analyti- within them and are not part of daily life for students either. cal, lack standardized data analysis, and are selective in the Second, the center of the campus has a large area of water presentation of evidence and the interpretation of phenom- that cannot be traversed, resulting in a decrease in its con- ena (e.g., Qiu, 2014). nection efficiency—especially between the east and west Half a century ago, in “The Oregon Experiment,” teaching blocks. In addition, although the campus with a lot Alexander (1975) discussed the use of “diagnosis” and of water and green spaces is good for ecology, its current “coordination” to create an “organic order” for campus plan- land use or distribution patterns are not intensive and effi- ning. In recent years, built environmental evaluation on cam- cient, especially for MDI. pus has gradually evolved from an index evaluation on Therefore, a dense and well-grouped spatial layout could objective physical environment—such as landscape green be a better type for future campus renovation that takes dis- coverage and land classification structure (Dober, 2000)—to ciplinary blocks with more interdisciplinary exchanges as a social environmental psychological evaluation, which the core of communication, close to student accommoda- includes students’ satisfaction and concern with the outdoor tions, and surrounded by service functions such as adminis- environment of the campus (Eckert, 2014). However, most trative offices and ecological green spaces in the periphery. 16 SAGE Open Figure 14. MDI adaptability of Zijingang campus. In fact, some college-based universities are trying to adopt informal communication means reducing the time for nec- this mode at present (Coulson et al., 2018). essary behaviors and increasing efficiency. So consider- For individuals on campus, spatiotemporal behaviors able informal communication spots should be arranged can be divided into necessary behaviors and unnecessary while optimizing the space layouts (Magdaniel et al., 2018; behaviors, which corresponds to formal communi-cation Winks et al., 2020). For example, the Infinite Corridor at and informal communication. Many studies (Assimakopoulos, the Massachusetts Institute of Technology (Massachusetts 2007; He, 2014; Yun et al., 2018) have shown that the MDI Institute of Technology, 2020b) connects all of the facul- is more inspired by informal communication of unneces- ties and departments, and interdisciplinary innovation sary behaviors. Since an individual’s the physical strength studios like the iLAB at Harvard University (Harvard and the total time of the individual are certain, promoting University, 2020). Xia et al. 17 Funding Limitations and Prospects The author(s) disclosed receipt of the following financial support Firstly, the ultimate aim of this study is to develop a spa- for the research, authorship, and/or publication of this article: This tial layout typology of MDI campus through quantitative research is funded by Open Project of State Key Laboratory of research. The present results are preliminary and limited Subtropical Building Science, South China University of case studies, which may affect the universality of the Technology, (Granted No. 2020ZB09) and National Nature and conclusions. Therefore, it is necessary to select and com- Science Foundation of China (Granted No. 51808486). pare more campuses to enhance the generality and credibility. ORCID iDs Secondly, this study does not consider whether MDI Bing Xia https://orcid.org/0000-0002-9448-7416 activities within the campus are affected by its location Jindong Wu https://orcid.org/0000-0003-2322-7286 and surrounding environmental resources. This needs to be further verified through a comparative study of cam- References puses in different zones and at different stages of urban Alexander, C. (1975). The Oregon Experiment. Oxford University development. Press. Thirdly, the evaluation method is based on the assump- Analytic Technologies. (2002). UCINET 6. 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SAGE Open – SAGE
Published: Mar 9, 2022
Keywords: multidisciplinary innovation; campus; adaptability; social network; behavioral characteristics; spatial layout
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