4.1. Overview of the Data Collected
While many studies have been performed to evaluate the complexity in construction projects, and a few studies have evaluated building projects, no previous research has been performed specifically assessing the measures of complexity in various types of building projects based on design and performance attributes [
16]. The process framework, as detailed in
Table 2, was adopted for data collection.
Design and performance attributes were chosen to evaluate the source of complexity, as the construction management aspect has been widely covered in earlier research. As in the earlier section, the variables or attributes were validated for measurement by consulting three architects and designers with nearly 17 years of average experience in this field to remove ambiguity and establish clarity in the description [
49]. The draft questionnaire was circulated amongst these experts, and their inputs and suggestions were suitably incorporated. With the help and reference of experts, the final survey questionnaire was distributed to a few select architects and designers. They were requested to refer a few other architects and designers from their professional circle. Using the snowball technique, of all the Architects and designers contacted, about 40 volunteered to participate in the survey. The snowball technique is an accepted method of collecting data, especially when dealing with exploratory research where no definite database is available [
4].
Given that 34 attributes need to be evaluated for seven building types, the survey required a minimum of 45 min (as per the experts who participated in the pilot study), which was informed to all the potential participants at the first briefing. Given these professionals’ general time constraints, many declined to participate in the survey in the initial inquiry stage. Only those who agreed to devote time to the survey were shared the questionnaire. Overall, 31 completed surveys were returned with a response rate of over 75%. While the sample size is not large enough, the statistical analysis can still be performed as the central limit theorem holds so long the sample size is more than 30 [
66]. The Respondents’ profile is as given in
Table 3.
A total of 25 of the 31 respondents were Bachelorettes in architecture, with three having completed a Master’s in the same field. The remaining six have a bachelor’s degree in engineering or construction management.
Of those who responded, nine were female, and the remaining 22 were male. The average age of the respondents was 36.93 years. The overall average experience is 13.88 years, and the number of years of experience associated with/relevant to the building construction industry is 12.80 years. Most of these respondents are located in the Metros, viz. Hyderabad, Bangalore, Chennai, New Delhi. 21 of the 31 respondents are employed in architectural firms, and the remaining are with developers, contracting companies, and the government. Architects and designers were chosen as the respondents as the research evaluates the source of complexity predominantly in the design and pre-construction stages.
4.2. Data Analysis and Discussion
Within each parameter, the highest scale rating was assigned for the responses with the highest level of conformity to the statement, thus the complexity [
67]. Since 34 items are evaluated for seven building classes, internal consistency has been calculated using Cronbach Alpha for each type of building, and the resultant values are given in
Table 4 hereunder. When developing a measurement scale, it is essential to assess the reliability of the constructs. This is done by evaluating the consistency of the repeated measurements or to what extent the items measure the same thing. Cronbach’s alpha is one of the established and well-accepted internal consistency reliability estimating methods. Cronbach’s alpha coefficients are used as the benchmark to verify the reliability and validity of the data collected through the survey. The online statistical tool was used to calculate Cronbach’s alpha coefficient for the survey data.
The acceptable level of internal consistency is 0.7 to 0.95, and in our analysis, the range is between 0.84 and 0.95. However, it is to be noted that values higher than 0.90 may indicate redundancy, requiring shortening the questionnaire [
68]. Since the questionnaire needs to be standard across all types of buildings, no changes were made. The recommended values for Corrected items to Total correlation should be ideally between 0.3 and 0.7 [
69]. In some cases, the lower range can be up to 0.20, subject to minimal impact in Cronbach’s Alpha if the item is deleted. Since the research is evaluating the complexity between various types of buildings, the measurement needs to be similar, and hence, internal consistency reliability may not be achieved on all items. Overall, as seen in
Appendix A, in 76% of the instances (number of items X types of buildings), Corrected items to Total correlation is more than 0.3, and in 88% of the cases, they are less than 0.7.
There are many statistical test options available for the evaluation of data from more than one group. The Kruskal-Wallis Test is the preferred method for inferential statistics, especially licker scale data, where the data may not follow a normal distribution. Whereas the
t-test (two Samples) and the Chi-Square test are best used in numerical data and data with binary responses, respectively [
70], Kruskal-Wallis is a non-parametric test and compares three or more groups in the ordinal or continuous data [
71]. This test is widely used when the assumption of one-way ANOVA, such as equal variances or normality, is unmet [
72]. In comparison to all other options, viz.
t-test, ANOVA, Wilcoxon Signed Rank test, Friedman test, and Mann-Whitney test, the Kruskal-Wallis test is (a) preferred where the data is not normally distributed and (b) compares multiple groups that are not paired or repeated measures. These two essential conditions are pertinent in evaluating seven building typologies on multiple parameters. In addition, the Kruskal-Wallis test provides the mean ranks that are effectively used to understand the relative position of each building typology against each parameter. The null hypothesis for the Kruskal-Wallis Test is that there is no difference between the groups of independent variables or between the medians of the groups [
73]. Regarding complexity evaluation, the Null hypothesis is that the medians of all the building types are the same irrespective of the level of complexity [
70]. Even if one building type has a different median, the null hypothesis fails to be accepted, and the complexity of at least one building is different than the other types of buildings. When the
p-value derived from the test is below the chosen significance level, the null hypothesis fails to be accepted, and we may safely conclude that there are significant differences between at least two types of buildings [
73]. In the Kruskal-Wallis test, the central tendency of each group can be identified using the mean ranks, which provide early insight into the differences among the groups. The mean ranks are used for the comparison, and the mean ranks represent the average position of observations within each group. In the study on “Identifying and Measuring Project Complexity” by Dao et al., [
70] the Kruskal-Wallis test was used as one of the statistical methods with the Null hypothesis of measuring the probability of the Median of a highly complex project being more than 0.5.
The chi-square statistic (Chi2) here represents the magnitude of the difference between the observed and expected frequencies. Degrees of Freedom (df) is the number of categories or groups minus 1. p-value (p) indicates the probability of observing a chi-square statistic as extreme as the one calculated, with the assumption that the null hypothesis is true. In most cases, the p-value observed is less than 0.001, indicating a very low probability of obtaining the observed results with the assumption that there is no association between the variables. This low p-value typically leads to the null hypothesis being rejected in favor of the alternative hypothesis.
The online Statistics Calculator DATAtab “
https://datatab.net/ (accessed on 8 April 2024)” was used for statistical calculations. The mean ranks obtained under the Kruskal-Wallis Test have been used to represent the relative complexity between buildings against each attribute/parameter, and the questions have been framed such that the highest value for a type of building represents relatively higher complexity between all kinds of buildings, for that parameter. Attributes on which the comparison is made between the types of buildings are called parameters.
The analysis is based on 31 completed surveys from respondents with an average experience of 13.88 years and from different cities in India. The data exhibit good internal consistency and reliability. The sample size may not be representative of the entire population of architects and building professionals in India, and increasing the sample size may ensure a more diverse representation of participants from different regions and project types.
4.2.1. Project Location as a Source of Complexity
Figure 1 and
Table 5 give the results from evaluating parameters connected to the project location using the Kruskal-Wallis Test. (For complete data, refer to
Appendix B).
We can infer the following using the mean ranks of each building class against the parameters.
Compared to other facilities, the location is more important than the price for Residential as it has the highest mean rank (parameter 1) and moderate mean rank score under importance assigned by the end-user to scope and quality over location (parameter 2). However, it does not necessarily need to be closer to the city center to be commercially successful (parameter 3). For the hotels and retail buildings/malls, the criticality of the facility being closer to the city center to be commercially successful (parameter 3) and the importance assigned by the end-user to location over price (parameter 1) is more important than the importance assigned by the end-user to scope and quality over location (parameter 2). For educational institutions and hospitals, the importance assigned by the end-user to scope and quality over the location (parameter 2) takes precedence over the importance assigned by the end-user to be closer to the city center (parameter 3) or location over price (parameter 1). Surprisingly, general offices did not have preferences for location over price, scope, and quality (parameters 1 and 2), except to some extent, they prefer to be closer to the city center as it has a moderate mean rank score under parameter 3. Warehouses score the least out of all of them.
As mentioned earlier, projects closer to the city center will always be challenging due to constrained space, logistical challenges, etc. [
42,
56]. Moreover, this leads to higher construction costs. All these factors make a project closer to the city center and location-centric riskier and more complex. With limital capital in hand for the developer and limited room to increase the price of the end product/service, if a more significant capital outlay is towards the land, there shall be pressure on the developer to lower the project cost and may resort to appointing the lowest bidder, reducing cost by choosing lesser technologies, eventually leading to higher risks in the execution [
74] and increase the complexity.
So, regarding choosing the project location, high-rise residential buildings get their complexity from the need to select the correct location [
75], which is subject to availability, demand-supply, price, and issues around the land parcel. In the case of hotels, as per the study on critical success factors, location attributes or business influencing factors play a crucial role in choosing the location or making the investment decision [
76]. In the study on CSF of Shopping mall development in Malaysia [
77], the location was identified as the single most critical external factor impacting the development of shopping malls. This makes the hotels and retail buildings/malls required to be next to the city center, resulting in issues such as land availability, traffic restrictions, higher construction costs, logistical constraints, etc. For educational institutions and hospitals, the importance assigned by the end-user to scope and quality is much higher, requiring prudent design and robust execution, resulting in complexity.
4.2.2. Planning and Design Approaches as the Source of Complexity
Six parameters were selected and evaluated as the source of complexity for the planning and design approaches. The results from the Kruskal-Wallis Test on the data are given in
Figure 2 and
Table 6 (For complete data, refer to
Appendix C).
In a critical review of complexity theory and how it relates to architecture, engineering, and construction [
45], the authors opined that it is difficult to determine the measures of complexity in the design, construction, and management of building projects as it involves multiple disciplines. They can, at best, be examined in the functional domain (design problem and process) and physical domain (construction and system integration). On the distinct design enhancing/impacting users’ efficiency (parameter 4), retail buildings/malls stand out with the highest mean rank, followed by educational institutions and high-rise residential. Hotels and hospitals have almost similar mean rankings.
Design plays a crucial role in the spatial planning and creation of various sub-facilities, their location, size, scope, etc. A sound design basis is expected to improve the facility’s and its users’ overall performance. Regarding different design approaches varying the capacity and scalability of operations (parameter 5), Retail buildings/malls still score the highest mean rank; however, high-rise residential buildings and hotels scores are higher than those of hospitals and educational institutions. This attribute indicates how the designer helps increase the overall scale of operations using smart designs, and this looks more relevant in the case of retail buildings/malls, residential buildings, and hotels. Regarding the influence of the design approach on the economic value of the facility (parameter 6), high-rise residential buildings score the highest mean rank, followed by hotels and retail buildings/malls at the second mean rank. All the other facilities do not score enough on the mean ranking, making design highly sensitive in the case of residential buildings, hotels, and retail buildings/malls. On questioning whether the end-users insist upon innovation in design and development (parameter 7), Hotels score the highest mean rank, followed by retail buildings/malls and residential buildings. In these types of buildings, value creation happens by having the right design. As the design takes center stage, the developers will be able to create the distinction over the period for its design and design-related benefits to the end user; however, in the short term, it might lead to additional costs towards development.
The complexity of these buildings emanates from the criticality of the design to achieve the desired project goals, leading to project success. In many of these projects, these attributes are included in the goals and strategies, such as Target Value Design and Value Management [
61]. However, all these attributes increase the burden on the designer to provide a robust design and on the developer to select the right consultant and develop these facilities as per the design despite its cost. These compulsions lead to the risk of the design becoming complicated and the budget increasing.
Regarding complexity in planning (i.e. floor efficiency, services, structure, façade, etc.) (parameter 8), hospitals and hotels are far ahead in the mean rank score. However, due to the number of specialist consultants required for the design of the facility (parameter 9) being the highest in hospitals, hospitals are most complex by nature.
Therefore, what is evident from the above is hospitals, followed by hotels and retail buildings/malls involve highly complex design processes compared to their peers in terms of the nature of the facility. On the design impacting productivity, efficiency, capacity, capability, economic value, etc., as a strategy, residential buildings join hotels and retail buildings/malls. Educational institutions are sensitive to the design impacting efficiency and scoring relatively better than general offices on other parameters. Warehouses have the lowest mean rank score.
4.2.3. The Volume of the Facilities and Their Key Components as the Source of Complexity
There are 11 parameters through which complexity through the volume of the facility has been evaluated. The results from the Kruskal-Wallis Test on the data are given in
Figure 3 and
Figure 4 and
Table 7 and
Table 8. (For complete data, refer to
Appendix D and
Appendix E).
Complexity in construction can be either vertical (level of details required) or horizontal (level of specialization required). The premise under this section is that occupational density, the requirement of higher parking, complexity in vertical transportation, the scope of amenities, many electromechanical installations being part of the facility, the level of safety and security required, the scope of integration of various sub-components of the project, building height, size of floor pate, the stringency of development control regulations, etc. lead to complexity in the development of any buildings.
Retail buildings/malls scored the highest mean rank overall within the volume of facilities. They score the highest mean rank on the number of parking spaces planned for every 1000 SFT of usable area (parameter 11) and vertical transportation logistics complexity (parameter 12). This is due to higher footfalls and floating population in the retail buildings/malls. They also score the highest mean rank in the typical floor plate size of a standard building (parameter 18), as malls are generally more horizontal than vertical in development. The score on the stringency of development control regulations and codal compliance (parameter 20) is possibly high due to the large number of people visiting the building, creating huge safety/risk issues.
Retail buildings/malls scored the second highest mean rank in occupancy density per 1000 square feet of usable area (parameter 10) next to educational institutions. While the floating population is higher in the case of retail buildings/malls, the student population is much higher based on more extended usage by time as end users. Surprisingly, they score lower than hotels in the scope of amenities within the project (parameter 13). On installation of many electro-mechanical equipment as part of the scope (parameter 14), retail buildings/malls score next to hospitals, as hospitals have more complicated electro-mechanical systems than any other buildings. Its mean rank on the general range of building height (parameter 19) is much lower than that of all other buildings, as retail buildings/malls are more horizontally developed. On the scope, complexity, and intelligence of BMS (parameter 16), the scope of integration represented by the number of sub-components of the project (parameter 17), and the level and intensity of safety and security (Parameter 15), retail buildings/malls score lower mean rank than hospitals and hotels. This is possibly due to the requirement for the highest quality of operations in hospitals and hotels compared to retail buildings/malls.
Hospitals, with the second highest overall mean ranking within this group, have the highest mean rank on the installation of much electro-mechanical equipment as part of the scope (parameter 14), scope, complexity, and intelligence of Building Management System (BMS) (parameter 16), the scope of integration represented by the number of sub-components of the project (parameter 17), and the stringency of development control regulations and codal compliance (parameter 20). While the first three parameters are due to the criticality of the hospital facilities, the fourth parameter above was high due to the criticality of the nature of services, viz., medical and health.
Hospitals scored very close to hotels on the level and intensity of safety and security as part of the design and scope (parameter 15), which is likely due to the volume of people they handle. However, they scored lower (third rank) on occupancy density per 1000 square feet of usable area (parameter 10), number of parking spaces planned for every 1000 SFT of usable area (parameter 11), and vertical transportation logistics complexity (parameter 12) as they have moderate density. Hospitals scored moderate-to-low on the scope of amenities within the project (parameter 13), the typical floor plate size of a standard building (parameter 18), and the general range of the building height (parameter 19). It is interesting to note that they scored moderately both on floor plate and height, meaning that the size of the facilities considered for evaluation is not large hospitals.
The ranking of the hotels was close to retail buildings/malls and hospitals in terms of the overall mean ranking of all the above parameters. While they score the highest in the level and intensity of safety and security as part of the design scope (parameter 15) and second highest in the general range of the building height (parameter 19), they scored low on occupancy density per 1000 SFT (parameter10), typical floor plate size of a standard building (parameter 18), and the number of parking spaces planned for every 1000 SFT of usable area (parameter 11).
The category of high-rise residential buildings scored high in the general range of building height (parameter 19). It also scores a moderate mean rank on the scope of amenities within the project (parameter 13) and the level and intensity of safety and security as part of the project design and scope (parameter 15). Its mean ranking on all the other parameters is lower than the midpoint. Therefore, we can safely conclude that the residential projects are not generally complex due to the volume of facilities. The most surprising is the moderate ranking on the scope of amenities within the project (parameter 13), which needs to be explored further.
The category of educational institutions scored highest in occupancy density per 1000 square feet of usable area (parameter 10), moderate in the scope of amenities within the project (parameter 13), and typical floor plate size of a standard building (Parameter 18). general offices scored second highest mean rank in the number of parking spaces planned for every 1000 SFT of usable area (parameter 11). Surprisingly, they do not score high on the general range of building height (parameter 19) or the typical floor plate size of a standard building (parameter 18). Warehouses, like before, scored the low mean rank in almost all parameters except in the typical floor plate size of a standard building (parameter 18), where they score the second highest rank, making them the least complex on the volume of the facility and its components.
4.2.4. Complexity Due to the End-User Influence on Design
Five parameters have been used to assess the complexity due to end user attributes, influence, and powers. The Kruskal-Wallis Test was applied to the data, and the results are shown in
Figure 5 and
Table 9 (For complete data, refer to
Appendix F).
On end-user influence on design, sensitiveness of the end users to ESG Compliance in the operation phase (parameter 21), prevalence of end users interference in the design and scope (parameter 22), prevalence of “built to suit” and customization for the end users (parameter 23) and critical nature of considerations for ergonomics in operations, in the design (parameter 24) were evaluated apart from the type of client (parameter 25). The facility users, such as hotel guests in the case of hotels, shoppers in the case of Retail buildings/malls, and students in the case of educational institutions, will not have much of a say as they enter the operation stage. So, end users in these facilities are assumed to be the entities owning and operating the facility, which needs validation.
If we exclude the type of client for whom the facility is developed (Parameter 25), viz. Business to Govt (B2G), Business to Business (B2B), or Business to Consumer (B2C), from the evaluation, the results show hospitals are highly sensitive to end-user requirements on all four parameters. This is followed by high-rise residential buildings, hotels, retail buildings/malls, and educational institutions. The complexity is always higher where there is client intervention in the design and are sensitive to the final product. Regarding the prevalence of end-user interference in the design and scope (parameter 22), high-rise residential buildings and hospitals are almost at par. Regarding the prevalence of built-to-suit and customization for the end users (parameter 23), Hospitals score the highest on the mean rank score, followed by high-rise residential buildings and retail buildings/malls. Based on the above, excluding parameter 25, we can conclude that hospitals, followed by high-rise residential buildings, are more complex due to the influence of the end users.
For the type of business, the median is 2 and 3 in all cases, indicating these are business to consumer or business to a combination of business and consumer. So, there is not much of an impact on the complexity due to the type of business. In the case of whether the end-user is sensitive to ESG compliance in the operation phase (parameter 21), except for warehouses, the median of all the facilities is four, which indicates that they somewhat agree with the statement, and this is a positive trend towards sustainable operations.
4.2.5. Complexity Due to Construction Management Factors
Six parameters were evaluated under the complexity criteria due to construction management factors. The results of applying the Kruskal-Wallis Test to the survey data are shown in
Figure 6 and
Table 10 (For complete data, refer to
Appendix G).
As per the data above, only on time urgency for development (Parameter 30) does the data look skewed in favor of high-rise residential buildings, followed by retail buildings/malls, and hotels score the lowest mean rank under this parameter. Hospitals collectively score the highest mean rank on all the other parameters, followed by retail buildings/malls and hotels. High-rise residential buildings and educational institutions are much lower than the above three facilities, followed by general offices and warehouses. Regarding the ease of obtaining government approval (sanction of permit) (parameter 26), Hospitals stand way ahead in terms of their difficulty level compared to all other facilities, followed by hotels. On average of time (in years) of development of a typical project (parameter 27), hospitals, hotels, and retail buildings/malls are almost at par on the mean score ranking, with the Median score being 1–3 years (hospitals) and 3–5 years (hotels and retail buildings/malls). Regarding the complexity of project management during execution (parameter 28) and the number of specialist contractors required for the execution of the project (parameter 31), hospitals score way ahead of all other buildings, indicating the overall complexity of the execution. Regarding the risks associated with execution impacting time, cost, and quality (parameter 29), retail buildings/malls score the highest mean rank, closely followed by hospitals and hotels, which are almost at par. So, on the complexity level due to construction management factors, based on mean ranks, hospitals have the maximum complexity, followed by retail buildings/malls and hotels.
4.2.6. Complexity Due to Operational Attributes
Three parameters are under the complexity due to operational attributes. The results based on applying the Kruskal-Wallis Test are given in
Figure 7 and
Table 11. (For complete data, refer to
Appendix H).
Based on the assessment of the results from the Kruskal-Wallis Test and the mean ranks under all the parameters of operational attributes, hospitals score the highest, indicating relatively higher complexity during the operation phase. Hospitals score the highest mean rank in the complexity of operations and maintenance of the facility after completion (parameter 32) and power consumption, connected load, and criticality of backup power (parameter 34). However, on traffic intensity during operations/while in use (parameter 33), retail buildings/malls score higher than hospitals.
This can be corroborated by the fact that Hospitals are very sensitive to operations due to medical services, complicated electro-mechanical systems, and the quality of services required to be maintained 24/7. This is followed very closely by retail buildings/malls and hotels due to the volume of facilities and traffic. Educational institutions and general offices score over the high-rise residential and warehouses score the least in mean ranking, indicating the least complex among all the facilities.