Exploring Key Quality Indicators (KQIS) in Green Office Buildings Through Pareto Analysis

In parallel with the increasing environmental movement in the 1990s, the building industry has mostly focused on green solutions, since it has an important share around the world in terms of total final energy use and carbon emission [1, 2]. Green buildings have tremendously grown over the years following their proven benefits such as lower energy consumption. However, providing higher user satisfaction in green buildings is a burning issue in scholarly research and practice [3,4,5,6,7,8]. This means that integrating user feedback into the life cycle process of green projects is considered a problem in the green building industry. This problem, which can also be defined as a quality performance gap, manifests itself largely in the operational phase of buildings. The quality of a green building is the degree to which green solutions for their occupants increase the probability of desired satisfaction outcomes and must be consistent with the goals of energy performance and sustainability.

Based on the insights from the scholarly studies, more efforts should be devoted to real building case studies that analyse the actual operating performance of green buildings. Assessing the real performance of green buildings can help diagnose quality indicators and motives buildings to be performed at a higher level of sustainability, including both energy performance and quality performance [6, 9, 10]. Due to user satisfaction as a proxy of perceived quality, it is clear that green buildings, which are generally characterized by energy performance indicators, are expected to stand out with their quality indicators as well.

Intensive discussions in the green office building literature mostly point out that the quality of green buildings is not performed as well as they were designed and also call attention to the fact that only design-oriented analyses are not sufficient to achieve the sustainability goals. In recent years, the rising trend of research on user satisfaction in green office buildings has been focused on determining the quality performance emerging between what is planned during the design phase and what is noticed during the operational phase. The concept of satisfaction emerges as the result of meeting user requirements. However, user satisfaction depends on numerous factors. Bordass et al. (2001) point out that users’ perception of environmental comfort and their control over environmental conditions affect satisfaction [11]. Users' perceptions of satisfaction can also depend on the sub-factors, such as indoor temperature, air quality etc. [12]. Frontczak et al. (2012) reported that features such as the quality of the indoor environment, aesthetics, furniture and cleaning are associated with the satisfaction of users in green office buildings [13]. Previous research has also highlighted the concept of satisfaction concerning occupant health and comfort. Factors such as thermal condition, temperature, humidity, visual, acoustic, noise condition and air quality have also been associated with user satisfaction and thus with health and comfort [10, 14, 15, 16]. It comes into sight that these factors have a significant effect on user satisfaction in office buildings, and can be also referred to as quality indicators.

With their study conducted on 15 green office buildings, Lee (2011) aimed to reveal whether the satisfaction criteria in indoor environments were met and thus focused on the relationship between users' overall satisfaction levels and their job performance levels [17]. Gou et al. (2012a) showed that green building users are generally more satisfied than non-green building users, but less satisfied with conditions affecting indoor quality such as temperature and noise [18]. Altomonte and Schiavon (2013) surveyed the users of 65 LEED-certified office buildings and 79 non-green office buildings to measure user satisfaction. They concluded that air quality and the amount of light affect user satisfaction [4]. Altomonte et al. (2019) examined the relationship between the scores of 93 office buildings with a green building certification in the indoor environmental quality category and the users' satisfaction levels in their indoor environments [19]. Pastore and Andersen (2019) more specifically determined the users' satisfaction levels in four green office buildings with a Minergie certification in Switzerland [7]. Most research in the built environment literature focus on LEED, BREEAM, and Green-Star certified office buildings. More recently, despite the growing interest, only a few studies have investigated the quality effectiveness of the Living Building Challenge and WELL-certified buildings. Clements (2018) pointed out the acoustical quality of buildings designed by the Living Building Challenge Design standard and WELL certification criteria [20]. Licina and Langer (2021), Licina and Yildirim (2021), and Ildiri et al. (2022) compared non-WELL-certified and WELL-certified buildings [21, 22, 23]. They concluded that WELL-certified buildings positively impact particularly perceived health and well-being.

Studies show that users' overall satisfaction in green office buildings is higher than in conventional buildings. But, it is still uncertain what are the prominent quality indicators that affect user satisfaction in green office buildings. To fill the research gap mentioned above, by considering the central role of quality indicators in ensuring desired user satisfaction, this study aims to achieve the following research objectives: –

Examining the user/occupant satisfaction literature on green office buildings.

In the context of quality and sustainability, the main focus point of this study is to find out key quality indicators (kQIs) in evaluating user satisfaction during the operational stage of green office buildings. Through a systematic review of case studies and the Pareto analysis, the present study attempts to answer the following research questions: (1) What quality indicators have been used in scholarly journal articles that empirically evaluate user satisfaction by taking into account real data concerning the sustainability solutions of green office buildings? (2) Which quality indicators can be identified as vital indicators for more correctly evaluating the satisfaction of green office building users?

The remain of the study is organized as follows: following the introduction, the research approach is explained in the research methodology section. The main findings obtained from the systematic case study review and Pareto analysis are presented and discussed in the results and discussion section. Finally, concluding remarks, limitations of the study, and some recommendations for further analyses are provided in the last section of the study.

The research method is based on both qualitative and quantitative analyses. The present study uses a systematic case study review and Pareto analysis as a methodological approach (exploratory and descriptive). Based on the literature dataset in which research articles focused on user satisfaction evaluation with real case studies, an exploratory research approach was used to identify quality indicators. A systematic case study review has the potential to provide lessons from real-world applications [24] for shedding light on the future development of the green building industry. Since a case study refers to an empirical study supported by real data [24, 25], it can provide a more reliable research platform and practice-based evidence for exploratory research. Furthermore, it can play a significant role in gaining insight into how change occurs over time as a source of knowledge. Therefore, a systematic review of case studies in scholarly research can offer an opportunity for exploring quality indicators. For this reason, in this study, quality indicators linked with user satisfaction in green office buildings were first identified through a systematic review of case studies in the related literature.

In the second step of the research methodology, as a descriptive research approach, the Pareto analysis was applied to identify key quality indicators (kQIs) according to ranking through their frequency occurrences in the case studies analysed within the scope of this paper. The Pareto principle put forth by Italian economist and sociologist Vilfredo Pareto revealed that 80 per cent of Italy's wealth is derived from 20 per cent of the total population. This is also known as the 80/20 rule [26]. Moreover, the Pareto analysis is a quality control tool that helps sort the data affecting an event in descending order from the highest frequency of occurrence to the lowest ones. In this way, the Pareto principle helps a better understanding of the distinction between “vital few” and “useful many”. “Vital few” accounts for an important rate of cumulative events (80%) while useful many cover the remaining 20% [27].

As the primary step of the research approach in this study, a systematic case study review was conducted to compose the literature dataset. For the correct selection of published articles, the following inclusion/exclusion criteria were used in the screening process of the related literature.

Considering the selection criteria mentioned above, out of 972 screened articles at the onset of the review process, only 34 were really described as green office building case studies. The common focus point of these case studies is mainly evaluating perceived quality by considering user experiences in green-certified office buildings and discussing results by supporting them with real data. For this reason, based on the search string results used for Scopus, the literature dataset in this study was formed of 34 research articles for further analysis. Each of the selected articles is shown in Table 1 according to the location of the building, green building status/certification status, the number of buildings, and the case analysis method.

3.1.

Quality indicators

The focus point of this study is on determining the key quality indicators (kQIs) affecting user satisfaction in green office buildings. To that end, a detailed investigation of case studies conducted in the reviewed 34 articles given in Table 1 is required for capturing current knowledge which is to be a base to develop a set of quality indicators for green office buildings. While analysing the literature dataset presented in Table 1, it was focused on case studies evaluating user/occupant satisfaction in green office buildings in order to reveal what the quality indicators are. The authors performed a more detailed examination of each green building case analysis, based on which was selected. Thereby, all quality indicators were compiled from the selected 34 research articles. As a result of that, a total of 37 quality indicators were identified. Following that, we systematically categorized these quality indicators for inclusion in the Pareto analysis.

The 37 quality indicators (QIs) were categorized into six main groups as illustrated in Figure 1. The categorization of these 37 QIs into main groups as well as subgroups was made based on the descriptive approach. As seen in Table 2, to prove the validity of the determined 37 QIs, they were itemized in a systematic manner according to their references indicated in the literature dataset formed in Table 1 within the scope of this study.

Figure 1.

Table 2.

Categorization of 37 quality indicators (QIs) for green office buildings [Elaborated by the authors]

Quality Indicators	Sources
INDOOR ENVIRONMENTAL QUALITY
Indoor Thermal Quality
Overall perceived thermal quality	Geng et al., 2020; Kim & Kim, 2020; Altomonte et al., 2019; Lee et al., 2019; Khoshbakht et al., 2018; Sant’Anna et al., 2018; Altomonte et al., 2017; Fieldson&Sodagar, 2017; Lin et al., 2016; Sediso&Lee, 2016; Pei et al., 2015; Gou et al., 2014; Liang et al., 2014; Schiavon&Altomonte, 2014; Altomonte&Schiavon, 2013; Gou et al., 2013; Gou&Lau, 2013; Menadue et al., 2013; Newsham et al., 2013; Deuble&de Dear, 2012; Frontczak et al., 2012; Gou et al., 2012a; Gou et al., 2012b; Gou et al., 2012c; Hodgson, 2008; Lee, 2011; Monfared&Sharples, 2011; Brown et al., 2010; Lee&Guerin., 2010.
Temperature in winter	Pastore&Andersen, 2019; Sant’Anna et al., 2018; Khoshbakht et al., 2018; Liu et al., 2018; Fieldson&Sodagar, 2017; Pei et al., 2015; Gou et al., 2014; Agha-Hossein et al., 2013; Gou et al., 2013; Gou&Lau, 2013; Menadue et al., 2013; Gou et al., 2012a; Gou et al., 2012b; Gou et al., 2012c; Armitage et al., 2011; Brown et al., 2010.
Temperature in summer	Pastore&Andersen, 2019; Sant’Anna et al., 2018; Khoshbakht et al., 2018; Liu et al., 2018; Fieldson&Sodagar, 2017; Pei et al., 2015; Gou et al., 2014; Agha-Hossein et al., 2013; Gou et al., 2013; Gou&Lau, 2013; Menadue et al., 2013; Gou et al., 2012a; Gou et al., 2012b; Gou et al., 2012c; Armitage et al., 2011; Brown et al., 2010.
Temperature in transition seasons	Liu et al., 2018; Sant’Anna et al., 2018; Fieldson&Sodagar, 2017; Pei et al., 2015.
Temperature stability in winter	Khoshbakht et al., 2018; Gou & Lau, 2013; Menadue et al., 2013; Armitage et al., 2011.
Temperature stability in summer	Khoshbakht et al., 2018; Gou & Lau, 2013; Menadue et al., 2013; Armitage et al., 2011.
Humidity	Geng et al., 2020; Lee et al., 2019; Liu et al., 2018; Sant’Anna et al., 2018; Fieldson&Sodagar, 2017; Liang et al., 2014; Menadue et al., 2013; Armitage et al., 2011; Monfared&Sharples, 2011.
Indoor Air Quality
Overall perceived air quality(air cleanliness, freshness, dryness, odours etc.)	Geng et al., 2020; Kim&Kim, 2020; Altomonte et al., 2019; Lee et al., 2019; Pastore&Andersen, 2019; Khoshbakht et al., 2018; Liu et al., 2018; Sant’Anna et al., 2018; Altomonte et al., 2017; Fieldson&Sodagar, 2017; Lin et al., 2016; Sediso&Lee, 2016; Pei et al., 2015; Liang et al., 2014; Schiavon&Altomonte, 2014; Altomonte&Schiavon 2013; Agha-Hossein et al., 2013; Menadue et al., 2013; Newsham et al., 2013; Deuble&de Dear, 2012; Frontczak et al., 2012; Armitage et al., 2011; Hodgson, 2008; Lee, 2011; Monfared&Sharples, 2011; Brown et al., 2010; Lee&Guerin, 2010.
Air quality in winter	Gou et al., 2014; Gou et al., 2013; Menadue et al., 2013; Gou et al., 2012a; Gou et al., 2012b; Gou et al., 2012c; Lee, 2011; Brown et al., 2010.
Air quality in summer	Gou et al., 2014; Gou et al., 2013; Menadue et al., 2013; Gou et al., 2012a; Gou et al., 2012b; Gou et al., 2012c; Lee, 2011; Brown et al., 2010.
Air movement/ventilation	Kim&Kim, 2020; Altomonte et al., 2019; Lee et al., 2019; Khoshbakht et al., 2018; Sant’Anna et al., 2018; Fieldson&Sodagar, 2017.
Indoor Lighting Quality
Overall perceived lighting quality	Geng et al., 2020; Kim & Kim, 2020; Lee et al., 2019; Pastore&Andersen, 2019; Khoshbakht et al., 2018; Altomonte et al., 2017; Lin et al., 2016; Sediso&Lee, 2016; Pei et al., 2015; Liang et al., 2014; Schiavon&Altomonte, 2014; Altomonte&Schiavon 2013; Gou et al., 2013; Hirning et al., 2013; Newsham et al., 2013; Deuble&de Dear, 2012; Frontczak et al., 2012; Gou et al., 2012b; Gou et al., 2012c; Hodgson, 2008; Monfared&Sharples, 2011; Brown et al., 2010; Lee&Guerin., 2010.
Natural lighting (daylight)	Altomonte et al., 2019;Lee et al., 2019;Khoshbakht et al., 2018; Liu et al., 2018; Sant’Anna et al., 2018; Fieldson&Sodagar, 2017; Gou et al., 2014; Agha-Hossein et al., 2013; Altomonte &Schiavon 2013; Menadue et al., 2013; Gou et al., 2012a; Gou et al., 2012c; Armitage et al., 2011.
Artificial lighting	Altomonte et al., 2019; Lee et al., 2019; Khoshbakht et al., 2018; Liu et al., 2018; Sant’Anna et al., 2018; Fieldson&Sodagar, 2017; Gou et al., 2014; Agha-Hossein et al., 2013; Altomonte&Schiavon 2013; Menadue et al., 2013; Gou et al., 2012a; Gou et al., 2012c; Armitage et al., 2011.
Glare from natural lighting	Khoshbakht et al., 2018; Gou et al., 2014; Liang et al., 2014; Hirning et al., 2013; Menadue et al., 2013; Newsham et al., 2013; Armitage et al., 2011; Monfared&Sharples, 2011; Gou et al., 2012a; Gou et al., 2012c.
Glare from artificial light	Khoshbakht et al., 2018; Gou et al., 2014; Liang et al., 2014; Hirning et al., 2013; Menadue et al., 2013; Newsham et al., 2013; Armitage et al., 2011; Monfared&Sharples, 2011, Gou et al., 2012a; Gou et al., 2012c.
Indoor Acoustic Quality
Overall acoustic quality	Lee et al., 2019; Pastore&Andersen, 2019; Liu et al., 2018; Khoshbakht et al., 2018; Sant’Anna et al., 2018; Altomonte et al., 2017; Fieldson&Sodagar, 2017; Lin et al., 2016; Sediso&Lee, 2016; Pei et al., 2015; Schiavon&Altomonte, 2014; Agha-Hossein et al., 2013; Altomonte&Schiavon 2013; Gou et al., 2013; Menadue et al., 2013; Newsham et al.,2013; Gou et al., 2012c; Frontczak et al., 2012; Armitage et al., 2011; Hodgson, 2008; Brown et al., 2010.
Noise from inside	Khoshbakht et al., 2018; Gou et al., 2014; Liang et al., 2014; Gou et al., 2012a; Gou et al., 2012c; Hodgson, 2008.
Noise from colleagues	Khoshbakht et al., 2018; Gou et al., 2014; Gou et al., 2012a; Gou et al., 2012c; Hodgson, 2008.
Noise from outside	Khoshbakht et al., 2018; Gou et al., 2014; Liang et al., 2014; Gou et al., 2012a; Gou et al., 2012c; Hodgson, 2008.
Unwanted interruptions	Khoshbakht et al., 2018; Gou et al., 2014; Menadue et al., 2013; Gou et al., 2012a; Gou et al., 2012c; Hodgson, 2008.
PERSONAL CONTROL
Personal control on temperature	Geng et al., 2020; Altomonte et al., 2019; Lee et al., 2019; Pastore&Andersen, 2019;Liu et al., 2018; Pei et al., 2015; Agha-Hossein et al., 2013; Deuble&de Dear, 2012; Gou et al., 2012a; Gou et al., 2012b; Armitage et al., 2011; Monfared&Sharples, 2011, Brown et al., 2010; Lee&Guerin., 2010.
Personal control on lighting	Geng et al., 2020; Lee et al., 2019; Pastore&Andersen, 2019; Agha-Hossein et al., 2013; Gou et al., 2012; Armitage et al., 2011; Monfared&Sharples, 2011; Brown et al., 2010; Lee&Guerin., 2010.
Personal control on ventilation (in terms of natural, mechanical or mixed ventilation systems)	Geng et al., 2020; Kim&Kim, 2020; Lee et al., 2019; Pastore&Andersen, 2019; Deuble&de Dear, 2012; Gou et al., 2012a; Gou et al., 2012b; Armitage et al., 2011; Monfared&Sharples, 2011; Brown et al., 2010; Lee&Guerin, 2010.
Personal control on noise	Pastore&Andersen, 2019; Agha-Hossein et al., 2013; Gou et al., 2012; Armitage et al., 2011; Brown et al., 2010.
WORKSPACE DESIGN QUALITY
Spatial layout/Office type (Enclosed-private, enclosed-shared, cubicles, open office-no partitions)	Lee et al., 2019; Liu et al., 2018; Sant’Anna et al., 2018; Altomonte et al., 2017; Lin et al., 2016; Sediso&Lee, 2016; Pei et al., 2015; Schiavon&Altomonte, 2014; Agha-Hossein et al., 2013; Monfared&Sharples, 2011; Hodgson, 2008; Brown et al., 2010.
The amount of space and area per person	Liu et al., 2018; Sant’Anna et al., 2018; Altomonte et al., 2017; Lin et al., 2016; Sediso&Lee, 2016; Schiavon&Altomonte, 2014; Monfared&Sharples, 2011; Brown et al., 2010.
Ease of Interaction	Liu et al., 2018; Altomonte et al., 2017; Lin et al., 2016; Schiavon&Altomonte, 2014; Agha-Hossein et al., 2013; Frontczak et al., 2012”
View to the outside	Pastore&Andersen, 2019; Sant’Anna et al., 2018; Sediso&Lee, 2016; Agha-Hossein et al., 2013.
Visual privacy	Lee et al., 2019; Liu et al., 2018; Sant’Anna et al., 2018; Altomonte et al., 2017; Lin et al., 2016; Pei et al., 2015; Schiavon&Altomonte, 2014; Agha-Hossein et al., 2013; Altomonte&Schiavon 2013; Frontczak et al., 2012; Armitage et al., 2011; Monfared &Sharples, 2011, Brown et al., 2010.
Sound privacy	Lee et al., 2019; Altomonte et al., 2017; Schiavon&Altomonte, 2014; Agha-Hossein et al., 2013; Altomonte&Schiavon, 2013; Frontczak et al., 2012; Armitage et al., 2011; Hodgson, 2008; Monfared&Sharples, 2011; Brown et al., 2010.
Colours and textures	Lee et al., 2019; Liu et al., 2018; Altomonte et al., 2017; Lin et al., 2016; Schiavon&Altomonte, 2014; Agha-Hossein et al., 2013; Frontczak et al., 2012; Monfared&Sharples, 2011; Brown et al., 2010.
Comfort of furnishing	Khoshbakht et al., 2018; Liu et al., 2018; Sant’Anna et al., 2018; Lin et al., 2016; Sediso&Lee, 2016; Schiavon&Altomonte, 2014; Agha-Hossein et al., 2013; Frontczak et al., 2012; Hodgson, 2008; Monfared&Sharples, 2011; Brown et al., 2010.
PERCEIVED HEALTH
The perceived feeling of health and wellbeing	Geng et al., 2020; Lee et al., 2019; Pastore&Andersen, 2019; Khoshbakht et al., 2018; Liu et al., 2018; Pei et al., 2015; Gou et al., 2014; Schiavon&Altomonte, 2014; Agha-Hossein et al., 2013; Gou et al., 2013; Menadue et al., 2013; Deuble&de Dear, 2012; Gou et al., 2012a; Gou et al., 2012b; Armitage et al., 2011; Lee, 2011; Brown et al., 2010; Lee&Guerin., 2010.
PERCEIVED PRODUCTIVITY
The perceived feeling of productivity (job performance)	Geng et al., 2020; Lee et al., 2019; Pastore&Andersen, 2019; Khoshbakht et al., 2018; Liu et al., 2018; Pei et al., 2015; Gou et al., 2014; Schiavon&Altomonte, 2014; Agha-Hossein et al., 2013; Gou et al., 2013; Menadue et al., 2013; Deuble&de Dear, 2012; Gou et al., 2012a; Gou et al., 2012b; Armitage et al., 2011; Lee, 2011; Brown et al., 2010; Lee&Guerin., 2010.
FACILITY MANAGEMENT
Operating &Maintenance	Liu et al., 2018; Sant’Anna et al., 2018; Sediso&Lee, 2016; Pei et al., 2015; Schiavon&Altomonte, 2014; Frontczak et al., 2012; Hodgson, 2008.
Building cleanliness	Altomonte et al., 2019; Liu et al., 2018; Khoshbakht et al., 2018; Sant’Anna et al., 2018; Altomonte et al., 2017; Lin et al., 2016; Sediso&Lee, 2016; Pei et al., 2015; Schiavon& Altomonte, 2014; Altomonte&Schiavon, 2013; Frontczak et al., 2012; Hodgson, 2008.

3.2.

Pareto analysis of quality indicators

In this study, the Pareto approach was used to see what quality indicators (QIs) come into prominence and which have potential importance in evaluating user satisfaction in green office buildings. The Pareto analysis for 37 QIs was performed. The results obtained from the analysis are presented and discussed below.

The Pareto analysis requires principally sorting the data in descending order to the frequency percentage of their occurrences. In this study, as a first step of the Pareto analysis, the frequency of each quality indicator was determined by considering the number of its occurrences in the literature dataset consisting of case studies evaluating user/occupant satisfaction in green office buildings. The frequency distributions of 37 quality indicators (QIs) are established as shown in Table 3. The total number of QIs identified from the whole of the 34 research articles is 37 with a total frequency occurrences of 411.

Table 3.

The frequency distributions of 37 quality indicators (QIs) [Elaborated by the authors]

Code	Main Groups of Quality Indicators (QIs)		Quality Indicators (QIs)	Personal control on ventilation
QI-1	INDOOR ENVIRONMENTAL QUALITY	Indoor Thermal Quality	Overall perceived thermal quality	29
QI-2			Temperature in winter	16
QI-3			Temperature in summer	16
QI-4			Temperature in transition seasons	4
QI-5			Temperature stability in winter	4
QI-6			Temperature stability in summer	4
QI-7			Humidity	9
QI-8		Indoor Air Quality	Overall perceived air quality	27
QI-9			Air quality in winter	8
QI-10			Air quality in summer	8
QI-11			Air movement/ventilation	6
QI-12		Indoor Lighting Quality	Overall perceived lighting quality	23
QI-13			Natural lighting (daylight)	13
QI-14			Artificial lighting	13
QI-15			Glare from natural lighting	10
QI-16			Glare from artificial light	10
QI-17		Indoor Acoustic Quality	Overall acoustic quality	21
QI-18			Noise from inside	6
QI-19			Noise from colleagues	5
QI-20			Noise from outside	6
QI-21			Unwanted interruptions	6
QI-22	CONTROL PERSONAL		Personal control on temperature	14
QI-23			Personal control on lighting	9
QI-24			Personal control on ventilation	11
QI-25			Personal control on noise	5
QI-26	WORKSPACE DESIGN QUALITY		Spatial layout / Office type	12
QI-27			The amount of space and area per person	8
QI-28			Ease of Interaction	6
QI-29			View to the outside	4
QI-30			Visual privacy	13
QI-31			Sound privacy	10
QI-32			Colors and textures	9
QI-33			Comfort of furnishing	11
QI-34	PERCEIVED HEALTH		The perceived feeling of health and wellbeing	18
QI-35	PERCEIVED PRODUCTIVITY		The perceived feeling of productivity	18
QI-36	FACILITY MANAGEMENT		Operating & Maintenance	7
QI-37			Building cleanliness	12
			∑=	411

The Pareto analysis for all 37 QIs was conducted to search out the vital or useful status of each QI and to clearly distinguish key QIs. As the second step of Pareto analysis, it was calculated the percentage of occurrences and the cumulative percentage of occurrences for 37 QIs. The results gathered from this stage of the Pareto analysis of 37 QIs are given in Table 4.

Table 4.

Pareto analysis of all 37 Quality Indicators (QIs) [Elaborated by the authors]

	Code	Quality Indicators (QIs)	Count of occurrences (Frequency)(n)	Percentage of occurrences (%)	Cumulative percentage of occurrences (%)
Vital few (key Quality Indicators-kQIs)	QI-1	Overall perceived thermal quality	29	7.06%	7.06%
	QI-8	Overall perceived air quality	27	6.57%	13.63%
	QI-12	Overall perceived lighting quality	23	5.60%	19.23%
	QI-17	Overall acoustic quality	21	5.11%	24.34%
	QI-34	The perceived feeling of health and wellbeing	18	4.38%	28.72%
	QI-35	The perceived feeling of productivity	18	4.38%	33.10%
	QI-2	Temperature in winter	16	3.89%	36.99%
	QI-3	Temperature in summer	16	3.89%	40.88%
	QI-22	Personal control on temperature	14	3.41%	44.29%
	QI-13	Natural lighting (daylight)	13	3.16%	47.45%
	QI-14	Artificial lighting	13	3.16%	50.61%
	QI-30	Visual privacy	13	3.16%	53.77%
	QI-26	Spatial layout/Office type	12	2.92%	56.69%
	QI-37	Building cleanliness	12	2.92%	59.61%
	QI-24	Personal control on ventilation	11	2.68%	62.29%
	QI-33	Comfort of furnishing	11	2.68%	64.97%
	QI-15	Glare from natural lighting	10	2.43%	67.40%
	QI-16	Glare from artificial light	10	2.43%	69.83%
	QI-31	Sound privacy	10	2.43%	72.26%
	QI-7	Humidity	9	2.19%	74.45%
	QI-23	Personal control on lighting	9	2.19%	76.64%
	QI-32	Colors and textures	9	2.19%	78.83%
	QI-27	The amount of space and area per person	8	1.95%	80.78%
Useful many	QI-9	Air quality in winter	8	1.95%	82.73%
	QI-10	Air quality in summer	8	1.95%	84.68%
	QI-36	Operating & Maintenance	7	1.70%	86.38%
	QI-11	Air movement/ventilation	6	1.46%	87.84%
	QI-18	Noise from inside	6	1.46%	89.30%
	QI-20	Noise from outside	6	1.46%	90.76%
	QI-21	Unwanted interruptions	6	1.46%	92.22%
	QI-28	Ease of Interaction	6	1.46%	93.68%
	QI-19	Noise from colleagues	5	1.22%	94.90%
	QI-25	Personal control on noise	5	1.22%	96.12%
	QI-4	Temperature in transition seasons	4	0.97%	97.09%
	QI-5	Temperature stability in winter	4	0.97%	98.06%
	QI-6	Temperature stability in summer	4	0.97%	99.03%
	QI-29	View to the outside	4	0.97%	100%
		∑=	411	100%

The final step of the Pareto application requires generating a Pareto chart. The graphical presentation peculiar to the Pareto analysis ensures an understanding of the extent to which the listed items are prioritized based on the data defined in a hierarchical order. A vertical bar chart format sorts data from highest to lowest. In other words, columns represent the structural distribution of data in the graphical representation. The Pareto chart consists of an X-axis and two Y axes. The X-axis expresses what the data is. The left vertical axis represents the absolute value of the data, while the right vertical axis represents its values as a cumulative percentage. The second Y-axis provides the formation of the cumulative curve in the graph. The cumulative curve created based on the data is effective in interpreting the Pareto analysis results. The Pareto chart was generated for all 37 QIs, as graphically illustrated in Figure 2.

Figure 2.

The Pareto analysis makes the vital QIs reveal themselves in conjunction with both frequencies of occurrences and cumulative percentages. Indeed, the Pareto approach provides a leading way to identify current data and to see which of them might be precursors in continual quality improvement. In line with the Pareto rule, based on descending order of the frequency percentage, the ranking of the cumulative percentage unveils both the most frequent QIs and the least frequent QIs. For this reason, in this study, the Pareto analysis was performed to find out vital and useful quality indicators. As seen in Figure 2, the results of the Pareto analysis made based on data given in Table 4 show that the most frequent QIs are “vital” indicators with 80.78%, while the remaining 19.22% represent the “useful” QIs.

In this study, a total of 34 research articles that evaluated user satisfaction in green office buildings based on real data were reviewed. 37 quality indicators were identified from the case study review. These indicators, in descending order based on their percentage of occurrence, are ranked, and Pareto analysis is applied. The most notable feature of the analysis is that the Pareto chart, which is also expressed as a quality tool, can be used to show how many times the data occurred by prioritizing in a hierarchical order. According to the Pareto approach, while the “vital few” items comprise a significant percentage of the cumulative occurrences (80 per cent), the “useful many” are attributed to only the remaining per cent (20 per cent) of the occurrences. In this sense, “vital few” items can be defined as key quality indicators (kQIs), whereas “useful many” items can represent potential ones that are likely to be significant indicators in the future. As a result of the Pareto analysis for 37 QIs, the “vital few” (80.78 %) group consists of 23 key quality indicators, while the remaining 14 (19.22 %) indicators are determined as the “useful many” group. This result means that 23 vital few quality indicators were found to be responsible for a greater effect than many other 14 useful quality indicators. Of 23 vital few quality indicators, the ten most prominent key quality indicators respectively are as follows: “QI-1:Overall perceived thermal quality”, “QI-8: Overall perceived air quality”, “QI-12: Overall perceived lighting quality”, “QI-17: Overall acoustic quality”, “QI-34- The perceived feeling of health and wellbeing”, “QI-35: The perceived feeling of productivity”, “Q-I2: Temperature in winter”, “Q-I3: Temperature in summer”, “QI-22-Personal control on temperature”, “QI-13-Natural lighting (daylight)”. Overall perceived thermal quality was found to be the most prominent quality indicator, with the frequency of occurrence 29 times.

This study fills the gap in the quality improvement of green office buildings. It provides an insight on key quality indicators to a certain extent for more accurately increasing user satisfaction in green office buildings, but it still has some limitations. For example, this study focused on finding out key quality indicators for only green office buildings, but there are limited researches which evaluate user satisfaction based on real data in green office buildings. For this reason, the impact of quality indicators on user satisfaction levels cannot be sufficiently extracted from the literature dataset.

Quality indicators worthy of consideration for enhancing user satisfaction can be explored for other types of green buildings through analysing case studies. In future research, further analyses need to explore quality indicators, and their effects on user satisfaction can also be extended to other types of buildings, such as residential buildings, healthcare buildings, educational buildings, etc.