ArticlePDF Available

Abstract and Figures

Purpose: Housekeeping is an important hotel process from the point-of-view of the number of work hours it takes and its impact on customer satisfaction. However, few previous scientific studies have addressed this topic or the variables that are determinants of the time required to clean a room. Design/methodology/approach: A stopwatch Time Study has been performed in a 4-star-hotel. Additionally, data on several variables that could affect cleaning time have been collected and subjected to regression analysis. Findings: Results show that only the task-related variables have a statistically significant influence on total cleaning time. None of the analyzed employee-related variables have any effect on cleaning time. Moreover, five tasks represent over 2/3 of the total cleaning time. Originality/value: In addition to empirically identifying the variables that influence cleaning time and to what extent, this study demonstrates the advantages of using stopwatch time studies to establish cleaning times.
Content may be subject to copyright.
Journal of Industrial Engineering and Management
JIEM, 2021 – 14(3): 645-660 – Online ISSN: 2013-0953 – Print ISSN: 2013-8423
Hotel Room Cleaning: Time Study and Analysis of Influential
Variables in a Spanish Hotel
Victor G. Aguilar-Escobar1 , Pedro Garrido-Vega1 , Julián Majado-Márquez2 ,
José Antonio Camuñez-Ruiz1
1Universidad de Sevilla, Departamento de Economía Financiera y Dirección de Operaciones, Facultad de Turismo y Finanzas (Spain)
2Universidad de Sevilla (Spain),,,
Received: December 2020
Accepted: June 2021
Housekeeping is an important hotel process from the point-of-view of the number of work
hours it takes and its impact on customer satisfaction. However, few previous scientific studies have
addressed this topic or the variables that are determinants of the time required to clean a room.
A stopwatch Time Study has been performed in a 4-star-hotel.
Additionally, data on several variables that could affect cleaning time have been collected and subjected to
regression analysis.
Results show that only the task-related variables have a statistically significant influence on total
cleaning time. None of the analyzed employee-related variables have any effect on cleaning time.
Moreover, five tasks represent over 2/3 of the total cleaning time.
In addition to empirically identifying the variables that influence cleaning time and to
what extent, this study demonstrates the advantages of using stopwatch time studies to establish cleaning
hotel management, housekeeping, room cleaning, time study, regression analysis
To cite this article:
Aguilar-Escobar, V.G., Garrido-Vega, P., Majado-Márquez, J., & Camuñez-Ruiz, J.A. (2021). Hotel room
cleaning: Time study and analysis of influential variables in a Spanish hotel. Journal of Industrial Engineering and
Management, 14(3), 645-660.
1. Introduction
The purpose of this paper is to study hotel room cleaning time and analyze the main variables that determine the
time required. Room cleaning is the responsibility of the hotel’s housekeeping department and numerous empirical
studies have proven the major importance of this department for customer satisfaction, perceived quality, and
loyalty. For example, in a study of four business hotels, Gundersen, Heide and Olsson (1996) found that tangible
aspects of the housekeeping department and intangible aspects of the reception department had the strongest
impact on overall satisfaction. Lockyer (2003) stated that many studies report strong evidence that cleanliness is a
Journal of Industrial Engineering and Management –
very important factor in the selection of accommodation. In a survey of three hotel chains, Hartline, Wooldridge
and Jones (2003) concluded that only front-desk and housekeeping performance have a positive and significant
impact on guests’ perceived service quality. Another survey by Lewis and McCann (2004) found that clean and
comfortable bedrooms are the leading service quality attribute for hotel guest respondents. Meanwhile, Pongsiri
(2012: page 346) concluded that it has been “clearly demonstrated that the expectation and importance of the
housekeeping service are related to customer satisfaction”. In an analysis of the factors that affect the quality of
service in Taiwan hotels, Chen and Chen (2014) found that cleanliness of both the rooms and the restaurants are
attributes of the quality of service and the positive perception of a hotel’s corporate image. Similarly, in their
analysis of 12 activities in hotels, Espino-Rodríguez and Ramírez-Fierro (2017) found that room cleaning can be
considered one of the core competencies due to its impact on competitive advantage. Robinson, Kralj, Solner, Goh
and Callan (2016) argued that, along with front office and food and beverage staff, being a housekeeping employee
can be considered a frontline occupation to a certain extent and that frontline staff play a key role in the success of
a service organization. A recent study by Hsieh and Chuang (2020) has shown that environmental quality is the
second key factor for service experience in hotels and that cleanliness comes first when customers evaluate the
quality of their service experience. In Spain, the hotel cleanliness is one of the seven requisites for obtaining the
ICTE (Institute for Spanish Tourist Quality) Q certificate, which is similar to ISO 9001 (Pereira-Moliner & Tarí,
2015; Nicolau & Sellers, 2011). Zemke, Neal, Shoemaker and Kirsch (2015) go one step further and propose that
hotel companies explore improving cleanliness as an amenity and consider the option of turning enhanced
cleanliness into a company strength and a marketing argument.
In terms of employment, the housekeeping department accounts for approximately 26% of all hotel employees
(Krause, Scherzer & Rugulies, 2005). More generally, cleaning activities account for more than 8% of all jobs in
France (Abasabanye, Bailly & Devetter, 2018) and over 4 billion euros are spent on cleaning every year in The
Netherlands alone (Vlijmen, 2019). Digging deeper into the importance of this activity, an estimate can be made of
the number of times that this task is performed per year. If on average 23.51 million rooms were offered per day
worldwide in 2015 (World Tourism Organization, 2017) with an average occupancy rate of 60.51% (ibid.), it can be
deduced that approximately 5,194 million rooms were cleaned in that year. If it is estimated that 0.5 hours is
required per room (including breaks, transportation, etc.), room cleaning generated a workload of approx. 2,597
million work hours worldwide in the said year. In contrast, if each of the 90,843,939 vehicles produced in the world
in 2015 (OICA - International Organization of Motor Vehicle Manufacturers, 2016) required an estimated average
of 20 hours for assembly, together they generated a workload of approx. 1,817 million work hours. However, while
it is possible to find a large number of studies in the academic literature that address industrial activities and the
time required to execute each of the tasks and sub-tasks of which a job is composed, there are hardly any that
address the activity of room cleaning and studies measuring the time involved are especially scarce.
Research on cleanliness in general, and on hotel room cleaning in particular, is quite sparse. A good part of the
literature that exists focuses on ethical or sociological aspects related to its consideration as “dirty work”, its social
invisibility, feminization of the sector, the role of immigrants, etc. (Vlijmen, 2019; Abasabanye et al., 2018;
Soni-Sinha & Yates, 2013; Onsøyen & Mykletun, 2009). Other studies focus on aspects of occupational health and
safety (Oxenbridge & Lindegaard-Moensted, 2011; Goggins, 2007; Krause et al., 2005; Zock, 2005) such as
occupational risks (musculoskeletal diseases, poisoning, falls, etc.). There are also works on cleaning from the
customers point of view, for example, on how it affects their satisfaction or service experience, what importance
they attach to it and how they value it, etc. (Manhas, 2015; Lockyer, 2003; Sherman, 2011). However, there are very
few studies that have focused on how to measure cleaning times and the variables that influence them. There are
some works that mention room cleaning times but without specifying how they are obtained, while others mention
factors that influence cleaning time but without empirically studying the relationship. To the best of our knowledge,
the only time study conducted to determine room cleaning time is Mehrez, Israeli and Haddad (2000), which
analyzes the case of a single hotel in Israel. This study can also be considered a precedent to Jones and Siag (2009),
although in this case the number of rooms per hour is used as a measure of productivity in a study of 45 hotels in
the United Kingdom owned by the same company. Therefore, given the limited number of previous studies and
having demonstrated the importance of hotel room cleaning, the present research study pursues a dual objective:
Journal of Industrial Engineering and Management –
1. To show how the stopwatch Time Study technique could be used to calculate room cleaning times and
what its advantages are,
2. To identify the variables that influence hotel room cleaning time.
The remainder of the paper is structured as follows. First, an overview is given of the previous research on the
topic and some hypotheses are proposed. Second, the methodology used to obtain time estimations and all the data
on cleaning are described and analysis techniques are explained. Next, the main results are presented, divided into
two subsections, one per each of the objectives. Finally, the results are discussed, and the main conclusions are
drawn, highlighting theoretical and managerial implications and the limitations of the study.
2. Background and Research Hypotheses
The housekeeping department’s most important activity is room cleaning. This is a repetitive task performed by
housekeepers/chambermaids. Despite its importance, room cleaning does not seem to have sparked much interest
in the research. This lack of interest also extends to the cleaning sector in general and, according to Vlijmen (2019),
the limited existing research on facility cleaning is focused on technical aspects, such as efficiency and cost
reduction, and the organization of cleaning. This may be due to the fact that cleaning does not enjoy the same
status in hotels as other activities (Boon, 2007); in fact, cleaners command little status in the eyes of managers and
fellow workers (Robinson et al. 2016). Also, cleaners in general are regularly subject to social invisibility and branded
by dirty work stigma (Vlijmen, 2019). Thus, several authors have shown that hotel housekeeping work is low paid
(Frumin, Moriarty, Vossenas, Halpin, Orris, Krause et al., 2006; Eriksson & Li, 2009), while others emphasize that it
is a job with significant occupational risks (Krause et al., 2005; Frumin et al., 2006). Additionally, the work has been
subject to considerable pressure and stress in recent years since, as a consequence of certain business strategies, the
“numbers of operations to be completed, the numbers and weights of items to be cleaned, and the effort involved”
have all increased, while “flexible employment relationships and outsourcing have also worsened cleaners’
workloads” (Seifert & Messing, 2006: page 557). From a gender point-of-view, “research has identified a clear
segmentation of men’s and women’s work in tourism, showing how the majority of women’s work is concentrated
in seasonal, part-time, and low-paid activities such as retail, hospitality, and cleaning” (Ferguson, 2011: page 237).
Onsøyen and Mykletun (2009) highlight that the main problems experienced at work by hotel room-attendants in
Norway include time pressure, a sense of being under close and negative supervision, being undervalued at work,
and not being involved in relevant decision-making.
Regarding cleaning times, some professional studies have been conducted in the hotel sector. Siguaw and Enz
(1999) cite the case of the Grand Theme Hotels, where a time study was conducted of each of the main hotel
activities (e.g., check-in, cleaning an occupied room, serving a meal). Heath (2016) reports a self-conducted
time-and-motion study for one of his clients with suite rooms. Other studies offer cleaning time data but do not
state how they have been obtained. For example, Falbo (1999) gathers different experiences to improve productivity
and cleaning quality in hotels, with times per room provided for some hotels. Seifert and Messing (2006) conduct
two ergonomic case studies of cleaning in hotels in Montreal, Canada. Krause et al. (2005) also obtain cleaning
times from a survey of 941 unionized hotel room cleaners. Onsøyen and Mykletun (2009) mention a time of 20-30
minutes servicing per room for the room-attendants in four chain-affiliated hotels in Norway. In another part of
the paper, they also indicate that each room-attendant was usually expected to clean approx. 17 rooms in a
7-7.5-hour shift, but this number could occasionally rise to 35 rooms on the most hectic days. In an analysis of
customer profitability, Dalci, Tanis and Kosan (2010) consider the time spent on different housekeeping activities
for various types of hotel customer but do not indicate how it is obtained. Sherman (2011) comments that room
cleaners are required to clean 12 rooms per shift at two American luxury urban hotels. All the cleaning times from
these preceding studies have been included in Table 1.
Other studies report the number of rooms cleaned per hour. Kirwin (1990) indicates an increase in productivity of
2.5 to 3 rooms per hour up from 2.25 in the previous data. Jones and Siag (2009) provide data for 2.27 rooms
cleaned per hour in a productivity study for a chain of 45 hotels in the United Kingdom. As mentioned previously,
Mehrez et al. (2000) analyze the case of a single hotel in Israel and its average cleaning time data are also included in
Table 1.
Journal of Industrial Engineering and Management –
Data collection method
and sample size
Hotel or Chain
Room cleaning time
in minutes
No. rooms per
housekeeper per day
Falbo, 1999 No data 5 hotels in USA 20, 18-20, 20-25, 27,
and 20
Mehrez et al., 2000 Observation of 175 rooms One Hotel in
24.31 to
24.81(stayover) and
42.23 to 44 (checkout)
Krause et al., 2005 Survey of 941 room cleaners 5 hotels in USA - 15.3
Seifert & Messing,
Observation of 18 room
cleaners for 90 hours
2 large hotels in
Montreal (Canada)
26-28 (Ranked from
15 to 49) 14-15
Eriksson & Li,
Interviews and questionnaire to
groups of 3-5 room-attendants
or to person in charge from
outside company
8 Danish hotels
15 to 20 for
outsourced cleaning
and 20 to 25 for in-
Onsøyen &
Mykletun, 2009
13 focus groups interviews with
4 Norwegian
hotels 20-30 17
Dalci et al., 2010 Observations in a one-year
period and follow-up interviews
One four-star
hotel in Turkey
4 (stayover), 6-15
(before check-in), and
16-31 (check-out)
Sherman, 2011
Participant observation in the
housekeeping department for
120 hours
2 luxury urban
hotels in a major
US city
- 12
Heath, 2016 Observation of 60 rooms One all-suite hotel
in USA
23 (stayover) and 43
Table 1. Room cleaning times collected in previous articles
As can be seen, there appear to be two ways to set room cleaning times: directly, through the identification of the
activities carried out during cleaning and their time measurement; and indirectly, through the allocation of a number
of rooms per hour or per workday. According to Sherman (2011), the quota is by far the most common way of
organizing room cleaning and turndown in the hotel industry. Nevertheless, Edghiem and Mouzughi (2018) explain
some of the advantages of the direct method, analyzing the case of a hotel that follows a sequence of 20 steps to
clean rooms: it enables better control and efficiency, it is easier to estimate the time spent on each step and identify
the weakest areas of performance, and it is possible to control the quality of the cleaning.
Regarding the factors that determine cleaning time, the variables that have been analyzed in the literature can be
divided into two groups: those related to the task, such as the condition that the room is in or the way that the hotel
organizes the activity, and those related to the attributes or characteristics of the cleaning employees.
2.1. Task-Related Variables
These are factors that affect the type of cleaning work to be done and, therefore, the time required for its
execution. For example, Mehrez et al. (2000) show that, in general, there are statistically significant differences in
room cleaning times depending on the type of room (room type) and the type of cleaning (room cleaning
schedule) and establish a model based on these two variables. In the same line, in their ergonomic study of two
hotels in Montreal, Canada, Seifert and Messing (2006) find that cleaners’ work activity varies considerably
depending on the type of room to be cleaned and whether the guest is staying over or not. Jones and Siag (2009) do
not consider the characteristics of the cleaning as explanatory variables but, rather, six characteristics of the hotels:
Age, Size, Location (Urban or Non-urban), Service level (Two-, Three- or Four-star), Occupancy and Region, and
find that the only variable that is significant is the Service level. In the study by Dalci et al. (2010), housekeeping
activity times are related to the type of customer (business, through travel agents, “walk-in”, etc.) and the point in
the customer’s stay (before check-in, during the stay, after check-out).
Journal of Industrial Engineering and Management –
Onsøyen and Mykletun (2009) point to other aspects that most likely influence room-attendants’ work, such as the
variation in surface qualities and room design, weight of objects and other attributes (e.g., bed covers, chairs etc.),
physical space for cleaning and equipment transport and the practicability and quality of the equipment.
Sherman (2011) emphasizes the role of customers as an influencing factor despite their minimal interaction with
housekeepers. She argues that customers affect several non-interactive dimensions of these jobs, including timing,
pace, and effort. In particular, guest behavior determines the timing of work, as, for example, the room-attendant
cannot enter the room until the guest leaves. The guest’s behavior and desires also affect what the workers do once
they are able to enter the room, as room cleaning is harder or easier depending on the use that the guest has made
of the room. For example, in Sherman’s (2011) study room cleaners agreed that occupied rooms (with a stayover
guest) were easier to clean than vacant rooms (when the guest has checked out). Likewise, rooms occupied by
business travelers were also easier to clean than those occupied by leisure travelers, who spend more time in their
rooms and so leave them in greater disarray.
Thus, in this study, we use three variables that should affect cleaning time, two of which have been used in previous
investigations (type of room and type of cleaning) while the third has been less studied (the number of rooms
assigned), and propose the following three hypotheses:
H1: Room cleaning time is related to room type.
H2: Room cleaning time is related to point in the guest’s stay.
H3: Room cleaning time is related to the number of rooms assigned to the employee.
2.2. Housekeeper-Related Variables
Another type of factor that can influence cleaning time is related to the characteristics of the employees who
execute the task. There are few studies that have analyzed the influence of this type of variable on cleaning time,
but it has been used in studies of other aspects of the hotel industry. Age, organizational tenure, and education
were included in Xie, Li, Chen and Huan (2016) and in Safavi and Karetepe (2018), for example, and employment
status (equivalent to contract type) in McPhail, Patiar, Herington, Creed and Davidson (2015).
With respect to training, Vlijmen (2019) states that it is important to prepare cleaners not only technically (the what
and how of cleaning) but also in the social aspects of the work (for whom the cleaning is being done) and
encourages face-to-face communication between users and cleaners. Abasabanye et al. (2018) reach this same
conclusion, although they also state that contact is virtually impossible in the case of cleaning in hotels. In her
study, Sherman (2011) finds that training is rather informal, as room cleaners and turndown attendants are trained
by their co-workers. She goes on to explain that this “sink or swim” approach is common in the industry, even
when more formal training supposedly exists, and, as a consequence, room cleaners do not necessarily do the major
tasks in the same order.
Oxenbridge and Lindegaard-Moensted (2011) find that payment per number of rooms cleaned results in task
“speed-up”. Onsøyen and Mykletun (2009) also mention that the type of contract or form of payment can
influence time: “A couple of hotels employed room-attendants on a fixed-hour-fixed-salary basis, at the same time
that the room-attendants were remunerated for every room they cleaned in addition to the fixed number of 17
rooms”. Sherman (2011) also notes that some hotels pay cleaners by the room, intensifying work even further and
involving guest-generated unpredictability in workers’ earnings.
In this study, four employee attributes taken from previous studies are analyzed: age, degree of experience, training,
and type of contract. Consequently, we propose the following four hypotheses:
H4: Room cleaning time is related to the employee’s age.
H5: Room cleaning time is related to the employee’s level of seniority.
H6: Room cleaning time is related to the fact of receiving training.
H7: Room cleaning time is related to the type of employee contract.
Journal of Industrial Engineering and Management –
3. Methodology
3.1. Case Study
The study was conducted in a 4-star hotel in Seville (Spain). The hotel has 74 rooms on 6 floors. From highest to
lowest category, the four types of room are: Suite, Superior Standard (double), Standard (double) and Single. The
Suite is 37 m2 in size with a living room and a hall; the Superior Standard Room is 27 m2, while the Standard Room
is 18 m2, and the Single Room between 12 and 15 m2.
The hotel has established three different types of cleaning: “Checkout”, “Stayover with changes”, and “Stayover”.
“Checkout” means that the customer vacates the room and that it needs to be left perfectly ready for the entry of a
new customer. “Stayover with changes” means that the customer is going to spend more than three nights in the
hotel, so the sheets are changed after the third night. This type of cleaning is not usual in this hotel since it is a
business hotel with very short stays. “Stayover” type cleaning means that the customer is going to reuse the room
the same day, so the bed is made, and the rest of the room is cleaned, but the sheets are not changed.
Although there are hotels where room cleaning can be performed in teams, in the hotel analyzed it is carried out
individually by a single employee. This hotel works with a fixed housekeeper assigned to each floor. This allows better
control when assessing the quality and condition of the rooms and helps to maintain stable quality standards. At the
same time, the management aims to achieve greater work efficiency with this policy, since the housekeeper knows the
status of the rooms on her/his floor, and it enhances the degree of involvement and commitment to work well done.
The average monthly salary of a housekeeper is 800€ and the working week is 40 hours spread over 5 days.
3.2. Time Study
The Time Study is a work-measurement technique used to obtain productivity standards (Fitzsimmons &
Fitzsimmons, 2011) or work standard times. “A work standard is the time for a trained worker, or team of trained
workers, to perform a task following a prescribed method with normal effort and skill” (Southern, 1999: page 371).
There are several work-measurement techniques (Freivald & Niebel, 2009: page 405): stopwatch time study,
pre-determined time systems, standard data, time formulas, and work sampling studies. As pointed out by Roser
(2016), Time Study is at the heart of the scientific management approach developed by Taylor, an approach whose
basis “was a detailed understanding of the work to be done, not only qualitatively but also and especially
quantitatively” (page 223). The stopwatch time study consists of identifying and measuring individual time elements
in repetitive work. These repetitive jobs are also called work cycles or Controllable Jobs (Thompson, 1998a, 1998b).
They have been given this last name because there is a degree of control over the execution of this type of work by
both managers and employees. In contrast, managers and employees do not have control over Non-Controllable
Work, since it depends entirely on the arrival of the customer and requires an immediate response.
Time studies are especially useful in the tourism sector for controllable jobs in which the work is repetitive. In
hostelry, cleaning rooms is by nature repetitive work and a controllable job. Other typical types of work of the hotel
industry, such as check-in, which involves attending to customers at reception, would be non-controllable, since
they can only be conducted, or at least in part, in the presence of the customer, which introduces a factor of high
variability. In these cases, another type of study called Work Sampling could be conducted, with the objective of
determining how workers distribute their time among several activities. Work Sampling can also be useful for
determining how restaurant waiters or amusement park monitors distribute their time, for example.
The present study focuses exclusively on room cleaning and excludes other areas of hotel cleaning that the
housekeepers are also responsible for, such as corridors, common areas or lounges. For this, a data collection sheet
was designed and used to register the time required to accomplish each of the tasks performed within the room
measured by stopwatch, as well as the time spent outside the room, but which forms part of room cleaning per se.
The last ones include the times required to load the cleaning trolley with the materials, maintenance of the “office”
(room located on the same floor in which the materials and cleaning products are stored), and to throw the dirty
linen into the laundry chute, etc.
The study data collection form was presented to each of the housekeepers, and they were told that some questions
would be asked to ascertain the profile of each worker. It was explained that the information would be treated
Journal of Industrial Engineering and Management –
anonymously, without identifying the worker and without communicating any personal information to the hotel
management. This was intended to ensure that no comparison could be made with other workers and that
employees could not be harmed professionally, since it was not the aim of the study to assess the performance of
any particular worker. The employee was therefore guaranteed to be under little pressure while being observed, and
that the study would be more representative of the process that it was intended to measure. During the study, an
effort was made to accustom floor housekeepers to the presence of the observer so that they would behave in the
most natural way possible, as they would without the presence of a stranger. It was made clear to the personnel that
the object of study was limited to the times spent cleaning rooms, and no other jobs performed by the
housekeepers or the cleaning quality were to be analyzed.
The person who collected data was trained for understanding the cleaning work to be measured, and to inspire
trust, develop a friendly approach to the workers and exercise judgement. Measurements have been made of all
hotel housekeeping employees so that the study is representative of this worker group.
To calculate the number of observations to be made, it was assumed that this was a normal population of infinite
size and, therefore, the sample size needed to estimate the population mean was measured by the formula (adapted
from Freivalds & Niebels, 2009: page 431):
Where: n = sample size or number of observations, s = sample standard deviation, x = sample mean, Z = the value
in normal distribution that corresponds to the desired confidence level (95% was chosen as this is a normal level in
studies of this nature), and
= the margin of error, set at a fraction of ±5% of x, which again is usual in this type
of study.
10 observations of cleaning times were made to obtain the mean and standard deviation in the above formula. The
resulting sample size was 74 observations of cleanings, so, as there were 8 staff employees, it was decided to make 9
observations per employee. 10 observations were made of 2 of the employees to reach a total of 74 observations.
Rooms to be measured were randomly selected each day. Measurement was done on different days and at different
times to be able to observe all the personnel in the housekeeping department, both permanent and temporary.
Table 2 shows the number of rooms observed in the study by category and cleaning type. It can be observed that
few rooms have needed “stayover with changes” cleaning since, as mentioned, this type of cleaning is only done for
customers who stay for longer than three nights. The number of rooms requiring “stayover” cleaning and
“checkout” cleaning are practically the same.
Room category
Cleaning type
Total Stayover
Stayover with
changes Checkout
Single 2 2
Standard 28 1 23 52
Superior Standard 4 1 9 14
Suite 3 1 2 6
Total 35 3 36 74
Table 2. Number of observations by room category and cleaning type
To address the first of our objectives, our analysis includes the calculations of the average values of the data collected
in the study of times for each of the tasks done as part of room cleaning and total time. Average total cleaning times
per room type and cleaning type have also been calculated. Excel spreadsheet was used for the analysis.
Journal of Industrial Engineering and Management –
3.3. Regression Analysis
The variables related to cleaning time that, according to the previous literature, were measured in this study are:
Room type: (1) single, (2) standard, (3) superior standard, and (4) suite.
Cleaning type: (1) stayover, (2) occupied with changes, and (3) checkout.
Number of rooms assigned per day: 13, 15, 16 or 19.
Cleaner’s age: (1) under 30 years, (2) 30—39 years, and (3) 40—49 years.
Cleaner’s seniority in the company: (1) under 5 years, (2) 5—9 years, and (3) 10—14 years.
Previous training in housekeeping tasks: (0) no training, and (1) training.
The type of employment contract: (0) temporary or (1) permanent.
Table 3 reports the percentages of rooms observed according to the characteristics of the housekeeping personnel.
Factor Attribute or variable Percentage in sample
Under 30 years 20
30—39 years 35
40—49 years 45
Under 5 years 20
5—9 years 35
10—14 years 45
Training No training 38
Trained 62
Type of employment
Permanent 54
Temporary 46
Rooms per day
13 9
15 18
16 15
19 58
Table 3. Observations (%) by cleaning staff characteristics (n = 74)
Table 3 shows that most of the rooms were made up by staff aged between 40 and 49 years and with an experience
of 10 to 14 years. Regarding the number of rooms per day, the most common value is that employees make up 19
rooms, which is the number usually assigned to a worker with a working day of 8 hours. Fewer rooms are usually
assigned to workers on a reduced working-day with nursery hours (5 hours, 13 rooms), and those who have a kind
of zero-hour contracts (6 hours, 15 rooms) and whose relationship with the company is temporary.
A descriptive analysis was performed previously using the data obtained in the observations to determine means,
dispersion measures and different graphs for the studied variables. For our second objective, multiple linear
regression analysis has been conducted with cross-sectional data. This analysis was performed with SPSS v.24
statistical software. The dependent or explained variable was the time spent cleaning (the total time without extras,
measured in seconds), since extras are irregular and unpredictable. All seven variables included in the study were
initially considered as independent or explanatory variables: Age, Seniority, Training, Contract Type, Room
Category, Cleaning Type and Rooms assigned per day. Two of these were dichotomous (Contract Type, and
Training); two, ordinal variables (Room Category and Cleaning Type); and the remaining were considered
continuous (Age, Seniority, and Rooms assigned per day). Age and Seniority were codified using the middle values
of the intervals (i.e., class midpoints).
Journal of Industrial Engineering and Management –
Although sufficient, the number of available observations was not high, so it was decided to perform a sequential
regression by dividing the variables into two blocks: the employee-related variables and the task-related variables.
We first considered a Model 1 with all the variables related to employee characteristics (Age, Seniority, Training and
Contract Type). Then, we considered a Model 2 for the variables related to cleaning task that were detected in the
prior literature: Room Category and Cleaning Type, plus the Number of Rooms assigned per day. Our analysis
strategy consisted of checking what employee-related variables were significant in Model 1 in order to include only
those ones in the second model (Model 2). The models to be estimated are, therefore:
Model 1:
Where, Yi = Total cleaning time without extras, X1= Age, X2= Seniority, X3= Training, and X4= Contract type.
Model 2:
Where, Yi = Total cleaning time without extras, X1= Room category, X2= Cleaning type, and X3= Number of
rooms assigned. The ellipses indicate the variables of Model 1 that are significant.
4. Results
4.1. Time Study Results
Table 4 presents some descriptive statistics of room cleaning tasks listed from greatest to least total time in the
Tasks N Total time in sample % of total time Average time Std. dev.
Making the bed 74 303.32 22.97% 4.10 1.73
Bedside tables and tables 74 181.18 13.72% 2.45 1.27
Washbasin. mirror and shower screen 74 175.57 13.29% 2.37 1.18
Scrubbing bathroom floor 73 128.52 9.73% 1.76 1.00
Hoovering 73 112.93 8.55% 1.55 0.57
Gathering bed linen 67 93.90 7.06% 1.39 0.88
Replacing towels 73 73.25 5.55% 1.00 0.53
Cleaning bathtub 70 72.00 5.45% 1.03 0.82
Removing used and dirty bathroom items 73 50.75 3.84% 0.70 0.50
Soaping and rinsing toilet and bidet 74 47.58 3.6% 0.64 0.44
Stripping the bed 37 37.63 2.85% 1.02 0.49
Replacing amenities 59 23.77 1.80% 0.40 0.38
Removing dirty bed linen 68 12.32 0.93% 0.18 0.19
Shower 6 8.62 0.65% 1.44 0.73
Total times without extras 74 1,320.73 100.0% 17.85 5.02
Total times with extras 74 1,370.37 18.52 6.69
Table 4. Descriptive statistics of room cleaning times (in minutes)
Journal of Industrial Engineering and Management –
Total times without extras correspond to routine tasks involved in room cleaning. Total times with extras include
some extra tasks entrusted to the housekeepers that are not routine or usual, such as cleaning liquid spills, picking
up a guest’s clothes, etc. These times have been separated so that extras do not distort measurement. However,
extras, and also idle or rest times, must be included in total times when estimating work standard times (WST). To
set the WST, it is also necessary to consider the pace of work developed by the worker in each observation
(Freivalds & Niebel, 2009; Fitzsimmons & Fitzsimmons, 2011). However, the objective of this work is not to
calculate the WST for hotel management but to analyze the cleaning time and the variables that can influence it.
As for the tasks, it can be seen in Table 4 that the task that requires the most time is “making bed” (23.0% of the
total time), followed by cleaning “bedside tables and tables”, “bathroom sink, mirror and shower screen”,
“scrubbing bathroom floor” and “hoovering. These 5 tasks together make up 68.24% of the total time. The data
for most of the measured times show moderate standard deviations.
Table 5 shows average total times without extras by cleaning type and room type. It is interesting to see how much
time is spent cleaning each type of room, and the time that it takes to perform each of the different types of
cleaning in the various room categories. The table clearly shows that the higher the category of room, the more
time is required. Likewise, it shows how time increases as the type of room cleaning required changes from
“stayover” to “checkout”.
Room category
Cleaning type
average timeStayover
Stayover with
changes Checkout
Single 15.48 15.48
Standard 14.79 15.60 19.66 16.96
Superior Standard 16.26 16.98 21.81 19.88
Suite 18.16 21.95 26.59 21.60
Total average time 15.24 18.18 20.35 17.85
Table 5. Average total times in minutes without extras by cleaning and room type
4.2. Regression Analysis Results
Table 6 shows the results of the first regression model, which only includes the employee-related variables.
Model 1*
Non-standardized Coefficients Standardized Coefficients
t Sig.B Std. error Beta
(Intercept) 906.19 253.17 3.853 .000
Age 3.77 5.69 .097 0.663 .510
Seniority -0.57 9.87 -.007 -0.058 .954
Training 127.06 84.35 .206 1.506 .137
Contract -92.59 74.94 -.154 -1.235 .221
R R2Adjusted R2Standard error of the estimate
.234 .055 .000 301.47
* Dependent variable: Total cleaning time without extras
Table 6. Model 1. Linear regression analysis with worker-related variables
Journal of Industrial Engineering and Management –
As can be seen, none of these variables is statistically significant and, in addition, the percentage of variance
explained by the model (R2) is practically nil. Thus, we can discard all these variables and, consequently, Model 2 will
only include the task-related variables. Table 7 shows the results of regression Model 2.
Model 2*
Non-standardized Coefficients Standardized Coefficients
t Sig.B Std. error Beta
(Intercept) 1,159.10 286.82 4.041 .000
Room category: Standard 273.94 177.05 .418 1.547 .127
Room cat.: Superior Standard 386.16 183.89 .505 2.100 .040
Room category: Suite 608.11 202.35 .554 3.005 .004
Cleaning: Stayover with changes 133.46 150.70 .088 0.886 .379
Cleaning: Checkout 328.28 59.42 .548 5.525 .000
Number of rooms assigned -32.88 13.41 -.236 -2.451 .017
R R squared Adjusted R squared Standard error of the estimate
.641 .411 .358 241.40
* Dependent variable: Total cleaning time without extras
Table 7. Model 2. Linear regression analysis with the cleaning task-related variables
The three variables included in the model are statistically significant. In Room Category, cleaning time increases
over the “Single” benchmark category are significant for “Superior Standard” and “Suite”, but not for “Standard”.
In the cleaning category, there is a significant time increase for the “Checkout” category compared to the
“Stayover” benchmark category, but not for “Stayover with changes”. Number of rooms assigned has a negative
and statistically significant coefficient, which means that, for each additional room assigned, the cleaning time is
reduced by almost 33 seconds or half a minute.
The ANOVA of this model confirms that when the two ordinal variables are contrasted jointly, i.e., considering
all their levels together, they are statistically significant (F(3, 67) = 4.062, p = 0.01, for Room Category, and
F(2, 67) = 14.297, p < 0.001, for Cleaning type).
The analysis of the residuals of this regression model suggests that there are no violations of the assumptions of
normality, linearity, and homoscedasticity. The inspection of Mahalanobis distances and standardized residuals do
not indicate the existence of significant outliers. Moreover, Cook’s distances do not show any influential case to be
highlighted. Neither of the two models exhibits multicollinearity problems, as the maximum VIFs are 1.56 in
Model 1 and 1.03 in Model 2.
5. Discussion, Implications and Limitations
5.1. Discussion
Regarding the first objective of this work, the average estimated time for cleaning a room in our study was about 18
minutes. However, the average time varies between 15 minutes for stayover standard room cleaning, and 27 for
checkout suite cleaning. These times are below what was obtained in previous studies (see Table 1). As for the
individual tasks, five of them (“making bed”, cleaning “bedside tables and tables”, “washbasin, mirror and shower
screen”, “scrubbing bathroom floor” and “hoovering”) jointly represent over 2/3 of the total time required for
The second objective of this article was to determine the variables that influence total room cleaning time in a
hotel. The linear statistical model obtained through the regression analysis has shown that these variables are room
category, cleaning type, and the number of rooms assigned to the employee. The study has also shown that, in our
Journal of Industrial Engineering and Management –
case study at least, housekeeper-related variables such as age, seniority, training or the type of contract (permanent
or temporary) do not influence cleaning time.
5.2. Theoretical Implications
This study makes some contributions on the academic level that may prove interesting. First, the study underscores
and demonstrates the utility of Time Study as a management technique, despite which, they are not usually used in
this sector. The application of a time study here has allowed to highlight the tasks consuming most cleaning time.
Despite their limited use, the need to conduct Time Studies in Tourism Sector research has become apparent, given
their importance in the development of modern industry (Roser, 2016). As Mehrez et al. (2000: page 360) point
out, “the abandonment of work measurement techniques suggests that many organizations no longer know how
much time is required to produce their products or services, a fact that may reduce long-run performance and
competitiveness”. In hospitality, work standards and service task time are needed to determine the number of
employees required, to simulate queuing situations, and to evaluate the performance of a service system (Southern,
Meanwhile, the regression analysis has allowed us to determine the variables that influence cleaning time. The study
proves that employee-related variables have no influence on cleaning time. This can be considered new information
as not all of this study’s variables have been applied in previous studies. The finding that room category and
cleaning type affect cleaning time is in line with previous studies (Mehrez et al., 2000; Seifert & Messing, 2006),
although empirical studies that prove this relationship are very scarce in the literature. However, the third variable
that was significant in the present study, the number of rooms assigned per employee, is rather more
It is important to highlight that the number of rooms assigned to an employee per day variable is a determinant of
cleaning time with a negative coefficient. This means that the housekeepers reduce time per room when they are
assigned more rooms, with the evident objective of not extending their workday beyond what they have been
contracted and paid for. This can be a major problem for the health and safety of hotel cleaners at work (Seifert &
Messing, 2006). Hsieh, Apostolopoulos and Sönmez (2016) stated that time pressure caused by rushing to clean
rooms puts great stress on workers and harms their psychological wellness. They go on to describe how hotel
housekeepers are exposed to a wide range of occupational hazards that have a negative effect on their health.
Kensbock, Jennings, Bailey and Patiar (2016) define room attending as “a predominantly female occupation
involving physically and psychologically hard work, low status and minimal pay” (page 115), and describe a number
of physical and psychological stressors that are likely to impact on their wellbeing and performance. Previous
studies have shown that this sector has one of the highest levels of risk for workers. For example, Krause et al.
(2005) state that occupational injury rates among hotel workers exceed the national service sector average. These
authors assess the prevalence of back and neck pain linked to physical workload, ergonomic issues, and increasing
work demands. In a 1999-2005 study of 40,030 employees at 87 unionized hotels in the US, Frumin et al. (2006)
states that there is a 61% greater risk of injury to housekeepers than to other hotel employees. In response to these
data, in some cases, “the labor union representing cleaners has negotiated a lower number of room assignments per
cleaner, as well as improvements to the way that work variability is taken into account when determining the quota
of rooms to be cleaned” (Seifert & Messing, 2006: page 557). The importance of controlling the workload is not
limited only to workers’ health but also to quality and absenteeism (Frumin et al., 2006), which clearly affect a
hotel’s productivity and, consequently, its economic results.
5.3. Practical Implications
It was seen that five individual tasks jointly represent over 2/3 of the total time required for cleaning, and so, if it is
wished to obtain any improvements in the process and time controls, the management and employees should focus
on these tasks (“bed making”, cleaning “bedside tables and tables”, “washbasin, mirror and shower screen”,
“scrubbing bathroom floor” and “hoovering”). At the same time, specific training should be provided for these
tasks and hotel designers could focus on them when planning facilities.
Journal of Industrial Engineering and Management –
It has also been shown how Time Study can be used for planning and scheduling room cleaning and employees and
what variables should be considered. This will allow staff numbers to be properly fitted to the hotel’s requirements
and the best combination of personnel to be determined, bearing in mind that temporary personnel are informed
of the hours of service that are required of them on a day-to-day basis. The management must be aware that this
activity needs to be properly planned as it affects its productivity and quality, as well as the satisfaction of the
employees who perform it.
One practical contribution made by this study is that it shows that assign a specific number of rooms to cleaners
could not be the best way to organize this hotel activity. Hotels have to know how much time is required to clean
each of the rooms according to room type and required type of cleaning. On many occasions, however, managers
are not mindful of these issues when assigning the number of rooms to be cleaned during the workday. It is
therefore proposed that the use of Time Study be generalized so the planning task can be undertaken more
scientifically in hotels. Among the main advantages of this technique, it can be mentioned the following:
The reliable estimation of room delivery time to customers.
The required personnel to be determined according to expected room demand.
The real cost of the analyzed process (personnel and materials) to be found, which will act as the basis for
price calculation.
Rewards and incentives to be set in the Housekeeping department.
A basis for comparisons with similar category hotels.
Improvements to be proposed that result in greater satisfaction and safety for workers, a reduction of
operating times and higher quality of service.
5.4. Limitations and Future Lines of Research
The main limitation of our study comes from its having been conducted of a single hotel establishment, which
prevents conclusions being applied more widely. Future studies of other hotels could also be used to compare the
influence on the cleaning time of cleaning methods, type of organization, hotel category, cleaning materials and
equipment used, etc.
It would have been desirable for this study of methods and times to have been done in conjunction with a study of
service quality, with observations of both the quality of room cleaning and the time spent cleaning. In short, if the
organization implements a quality control system, it should be linked with the time required to clean the room to
determine the best time–quality ratio.
In other respects, it would be very interesting to discover the degree of customer satisfaction and customers’
opinions regarding shortcomings that they happen upon in hotel rooms and any possible improvements that can be
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication
of this article.
The publication of this article was funded by G.I.D.E.A.O. Research Group (SEJ415).
Abasabanye, P., Bailly, F., & Devetter, F.X. (2018). Does Contact Between Employees and Service Recipients Lead
to Socially More Responsible Behaviours? The Case of Cleaning. Journal of Business Ethics, 153, 813-824.
Boon, B. (2007). Working within the front-of-house/back-of-house boundary: Room attendants in the hotel guest
room space. Journal of Management and Organization, 13(2), 160–174.
Journal of Industrial Engineering and Management –
Chen, W.J., & Chen, M.L. (2014). Factors Affecting the Hotel’s Service Quality: Relationship Marketing and
Corporate Image. Journal of Hospitality Marketing & Management, 23(1), 77-96.
Dalci, I., Tanis, V., & Kosan, L. (2010). Customer profitability analysis with time-driven activity-based costing: A
case study in a hotel. International Journal of Contemporary Hospitality Management, 22(5), 609-637.
Edghiem, F., & Mouzughi, Y. (2018). Knowledge-advanced innovative behaviour: A hospitality service perspective.
International Journal of Contemporary Hospitality Management, 30(1), 197-216.
Eriksson, T., & Li, J. (2009). Working at the boundary between market and flexicurity: Housekeeping in Danish
hotels. International Labour Review, 148(4), 357-373.
Espino-Rodríguez, T., & Ramírez-Fierro, J.C. (2017). Factors determining hotel activity outsourcing. An approach
based on competitive advantage. International Journal of Contemporary Hospitality Management, 29(8), 2006-2036.
Falbo, B. (1999). Room cleanliness remains key to garnering repeat business. Hotel and Motel Management,
September 6, 60-61.
Fitzsimmons, J.A., & Fitzsimmons, M.A. (2011). Service Management: Operations, Strategy and Information Technology.
Singapore: Mc Graw Hill.
Ferguson, L. (2011). Promoting gender equality and empowering women? Tourism and the third Millennium
Development Goal. Current Issues in Tourism, 14(3), 235–249.
Freivalds, A., & Niebel, B.W. (2009). Niebel’s Methods, Standards and Work Design (12th ed.). Boston, USA:
McGraw-Hill Higher Education.
Frumin, E., Moriarty, J., Vossenas, P., Halpin, J., Orris, P, Krause, N. et al. (2006). Workload-related musculoskeletal
disorders among hotel housekeepers: Employee records reveal a growing national problem. Paper presented at
the NIOSH 2006 National Occupational Research Agenda Symposium. Washington, DC.
Goggins, R. (2007). Hazards of Cleaning: Strategies for reducing exposures to ergonomic risk factors. Professional
Safety, 20-27.
Gundersen, M.G., Heide, M., & Olsson, U.H. (1996). Hotel guest satisfaction among business travelers: What Are
the Important Factors? Cornell Hotel and Restaurant Administration Quarterly, 37(2), 72-81.
Hartline, M.D, Wooldridge, B.R., & Jones, K.C. (2003). Guest perceptions of hotel quality: Determining which
employee groups count most. Cornell Hotel and Restaurant Administration Quarterly, 44(1), 43-52.
Heath, B.D.W. (2016). Housekeeping best practices help improve productivity., January, 20.
Hsieh, Y.H., & Chuang, I.C. (2020). Evaluation of key factors for service experience: A comparison of tourism
factories and international tourism hotels. Tourism Economics, 26(3), 404-436.
Hsieh, Y.C., Apostolopoulos, Y., & Sönmez, S. (2016). Work Conditions and Health and Well-Being of Latina Hotel
Housekeepers. Journal of Immigrant and Minority Health, 18(3), 568-581.
Jones, P. & Siag, A. (2009). A Re-Examination of the Factors That Influence Productivity in Hotels: A Study of the
Housekeeping Function. Tourism and Hospitality Research, 9(3), 224-234.
Kensbock, S., Jennings, G., Bailey, J., & Patiar, A. (2016). Performing: Hotel room attendants’ employment
experiences. Annals of Tourism Research, 56, 112-127.
Journal of Industrial Engineering and Management –
Kirwin, P. (1990). A Cost-Saving Approach to Housekeeping. Cornell Hotel and Restaurant Administration Quarterly,
31(3), 25-27.
Krause, N., Scherzer, T., & Rugulies, R. (2005). Physical workload, work intensification, and prevalence of pain in
low wage workers: results from a participatory research project with hotel room cleaners in Las Vegas. American
Journal of Industrial Medicine, 48(5), 326-337.
Lewis, B.R. & McCann, P. (2004). Service failure and recovery: Evidence from the hotel industry. International Journal
of Contemporary Hospitality Management, 16(1), 6-17.
Lockyer, T. (2003). Hotel cleanliness–How do guests view it? Let us get specific. A New Zealand study. International
Journal of Hospitality Management, 22, 297-305.
Manhas, P.S. (2015). Understanding service experience and its impact on brand image in hospitality sector.
International Journal of Hospitality Management, 45, 77-87.
McPhail, R., Patiar, A., Herington, C., Creed, P., & Davidson, M. (2015). Development and initial validation of a
hospitality employees’ job satisfaction index: Evidence from Australia. International Journal of Contemporary
Hospitality Management, 27(8), 1814-1838.
Mehrez, A., Israeli, A., & Haddad, Y. (2000). A work measurement application for hotel housekeeping management.
Tourism Economics, 6(4), 359-371.
Nicolau, J.L., & Sellers, R. (2011). The Effect of Quality on Hotel Risk. Tourism Economics, 17(1), 39-52.
OICA - International Organization of Motor Vehicle Manufacturers (2016). 2016 Production Statistics. Available at (Accessed: May 2017).
Onsøyen, L.E., & Mykletun, R.J. (2009). Silenced and Invisible: The Work- experience of Room-attendants in
Norwegian Hotels. Scandinavian Journal of Hospitality and Tourism, 9(1), 81-102.
Oxenbridge, S., & Lindegaard-Moensted, M. (2011). The relationship between payment systems work
intensification and health and safety outcomes a study of hotel room attendants. Policy and Practice in Health and
Safety, 9(2), 7-26.
Pereira-Moliner, J., & Tarí, J.J. (2015). Quality Certification, Performance and Size in Hotel Chains. Tourism
Economics, 21(2), 307-324.
Pongsiri, K. (2012). Housekeeping, Human Resources: Competency Service Standard Management for Hotel
Business in ASEAN. International Journal of e-Education, e-Business, e-Management and e-Learning, 2(5), 343-347.
Robinson R.N.S., Kralj, A., Solner, D.J., Goh, E., & Callan V.J. (2016). Attitudinal similarities of hotel frontline
occupations. International Journal of Contemporary Hospitality Management, 28(5), 1051-1072.
Roser, C. (2016). Faster, better, cheaper in the history of manufacturing. Boca Raton, FL, USA: CRC Press.
Safavi, H.P., & Karatepe, O.M. (2018). High-performance work practices and hotel employee outcomes: The
mediating role of career adaptability. International Journal of Contemporary Hospitality Management, 30(2), 1112-1133.
Seifert, A.M., & Messing, K. (2006). Cleaning Up After Globalization: An Ergonomic Analysis of Work Activity of
Hotel Cleaners. Antipode, 38, 557-578.
Sherman, R. (2011). Beyond interaction: Customer influence on housekeeping and room service work in hotels.
Work, Employment and Society, 25(1), 19-33.
Journal of Industrial Engineering and Management –
Siguaw, J.A. & Enz, C.A. (1999). Best practices in hotel operations. Cornell Hotel and Restaurant Administration
Quarterly, 40(6), 42-53.
Soni-Sinha, U., & Yates, C.A.B. (2013). ‘Dirty Work?’ Gender, Race and the Union in Industrial Cleaning. Gender,
Work and Organization, 20(6), 15.
Southern, G. (1999). A systems approach to performance measurement in hospitality. International Journal of
Contemporary Hospitality Management, 11(7), 366-376.
Thompson, G. (1998a). Labor scheduling, Part 1: Forecasting Demand. Cornell Hotel and Restaurant Administration
Quarterly, 39(5), 22-31.
Thompson, G. (1998b). Labor scheduling, Part 2: Knowing How Many On-duty Employees to Schedule. Cornell
Hotel and Restaurant Administration Quarterly, 39(6), 26-37.
Vlijmen, J. van. (2019). Being a cleaner in The Netherlands: Coping with the dirty work stigma. Facilities, 37(5/6),
World Tourism Organization (2017). Compendium of Tourism Statistics dataset [Electronic]. UNWTO, Madrid, data
updated on 11/01/2017. Available at: (Accessed with subscription:
April 2017).
Xie, L., Li, Y., Chen, S.H., & Huan, T.C. (2016). Triad theory of hotel managerial leadership, employee
brand-building behavior, and guest images of luxury-hotel brands. International Journal of Contemporary Hospitality
Management, 28(9), 1826-1847.
Zemke, D.M.V., Neal, J., Shoemaker, S., & Kirsch, K. (2015). Hotel cleanliness: Will guests pay for enhanced
disinfection? International Journal of Contemporary Hospitality Management, 27(4), 690-710.
Zock, J.P. (2005). World at work: Cleaners. Occupational & Environmental Medicine, 62, 581-584.
Journal of Industrial Engineering and Management, 2021 (
Article’s contents are provided on an Attribution-Non Commercial 4.0 Creative commons International License. Readers are
allowed to copy, distribute and communicate article’s contents, provided the author’s and Journal of Industrial Engineering and
Management’s names are included. It must not be used for commercial purposes. To see the complete license contents, please
... In a timed study of hotel housekeeping tasks, cleaning the bathroom and furniture cleaning were among the top five individual room cleaning tasks when accounting for time spent cleaning [23], with combined bathroom-cleaning tasks accounting for 33% of room cleaning time and cleaning furniture for 14%. When studying cardiovascular demands for hotel room cleaning work, dusting and bathroom-cleaning ranked second and third, respectively, for peak percent heart rate reserve among five room cleaning tasks studied [24]. ...
Full-text available
Hotel room cleaners frequently report job-related pain, with high rates of work-related musculoskeletal disorder injuries established for this group of workers. Surprisingly, there is limited published research documenting the impact of interventions to reduce ergonomic-related injury risks specific to hotel room cleaners’ job tasks. In this study focused on hotel bathroom-cleaning and furniture-dusting tasks, twelve experienced hotel room cleaners used their standard method and a risk-reduction method—a tool with a handle that could extend, to perform these tasks. The female study participants’ average age was 45.3 (SD 8.7) years with an average of 10 years of work experience as cleaners (range: 0.8–26.0 years). Trunk kinematics and a low back injury risk assessment were measured using the Lumbar Motion Monitor. All study metrics were significantly reduced when cleaning tasks involved use of adjustable, long-handled tools (p < 0.05). This study demonstrated that commonly available cleaning and dusting tools with extendable handles can significantly reduce low back injury risk among hotel room cleaners and potentially reduce injury risk to other body parts known to be the site of musculoskeletal disorders in this workforce. The study findings suggest that cleaning or housekeeping jobs in other industries where these same tasks are performed could benefit from use of extended-handle tools like those investigated here.
Cleanliness is a key determinant of service quality, and robot cleaners are increasingly being deployed in tourism venues to reduce cleaning costs and increase efficiency. However, how robot deployment might alter tourists' perceptions of a venue's cleanliness remains unexplored. Building upon the person-environment fit theory, we propose that consumers' evaluations of robot cleaners are contingent on the fit between robots and the cleaning environment. We supplement two experiments with text analysis to show that deploying robot (vs. human) cleaners in a hotel/airport dilutes consumers' perceptions of the venue's cleanliness. Consumers generally perceive robot cleaners to be less competent than humans and thus expect a venue serviced by robot cleaners to be less clean. However, when the cleaning task is considered to be disgusting or disruptive, consumers view robot cleaners as more competent. These findings have important managerial implications for whether and how to deploy robot cleaners in tourism settings.
Full-text available
Cleaning occupations, which in recent years have accounted for a not inconsiderable share of employment and job creation in France, are characterised by particularly bad working conditions and low pay. Is this situation inevitable? Are there not in fact mechanisms that might lead employers in the cleaning sector to adopt socially more responsible behaviours towards their employees? After all, the literature on corporate social responsibility suggests that the actions of consumers could be one of these mechanisms. The aim of our paper is to test the impact on job quality of contact between cleaning workers and service recipients. To this end, we analyse data from a survey carried out by the French Ministry of Labour and supplemented by interviews. Our results indicate that contact with service recipients does indeed have an influence.
When building successful service experience, service providers have to consider multiple factors from a multi-element standpoint. This study aims to establish a new conceptual model for key factors affecting service experience and determine the influential key factors using a multi-perspective and multi-criteria methods. This study uses an analytic network process (ANP) to calculate the degree of influence exerted by the criteria and factors of the service experience and conducts in-depth interviews to validate the results of the ANP, improving the reliability of the results of the study and increasing its practical reference value. Results from tourism factories and international tourist hotels find five main criteria that affected service experience: employee, customer, service environment, information technology, and knowledge creation. The results reveal that employee and service environment are the most important criteria. Therefore, tourism factories and international tourist hotels must invest resources in training and managing employees to equip them with specialized knowledge needed to deliver high-quality service experience. Tourism factories and international tourist hotels also need to pay attention to service environments, and by building an environmental ambience, they allow customers to receive an aesthetically pleasing and comfortable service experience. Future researchers can extend this study’s architecture and results, incorporate other important criteria and factors, and consider the interdependent relations between multiple key factors to further improve the key factors affecting service experience.
Purpose This study aims to explore what it is like to be a cleaner in the Netherlands. Drawing on the dirty work theory, it answers the question of how cleaners in the Netherlands cope with the dirty work stigma. Design/methodology/approach This study used a qualitative approach: 24 cleaners were interviewed and the researcher participated in a three-month cleaning course. By doing so, an insiders’ perspective was taken. Drawing upon the dirty work theory, a thematic analysis was made. Findings Cleaners take great pride in their work, but because of their social invisibility, they are not recognized by the people they work for, and they fail in being proud of themselves. This has moral consequences since cleaners start doubting whether they are seen as equal. Eventually, cleaners have an ambivalent relationship with their job. Practical implications Given the moral consequences, FM practitioners and researchers should take these findings into account. As FM value is dependent on the quality of the relations between FM and its stakeholders (e.g. cleaners), FM is challenged to think about its responsibilities toward cleaners and other dirty workers in its context. Originality/value There is not much research done in cleaning. The research that is done focuses on efficiency and organization of cleaning. Only little research focuses on the cleaner, an insiders’ perspective is scarce.
Purpose The current study explores the nature and implications of knowledge advanced through service employees’ innovative behaviour and leading to initiating innovation within the hotel service subsector. Design/methodology/approach A case study research method was applied to achieve the research objectives, which investigated two hotel properties resembling two personal–interactive service systems. Fifty-two semi-structured interviews were conducted along with other qualitative research methods, including the direct observation of employees, review of management archives/literature and the assessment of ‘micro cases’. Findings The research outcome highlights the role of knowledge as supplementary to the interlinked process of idea generation and development. A novel classification of two types of knowledge is revealed as pre-encounter and encounter-dependent knowledge, implicating four patterns of service employees’ innovative behaviour. Practical implications This paper recommends practical measures to nurture service employees’ innovative behaviour, leading to innovation. Originality/value The study contributes to service innovation research by providing an in-depth assessment at the micro level, overlooked to date, of the nature of knowledge and the service employees’ role in initiating innovation within the hotel service subsector.
Purpose The purpose of this study is to examine the levels of the main hotel outsourcing activities to identify the factors that determine the use of external suppliers for these activities. Design/methodology/approach A model was developed that analyzes the relationship between competitive advantage and outsourcing and how the relationship between competitive advantage and activity performance is affected by whether an activity is outsourced or not. Moreover, the study builds a matrix called “outsourcing and competitive advantage” where each of the activities can be placed. The study was carried out with a representative sample of hotels in a tourist destination, analyzing 12 activities from different departments in the hotels. Findings The study results indicate that there is a positive relationship between the competitive advantage of an activity and its outcome. In addition, the findings show that the relationship between competitive advantage and activity performance is stronger when the activity is developed internally than when it is outsourced. The study supports a negative relationship between the degree of outsourcing an activity and its competitive advantage. In addition, the findings showed that a change in the way of managing the outsourcing is determined by its performance. Practical implications This study aims to help managers make decisions about outsourcing by considering the perspective of the competitive advantage. Each hotel can situate the activities in the matrix created and compare itself to the sector mean for a strategic positioning of the outsourcing. Originality/value Most studies analyze asset specificity as a key variable; however, the competitive advantage has not been used in previous studies, in spite of being a better defined variable in the literature. This study classifies the activities into core and non-core and establishes their relationship with outsourcing. It also studies how the way of managing an activity (outsourcing or in-house) moderates the relationship between competitive advantage and performance. These aspects have not been analyzed in the literature..
Purpose – The purpose of our study is to test career adaptability (CA) as an underlying mechanism linking high-performance work practices (HPWPs) to met expectations, creative performance, and extra-role performance. Design/methodology/approach – Data were obtained from 313 customer-contact employees (CCEs) two weeks apart in three waves in the hotel industry in Iran. The proposed relationships were tested via structural equation modeling. Findings – Employees who perceive that management offers various HPWPs display elevated levels of CA. These employees in turn find that their jobs have met their expectations. They exhibit higher creative and extra-role performances. In short, CA is a mediator between HPWPs and the aforementioned employee outcomes. Practical implications – Management should invest in HPWPs to enable employees to manage various work- and career-related demands. Management should also create an environment where employees can take advantage of career opportunities for growth and development. In this environment, employees can prepare themselves for the future in the current organization and gain new skills. Originality/value – What is known about the factors influencing Savickas’s (2005) notion of CA and CA influencing various employee outcomes is limited.
A wide-ranging study conducted by researchers based at Cornell's School of Hotel Administration identified a diverse group of hotel companies that have implemented outstanding practices to improve operations. Some of the hotels and management companies selected as best-practice champions improved specific departments' operations, while others took a hotel-wide approach to improving operations. Several operators have sought out distressed properties with an eye to renovating the physical plant and upgrading operations. Another group of operators has implemented some form of quality-assurance system. The two specific functional areas that received the most attention were maintenance (including housekeeping) and the front desk, especially check-in and check-out. Respondents in this study reported an increase in guest and employee satisfaction, along with considerable profit improvements. Significantly, in several cases the hotels shared with employees the savings from revised work practices, particularly those that prescribe standard times for each activity.
Purpose: This paper aims to investigate the brand building behavior in the luxury hotel industry from the perspective of frontline employees. In particular, this study addresses the importance and relevance of supportive leadership, brand building behavior and customers’ perceived brand image in the hotel industry. Design/methodology/approach: The research uses data from four luxury class (4 and 5 star) hotels in the Pearl River Delta of China. Contact with frontline employees yielded employee and customer data, with 243 of 369 employee questionnaires having one or more matches with 1,158 customer questionnaires. Hierarchical linear modeling was adopted to test the research model. Findings: Luxury hotels benefit from managers who provide supportive leadership that encourages employee brand building behavior. In turn, employee brand building behavior influences customers’ positive perception of brand image. Practical implications: Brand building behavior is a top-to-bottom process. Luxury hotels need to pay attention to internal brand building orientation, while managers should reinforce the organization’s cultural orientation and provide appropriate job skills training to improve employees’ willingness and ability to build the company’s brand. Originality/value: Findings of this study contribute to the brand management literature and the hotel management literature by addressing important matters affecting the frontline employees’ brand building behavior.