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INTRODUCTION The global economic downturn that began in 2008 has led to fi rms re-evaluating their cost base and, in particular, the productivity of their

Original Article

A re-examination of the factors that infl uence productivity in hotels: A study of the housekeeping function Received (in revised form): 2nd March 2009

Peter Jones is ITCA Chair of Production and Operations Management in the School of Management at the University of Surrey. He has researched productivity for a number of years as part of a research programme designed to understand and improve operational performance in the hospitality industry. Since 1981 he has written or edited 12 textbooks on operations management, published numerous articles in a wide range of operations management and hospitality journals, and presented over 90 conference papers throughout the world.

Abhijeet Siag worked in the hotel industry for a number of years in India before recently graduating with his MBA from the University of Surrey.

ABSTRACT Evidence from a number of studies suggests that productivity in hotels is largely driven by factors outside the control of the manager. This paper questions this assumption by examining the level of productivity in the housekeeping departments in a chain of 45 hotels. The paper reviews the concept of productivity and the issues relating to its measurement, before reviewing previous studies of productivity in the hotel sector. A number of factors are identifi ed that appear to affect productivity performance. These are then investigated through analysing one year ’ s data from a web-based labour scheduling system that records every hour worked by every employee in a chain of hotels. This kind of data has not been used in any previous published study, which unlike studies based on Data Envelopment Analysis, enables specifi c performance indices or benchmarks to be identifi ed. The paper concludes that there is no signifi cant difference in productivity levels according to the size, location, demand variability or age of the hotel, thereby refuting evidence from some prior studies. It concludes that managers have much more control over productivity performance than previously thought. Tourism and Hospitality Research (2009) 9, 224 – 234. doi: 10.1057/thr.2009.11 ; published online 20 April 2009

Keywords: hotel ; productivity ; partial factor productivity

Correspondence: Peter Jones Faculty of Management & Law, University of Surrey, Guildford GU2 7XH, Surrey, UK E-mail: [email protected]

Re-examination of the factors that infl uence productivity in hotels

workforce. A relatively recent development in the hotel industry has been the adoption of web-based labour scheduling systems. A number of different application service providers (ASPs) now make it possible for those respon- sible for scheduling hotel employees (typically Heads of Department) to do so on the internet, thereby enabling oversight of their rosters by line management, General Managers and Head Offi ce. Moreover, such systems incorporate ‘ rules ’ designed to prevent schedulers from over staffi ng based on a range of criteria, most notably forecasts of demand. Many of these systems are also tied into human resource practices so that they help manage employee pay, holiday entitlement, statutory obligations such as the EU Working Time Directive, and so on. As a result, these systems provide a unique and detailed set of data into every hour worked, by every employee, in every hotel, in any hotel chain that has adopted this approach to scheduling.

Even though productivity within the service sector, and particularly the hospitality industry, has been widely researched (see for instance Witt and Witt, 1989 ; McKinsey Global Institute, 1998 ; Sigala, 2004 ), such a data set has never before been analysed to investigate productivity performance in hotels. Given that productivity performance has been found to be low, the data potentially provide a unique insight into potential causes of this. Witt and Witt (1989) and Rimmington and Clark (1996) presented evidence of poor productivity in the hospitality industry due to a lack of understanding and application of quantitative and analytical tech- niques. The McKinsey (1998) study reported productivity in UK hotels as being signifi cantly lower than that in hotels in the United States and France, due to fi ve key factors. These fi ve factors were:

age of the UK hotel stock – 75 per cent more than 40 years old; relatively low chain penetration; service mix (that included cooked breakfast rather than ‘ continental ’ );

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relative small size of UK hotels; lack of organisational learning.

Sigala (2004) also identifi ed factors that appear to infl uence productivity performance in the hotel rooms division. Moreover many of these factors are contextual and outwith the direct control of management – factors such as age of the property, location, size of the hotel and demand variability. Hence the study reported on here sought to investigate whether these contextual factors that appear to affect productivity can be substantiated using this new data set.

The paper is organised as follows. The issues regarding productivity measurement are dis- cussed, both in general terms and specifi cally within the hospitality industry. This is followed by a review of previous studies in order to identify the factors that appear to infl uence productivity levels, most especially in the rooms division of hotels. The research design for investigating these factors further is then explained. This is based on access to a data set derived from a web-based labour scheduling system that has extremely detailed data on relevant inputs and outputs over a 1-year period for 45 hotels. The fi ndings of the study and implications for further research are then discussed.

LITERATURE REVIEW The literature identifi es a number of related themes. Each of these is discussed below as follows – alternative productivity defi nitions, issues relating to productivity research, previous studies of hotel productivity and the factors that drive productivity in hotels.

Productivity defi nitions Productivity is typically defi ned as the ratio of inputs to outputs. But in fact, a widely accepted productivity defi nition cannot be found in the literature ( Johns et al , 1997 ; Brown and Dev, 1999 ). Productivity means different things to different people ( Prokopenko, 1987 ) and many advocate slightly different or even

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confl icting defi nitions and perceptions of productivity ( Pickworth, 1987 ; Sasse and Harwood-Richardson, 1996 ). Indeed, the conception of productivity, and how to set about improving it, is largely a refl ection of such disciplinary predispositions, with alterna- tive viewpoints expressed in management, the behavioural science and economics. For instance, Jones and Hall (1996) argued that the current thinking of productivity stems from and is a construct of the ‘ manufacturing paradigm ’ developed during the Fordist period. In this context the main focus was on mass production and consumption of as many iden- tical products as possible, wherein the work- force was controlled along Taylorist scientifi c principles. In reviewing the different produc- tivity interpretations, Sigala (2004) argued that productivity has been approached both as an umbrella concept including effi ciency, effectiveness, quality, predictability and other performance dimensions, and a concept refl ecting only production effi ciency. In this study, productivity will be measured as the ratio of one input to one output.

Researching productivity Even if the simple defi nition of productivity – the ratio of inputs to outputs – is accepted, there remain signifi cant issues with regards to measurement. Andersson (1996) identifi ed three diffi culties namely:

identifi cation of the appropriate inputs and outputs; measures of those inputs and outputs; ways of measuring the relationship between inputs and outputs.

Productivity measurement in hospitality in particular faces additional diffi culties due to the specifi c characteristics of its service nature that in turn create problems such as labour and process scheduling, consistency and demand ( Witt and Witt, 1989 ; Mill, 2008 ). Indeed, several authors ( Sasser et al , 1978, p. 122 ; Jones, 1988 ; Jones and Lockwood, 1989, p. 133 ;

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Witt and Witt, 1989 ) have argued that produc- tivity management and measurement has been limited in the hospitality sector by the features and characteristics of services. Specifi cally, the intangible nature of hospitality services suggests that it is diffi cult to objectively defi ne and measure the service outputs being provided (for example, number of guest-nights versus number of satisfi ed guests). The measurement and management of hospitality inputs and outputs is also complicated because of the simultaneous production and consumption of the hospitality services as well as their perish- ability and heterogeneity, as service encounters are experienced differently by different people or even by the same people at different circum- stances. For example, in a hotel stay, only the physical items can be easily measured and controlled, whereas many of the other features of the hotel experience, such as service and atmosphere are intangible. Moreover, because each transaction with each customer can be regarded as unique, a quality challenged is created. Jones and Lockwood (1989, p. 133) conclude that productivity measurement and management in services is extremely diffi cult because: inputs and outputs are diffi cult to standardise (mainly due to the unique nature of service transactions); input / output relation- ships are not constant (not standardised between units or departments); and inputs and outputs may be diffi cult to measure (due to their vari- ability and intangibility).

Witt and Witt (1989) also identifi ed three problems regarding productivity measurement in hospitality: the ‘ defi nition problem ’ ; the ‘ measurement problem ’ ; the ‘ ceteris paribus ’ problem. The defi nition problem refers to those diffi culties encountered when attempting to defi ne precisely what are the outputs and inputs of a given industry, which is particularly diffi cult when the outputs / inputs are intangible or are highly heterogeneous. Thus, the defi ni- tion problem is similar to the problem of identifying the right inputs and outputs. The measurement problem was described as the problem encountered when outputs / inputs can

Re-examination of the factors that infl uence productivity in hotels

affect productivity, for example, level of competition, location; or to the consideration of all the previous factors or a combination of them. Although the importance of using a total approach to productivity has been high- lighted, authors have simultaneously stressed the diffi culty for one metric to encompass all different measured factors. In other words, the defi nition problem is closely interrelated to the other two measurement problems.

The selection of appropriate outputs / inputs is also related to and dependent on the level and unit of analysis. Depending on what is the focus of analysis (for example, hotel depart- ment, product, market segment) relevant inputs / outputs should be used ( Johns and Wheeler, 1991 ). Aggregated input / output metrics can be disaggregated at any level in order to construct a whole ‘ family ’ / ’ hierarchy ’ of partial productivity ratios. However, aggre- gated metrics tend to obscure information, whereas partial measures tend to hide informa- tion and trade offs among other dimensions (for example, departments, resources). The latter can be overcome by considering partial metrics simultaneously, but this is very laborious and some times may lead to confl icting results ( Baker and Riley, 1994 ).

In this study, the focus is on one depart - ment – namely housekeeping; and one specifi c measure of productivity – the number of rooms that can be cleaned by a room attendant. Although only a partial measure, the cost of labour in cleaning a room is a signifi cant proportion of the total cost of room provision, and the room sales typically represent a signi- fi cant proportion of hotel revenue. Therefore understanding this key activity within the hotel and managing it effi ciently is extremely important.

Previous studies of hotel productivity Ball et al (1986) identifi ed three main categories of measurement units namely, fi nancial, physical and combination of the previous two. Based on these categories, several authors have cited

be defi ned but cannot be measured. However, even if outputs / inputs can be measured in some way, there may be problems in terms of using suitable units of measurement. The ‘ ceteris paribus ’ problem involves holding the other infl uences constant when examining the impact of a particular factor on productivity.

To resolve these challenges two alternative approaches may be adopted – either total factor productivity or partial productivity may be studied. Total productivity, as defi ned by Ball (1996) is ‘ the ratio of total outputs to the sum of all contributing inputs and associated resource inputs ’ . On the other hand, partial productivity usually focuses on just one input, or class of inputs. Sigala (2004) illustrated that the selection of inputs / outputs is dependent on two issues – the approach to productivity defi - nition, namely partial or total approach, and the identifi cation of the level and / or unit of analysis. Partial productivity studies focus on specifi c inputs that can be easily identifi ed and measured. However, because of the synergy between all inputs, as well as the fact that hospitality inputs / outputs are amalgams of tangible and intangible / qualitative elements, a multi-factor ( Chew, 1986 ) or total factor view of productivity has often been adopted. This takes into account all inputs, as well as the structural complexity of hospitality outputs / inputs, refl ecting the typical intangibility, perishability, heterogeneity and simultaneity characteristics of services ( Mahoney, 1988 ). As we shall see, due to the complexity of total factor productivity such research has tended to adopt one specifi c methodology, namely data envelopment analysis (DEA).

Overall, there is no conclusive agreement as to whether productivity refers to – the inclu- sion of all inputs and outputs (total factor) rather than the consideration of each input at a time (partial measures); the measurement of both tangible and intangible features of inputs / outputs regardless whether partial or total productivity ratios are calculated; the consid- eration of other factors that may be external to the control of management but can crucially

Jones and Siag

several productivity measures in the context of the hospitality industry (for example, Medlik, 1980 ; Sandler, 1982 ; Pavesik, 1983 ), while both fi nancial and physical units have been used in previous studies. For example, in devel- oping their DEA model, Johns et al (1997) used simple inputs and outputs, no ratios or composite data were employed, and non- fi nancial data were preferred. Specifi cally three outputs – number of room nights sold, total covers served and total beverage revenue – and fi ve inputs – number of room nights available, total labour hours, total food costs, total beverage costs and total utilities cost – were used. Anderson et al (1999) used a stochastic frontier analysis in order to measure the performance of 48 hotels by using four outputs (total revenue generated from rooms, gaming, food and beverage and other revenues) and fi ve inputs (number of full time equivalent employees, the number of rooms, total gaming related expenses, total food and beverage expenses and other expenses).

Hu and Cai (2004) also used DEA on a data set of 242 hotels in California. They compared labour productivity in three types of hotel – Bed and Breakfasts, Limited Service and Full Service. Two output measures were used – total revenue and number of rooms sold; and four inputs – number of full-time managers, number of part-time managers, number of full-time employees and a number of part-time employees. The study demonstrated different levels of productivity between the three types of hotel.

Sigala (2004) and Sigala et al ’s (2005) study adopted DEA, employed on a step-wise basis, so that inputs and outputs emerged from the analysis. For the Rooms Division the relevant inputs were found to be average room rate, room nights, non-room nights revenue, number of rooms, front offi ce payroll, administration M & O expenses, other rooms division payroll, other rooms division M & O expenses and demand variability. In Sigala et al ’s (2005) study, 14 hotels were identifi ed as effi cient, and the remaining 79 hotels as relatively less

effi cient, based on two main criteria – their ability to manage their operations (in large part labour) and their market effi ciency (managing demand variability).

The problem with DEA is that as a non- parametric technique it does not identify actual levels of productivity performance, only the relative performance of each of the units included in the study. To quote Hu and Cai (2004) – ‘ DEA revealed nothing about the underlying factors that contributed to the differ- ential productivity scores ’ . Likewise Barros (2005) , who studied productivity in Portuguese hotels, states – ‘ although DEA identifi es the ineffi cient hotels in the sample, it does not reveal the cause of the ineffi ciency ’ .

In this study, DEA will not be used as only partial productivity is being measured. Non- fi nancial measures will be adopted. The input will be employee work hours and output will be the number of rooms cleaned. This produces the ratio ‘ number of rooms cleaned per work hour ’ . This is seen as being consistent with industry practice in which similar norms are typically used to identify productivity in the housekeeping, typically such as a room attendant being expected to clean 16 rooms in an 8-hour shift.

Factors driving productivity Over many years in the hotel industry, research ( Van der Hoeven and Thurik, 1984 ; National Economic Development Council, 1992 ; Johns et al , 1997 ; McKinsey Global Institute, 1998 ; Brown and Dev, 1999 ; Cizmar and Weber, 2000 ) has shown that productivity can be signifi cantly impacted by hotel size, location, service orientation, ownership and manage- ment arrangement; hotel age, design, type and number of facilities; demand patterns and variability; staff fl exibility (refl ected in the use of part-time and full-time employees); marketing practices ’ effectiveness(for example, distribution, promotion, frequent guest pro - grammes). We shall now consider in more detail four of the most recent studies of hotel productivity.

Re-examination of the factors that infl uence productivity in hotels

Sigala (2004) summarised the factors identi- fi ed from previous studies that potentially infl uence hotel productivity as:

Location – rural, city centre, suburban Property size Hotel design – old / new Ownership – independent or chain Business format – owned, franchised Demand variability Level of repeat custom Average length of stay Market segments served Distribution channels Proportion of part-time staff

Sigala’s (2004) study of overall hotel pro - ductivity only found demand variability, hotel design and ownership to be signifi cant.

Barros (2005) applied DEA to 45 hotels in Portugal. He identifi es two factors that appear to infl uence productivity. The fi rst of these is age of the property as he states ‘ ineffi ciency is more prevalent among the historic pousadas (hotels) than among the regional pousadas ’ . The second is location as ‘ pousadas, in or near, the cities were more effi cient than those in more remote locations ’ .

Hu and Cai (2004) having conducted a DEA analysis went on to apply regression analysis to their data in order to identify the factors that appeared to infl uence performance. They concluded that ‘ managerial capabilities tends to be an important underlying factor that affects the hotel ’ s productivity, while a hotel ’ s size, category, and service quality can explain the variation in some segments ’ . However, mana- gerial capability was operationalised by using the measure ‘ average manager wages ’ . While it is to be hoped that better managers are paid more than less able ones and that these better managers manage productivity more effectively, we would argue that average manager wage is a poor measure of capability. However this study is signifi cant in that it is one of the few that suggests managers have a signifi cant impact on productivity performance, compared with

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factors outwith management control. It also clearly identifi es that different types of hotel have different levels of productivity.

Finally Kilic and Okumus (2005) adopted a different approach. They surveyed and inter- viewed managers of 55 hotels in Northern Cyprus to ask their opinion as to the key drivers of productivity. They identifi ed the fi ve most important factors as staff recruitment, staff training, customer expectations, multi-skill training programmes and the role of manage- ment. This study is interesting in that unlike the more quantitative studies, it shows that managers perceive human factors to be at the heart of productivity performance.

RESEARCH DESIGN Based on previous research we fi nd that there have been very few productivity studies inves- tigating partial productivity based on non- fi nancial data. In particular, there have been no studies of housekeeping in hotels that iden- tify outputs (that is, rooms cleaned) per employee hour worked – even though such a performance standard is commonly used as an industry benchmark. Moreover, the factors that appear to affect productivity performance – hotel age, hotel size, location, service orienta- tion and demand variability – have not been investigated in the partial productivity context. Hence the main aims of this study are to:

measure the level of productivity of room attendants in a chain of 45 hotels over a period of 1 year; identify and test the factors that appear to infl uence the productivity performance of room attendants in hotels.

A number of propositions were derived from the literature review, as follows:

older hotels have lower levels of produc- tivity than newer ones ( McKinsey Global Institute, 1998 ; Barros, 2005 ); smaller hotels have lower levels of produc- tivity than larger ones ( McKinsey Global

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Jones and Siag

Institute, 1998 ; Hu and Cai, 2004 ; Sigala, 2004 ); non-urban hotels have lower levels of productivity than urban ones ( Barros, 2005 ); full service hotels have lower levels of productivity than limited service hotels ( Hu and Cai, 2004 ; Barros, 2005 ); hotels with high demand variation have lower levels of productivity than those with lower levels of variation ( Sigala, 2004 ).

In addition, although no evidence emerged from the literature review that regional location might affect the results, this potential factor was also tested, because the chain had hotels throughout the United Kingdom and operated in potentially different markets, both in terms of labour markets and consumer markets.

To operationalise these propositions specifi c hypotheses were developed and tested on an existing data set. Three of the factors were treated as attributes, that is, age, location and service level; while the other two as variables, that is, size and level of demand. Hence the hypotheses were as follows:

1. Hotels more than 20 years old will have signifi cantly fewer rooms cleaned per employee hour.

2. The number of rooms cleaned per employee hour will increase as the number of rooms in the hotel increases.

3. Hotels designated as having a non-urban location will have signifi cantly fewer rooms cleaned per employee hour.

4. (a) Four star hotels will have signifi cantly fewer rooms cleaned per employee hour than three star hotels; and (b) three star hotels will have signifi cantly fewer rooms cleaned per employee hour than two star hotels.

5. The number of rooms cleaned per employee hour will increase as hotel occupancy increases.

6. Hotels in different regional locations will have signifi cantly different numbers rooms cleaned per employee hour.

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The sample chain was UK-based. It was selected because it operated a variety of hotels, some on a franchise basis for global brands. Hence it had both large and smaller hotels; old and new properties; urban and non-urban loca- tions; and two, three and four star hotels. The chain had adopted web-base scheduling and therefore had data over a one year period for all its properties. There were 48 hotels in the chain, but three hotels were excluded from the study due to missing data. Because the infor- mation stored in the system has been used as the basis for paying employees employee pay and meeting statutory obligations there is high degree of confi dence that these data are extremely accurate.

FINDINGS Within the labour scheduling system, each category of employee was coded and every hour worked by each type of employee was recorded, as was the number of rooms sold. Therefore the number of rooms cleaned per employee hour in each hotel was calculated by taking the total number of room attendant hours and dividing it into the total number of rooms sold. In addition, additional information was collected on 45 hotels with regards to their age (old or new), size (number of rooms), location (urban or non-urban), service level (two star, three star, or four star), level of demand (room occupancy) and regional location. The characteristics of the sample against these criteria are shown in Table 1 . For each of these an appropriate test of signifi cance was applied, depending on whether the factor was an attribute or variable and / or the number of data sub-sets.

Hypothesis 1, that hotels more than 20 years old will have signifi cantly fewer rooms cleaned per employee hour, was tested. The number of rooms cleaned per employee hour in old hotels was found to be 2.30, and in new hotels 2.24. The two-tailed unpaired t -test had P = 0.629, thereby showing no signifi cant difference between old and new hotels.

Re-examination of the factors that infl uence productivity in hotels

Hypothesis 2, that the number of rooms cleaned per employee hour will increase as the number of rooms in the hotel increases, was tested using the Pearson correlation test. This test resulted in P = 0.125, thereby demon- strating no correlation between the number of rooms in the hotel and the productivity of the housekeeping employees.

Hypothesis 3, that hotels designated as having a non-urban location will have signifi - cantly fewer rooms cleaned per employee hour, was tested. The number of rooms cleaned per employee hour in non-urban properties was found to be 2.35, and in urban properties 2.19. The two-tailed unpaired t -test had P = 0.149, thereby showing no signifi cant difference between urban and non-urban hotels.

ANOVA was used to test Hypothesis 4, namely that (a) four star hotels will have signif- icantly fewer rooms cleaned per employee hour than three star hotels; and (b) three star hotels will have signifi cantly fewer rooms cleaned per employee hour than two star

hotels. The results are summarised in Table 2 . In two star hotels, 2.51 rooms are cleaned per employee hour, in three star 2.28 rooms are cleaned per employee hour, and in four star hotels 2.05 rooms are cleaned per employee hour. These show a P = 0.034, thereby substan- tiating the hypothesis.

The Pearson correlation test was used to test Hypothesis 5, namely that the number of rooms cleaned per employee hour will increase as hotel occupancy increases. With P = 0.921, this hypothesis was not proven.

As a check to see that geography was signif- icant, Hypothesis 6 that hotels in different regional locations will have signifi cantly different numbers rooms cleaned per employee hour was tested by ANOVA. The results are shown in Table 3 . The result was P = 0.836, demonstrating no signifi cant difference in performance across the regions.

DISCUSSION Only one of the six hypotheses tested was proven. This was that four star hotels will have signifi cantly fewer rooms cleaned per employee hour than three star hotels; and three star hotels will have signifi cantly fewer rooms cleaned per employee hour than two star hotels. This was shown to be 2.05 rooms per employee hour in four star properties, 2.28 rooms per employee hour in three star and 2.51 rooms per employee hour in two star hotels. This fi nding confi rms previous fi ndings by Hu and Cai (2004) and Barros (2005) . It is also logical, as well as consistent with industry norms. Rooms in two star properties have fewer amenities placed in them, as well as less furniture and less equipment to clean than three star hotels; and likewise three star hotels compared with four star hotels.

The remaining hypotheses were not proven. Factors such as age, size, location and level of demand do not appear to infl uence productivity in the housekeeping department. It is therefore interesting to speculate why so many previous studies ( McKinsey Global Institute, 1998 ; Hu and Cai, 2004 ; Sigala, 2004 ; Barros, 2005 )

Table 1 : Characteristics of the sample

Factor Attribute or variable Number in sample

Age Old 22 New 23 Size 44 up to 272 rooms 45 Location Urban 22 Non-urban 23 Service level Two star 8 Three star 28 Four star 9 Occupancy 39.43 % up to 90.25 % 45 Region Scotland 9 Northern England 10 Midlands 6 London and environs 12

South-east England 8

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suggest that these factors are infl uential. We would argue that this is due to the issues we identifi ed in the literature review with regards the desirability or otherwise of measuring partial or total productivity, along with the specifi c measures used to analyse productivity. All of these previous studies adopted a total factor approach and all incorporated some fi nancial measures as either inputs and / or outputs.

However, as Table 2 illustrated, there was a wide range of productivity performance within the researched hotels in the same star category. In two star hotels, the number of rooms cleaned per employee hour ranged from 2.18 up to 3.00. In three star properties the range was extremely wide at 1.52 up to 3.34. And in four star properties it was 1.73 to 2.32 rooms per employee hours. It is clear from this that some- thing is causing different levels of performance, so what might this be?

This research appears to substantiate the fi ndings of Kilic and Okumus (2005) and the perceptions of the managers researched in their study. Human factors are likely to affect produc- tivity performance far more than other studies would suggest. Indeed this emerged as the view of those instrumental in implementing web-based labour scheduling within this hotel chain. In discussing the study with the Human Resource Director of the hotel chain and managers of the ASP who trained managers in the system ’ s use, they opined that differences in productivity performance derived solely from the quality of the management team in any given hotel. While this study was not designed to test their opinion, as no management char- acteristics were measured, by eliminating external factors such as age, location and size, we are left with the conclusion that something other than these is driving productivity performance.

Table 2 : Summary statistics for hotels of different star rating

N Mean

rooms / hour Std.

deviation Std. error

95 % Confi dence interval for mean

Minimum Maximum

Lower Upper

2 star 8 2.51 0.26455 0.09353 2.2857 2.7280 2.18 3.00 3 star 28 2.28 0.40094 0.07577 2.1211 2.4321 1.52 3.34 4 star 9 2.05 0.21432 0.07144 1.8820 2.2115 1.73 2.32

Total 45 2.27 0.37223 0.05549 2.1597 2.3834 1.52 3.34

Table 3 : Summary statistics for hotels in different regions

N Mean

rooms / hour Std.

deviation Std. error

95 % Confi dence interval for mean

Minimum Maximum

Lower Upper

Scotland 9 2.3022 0.44863 0.14954 1.9574 2.6471 1.84 3.34 North 10 2.3269 0.45392 0.14354 2.0021 2.6516 1.52 3.00 Midlands 6 2.1931 0.17224 0.07032 2.0123 2.3738 1.93 2.36 London 12 2.13192 0.40136 0.11586 2.0642 2.5742 1.90 3.09 S.E. 8 2.1554 0.26740 0.09454 1.9319 2.3790 1.75 2.53

Total 45 2.27 0.37223 0.05549 2.1597 2.3834 1.52 3.34

Re-examination of the factors that infl uence productivity in hotels

CONCLUSION Despite adopting a partial productivity approach, we would argue that this study has provided a number of signifi cant insights into labour productivity in hotels. First, this approach has for the fi rst time established productivity benchmarks for the housekeeping department in two, three and four star hotels based on a signifi cant sample of hotels. Secondly, it contra- dicts previous studies that suggest productivity is largely driven by factors outside the control of the manager. It is our contention that productivity can be managed and that managers can make a signifi cant difference to the effi - ciency of their hotels.

Of course, the study does have limitations. First, it is very focused on one specifi c activity with a hotel, namely the cleaning of rooms, albeit that this is an extremely important activity. Second, the study was only conducted within one chain and the results may not be generalisable. Third, the results only indicate what does not affect productivity performance, so we can only speculate as to what the actual drivers are. In view of this, further research studies are planned based on analysing data from the ASP. Such studies will include a similar study to this that investigates the productivity performance of other depart- ments in this hotel chain, such as bars, kitchen, restaurant and front offi ce. A second study is planned that will investigate the infl uence of the management team on productivity performance. Thirdly, a comparative study of this chain and other chains using the system will be undertaken. This will incorporate an international dimension, as the system has been adopted by chains outside the United Kingdom. Finally, it is argued by the ASP that web-based labour scheduling in itself can improve productivity and hence this proposition is to be tested.

REFERENCES Anderson , R . I . , Fish , M . , Xia , Y . and Michello , F .

( 1999 ) Measuring effi ciency in the hotel industry;

a stochastic frontier approach . International Journal of Hospitality Management 18 : 45 – 57 .

Andersson , T . D . ( 1996 ) Traditional key ratio analysis versus data envelopment analysis: A comparison of various measurements of productivity and effi ciency in restaurants . In: N. Johns (ed.) Productivity Management in Hospitality and Tourism . London: Cassell , pp. 209 – 226 .

Baker , M . and Riley , M . ( 1994 ) New perspectives on productivity in hotels: Some advances and new directions . International Journal of Hospi- tality Management 13 (4) : 297 – 311 .

Ball , S . ( 1996 ) Perceptions and interpretations of productivity within fast food chains: A case study of Wimpy International . In: N. Johns (ed.) Productivity Management in Hospitality and Tourism . London: Cassell , pp. 141 – 164 .

Ball , S . D . , Johnson , K . and Slattery , P . ( 1986 ) Labour productivity in hotels: An empirical analysis . International Journal of Hospitality Management 5 (3) : 141 – 147 .

Barros , C . P . ( 2005 ) Measuring effi ciency in the hotel sector . Annals of Tourism Research 32 (2) : 456 – 477 .

Brown , J . R . and Dev , C . S . ( 1999 ) Looking beyond RevPAR; productivity consequences of hotel strategies . Cornell Hotel and Restaurant Administration Quarterly , 40(April): 23 – 33 .

Chew , B . W . ( 1986 ) No nonsense guide to meas- uring productivity . Harvard Business Review 64 (1) : 30 – 89 .

Cizmar , S . and Weber , S . ( 2000 ) Marketing effec- tiveness of the hotel industry in Croatia . Inter- national Journal of Hospitality Management 19 : 227 – 240 .

Hu , B . A . and Cai , L . A . ( 2004 ) Hotel labor productivity assessment: A data envelopment analysis . Journal of Travel & Tourism Marketing 16 (2/3) : 27 – 38 .

Johns , N . , Howcroft , B . and Drake , L . ( 1997 ) The use of data envelopment analysis to monitor hotel productivity . Progress in Tourism and Hospitality Research 3 (2) : 119 – 127 .

Johns , N . and Wheeler , K . ( 1991 ) Productivity and performance measurement and monitoring . In: R. Teare (ed.) Strategic Hospitality Manage- ment . London: Cassell , pp. 45 – 71 .

Jones , P . ( 1988 ) Quality, capacity and productivity in service industries . International Journal of Hospitality Management 7 (2) : 104 – 112 .

Jones and Siag

Jones , P . and Hall , M . ( 1996 ) Productivity and the new service paradigm, or servicity and the ‘ neo-service paradigm ’ . In: N. Johns (ed.) Managing Productivity in Hospitality and Tourism . London: Cassell , pp. 227 – 240 .

Jones , P . and Lockwood , A . ( 1989 ) The Management of Hotel Operations . London: Cassell .

Kilic , H . and Okumus , F . ( 2005 ) Factors infl u- encing productivity in small island hotels: Evidence from Northern Cyprus . International Journal of Contemporary Hospitality management 17 (4) : 315 – 331 .

Mahoney , T . A . ( 1988 ) Productivity defi ned . In: R.J. Campbell and J.P. Campbell (eds.) Produc- tivity in Organizations: New Perspectives from Industrial and Organizational Psychology . New York: Jossey-Bass , pp. 101 – 121 .

McKinsey Global Institute . ( 1998 ) Driving Produc- tivity and Growth in the UK Economy . London: McKinsey Global Institute .

Medlik , S . ( 1980 ) The Business of Hotels . London: Heinemann .

Mill , R . C . ( 2008 ) Managing labour productivity . In: P. Jones (ed.) Handbook of Hospitality Oper- ations and IT . Oxford, UK: Butterworth Heinemann , pp. 269 – 294 .

National Economic Development Council (NEDC) . ( 1992 ) Working Party, Competitiveness in Tourism and Leisure, Sub-group Report. Costs and Manpower Productivity in UK Hotels . London: NEDC .

Pavesik , D . V . ( 1983 ) The myth of labour cost per - centages . Cornell HRA Quarterly , 24 (November): 87 – 93 .

Pickworth , J . ( 1987 ) Minding the P’s and Q’s: Linking quality and productivity . Cornell Hotel Restaurant and Administration Quarterly 28 (1) : 40 – 47 .

Prokopenko , J . ( 1987 ) Productivity Management: A Practical Handbook . Paris: International Labour Offi ce .

Rimmington , M . and Clark , G . ( 1996 ) Productivity measurement in food service systems . In: N. Johns (ed.) Productivity Management in hospitality and Tourism . London: Cassell , pp. 194 – 208 .

Sandler , M . ( 1982 ) Productivity measurement and improvement in the hospitality industry . In: A. Pizam, R.C. Lewis and P. Manning (eds.) The Practice of Hospitality Management . New York: AVI , pp. 153 – 163 .

Sasse , M . and Harwood-Richardson , S . ( 1996 ) Infl uencing hotel productivity . In: N. Johns (ed.) Productivity Management in Hospitality and Tourism . London: Cassell , pp. 141 – 164 .

Sasser , W . E . , Olsen , P . R . and Wyckoff , D . D . ( 1978 ) Management of Service Operations . Boston, MA: Allyn and Bacon .

Sigala , M . ( 2004 ) Using the data envelopment analysis for measuring and benchmarking productivity in the hotel sector . Journal of Travel and Tourism Marketing 16 (2/3) : 39 – 60 .

Sigala , M . , Jones , P . , Lockwood , A . and Airey , D . ( 2005 ) Productivity in hotels: A stepwise data envelopment analysis of the hotels ’ rooms division processes . Service Industries Journal 25 (1) : 61 – 81 .

Van der Hoeven , W . and Thurik , A . R . ( 1984 ) Labour productivity in the hotel business . Service Industries Journal 2 (2) : 161 – 173 .

Witt , C . A . and Witt , S . F . ( 1989 ) Why produc- tivity in the hotel sector is low . International Journal of Contemporary Hospitality Management 1 (2) : 28 – 33 .

A Greener Approach to Laundry Starts with the Little Things

by Randy Radtke

True or false: Adopting a Green campaign is a huge undertaking requiring a major commitment of resources? False. Greening up your facility mostly requires preplan- ning and some outside-the-box thinking, especially when it comes to the laundry room.

"It's i m p o r t a n t for l a u n d r y managers and general managers ofthe property to have realistic ex- pectations," says William Bittner, national sales manager of the

Speed Queen brand of commercial laundry equipment. "That starts with the realization that tweak- ing cycles will never be a counter measure for old, inefficient wash- ers and dryers. However, if your laundry is modern and equipment newer, small adjustments can net significant savings and help make strides toward a Greener property."

Old, inefficient equipment can he a huge drain of laundry resourc-

es because it impacts everything, including staffing.

Equipment Tips A Greener approach in the

laundry s t a r t s with a few key equipment features on the wash- ing side, such as programmable controls, multiple water levels, and high G-force extraction. On the drying side, dryers should have a balance of airfiow, tumble action,

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GREENER APPROACH

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and heat. "A careful balance of the key

components of the drying process will be far more efficient that just a heavy Btu input," says Dave Phillips, national sales manager of the Cissell brand of laundry equip- ment, adding that, "many manu- facturers like to push high Btus as fast, efficient drying; however, without a balanced design, extra energy could just be wasted. A Green approach definitely doesn't include wasting energy."

To make the drying process even more efficient, managers should seek washers with high speed ex- traction of 300 G-force or higher.

"Fast, efficient drying obviously includes a quality commercial dryer, but more efficient drying actually starts with a washer-ex- tractor with super high G-force," says Kim Shady, vice president of distributor sales for the UniMac brand of laundry equipment. "One needs only to review the difference in water retention between a 300 G-force cycle and a 200 to see the impact of spin speed.

"For a laundry washing a 60- pound load of terry towels, mois- ture retention at 200 G-force is about 67 percent, compared to 57 percent at 300 G-force. This dif- ference can greatly reduce drying times," adds Shady.

With lower G-force, the gap can be even more dramatic. Going from 86 to 300 G-force can reduce the laundry's drying times by as much as 30 percent.

Shady, however, cautions that t h e r e are diminishing r e t u r n s when going above 300 G-force and managers should weigh machine cost a g a i n s t those r e t u r n s . A machine with 300 G-force can be economical and deliver the Green results the property is seeking.

Often, a Greener approach in the laundry starts with outside- tbe-box thinking. Some laundries have seen the benefit to upgrading

from a dated 75-pound single dryer to a stack 45-pound tumbler. The move gives the laundry more dry- ing capacity—90 pounds total with two 45-pound cylinders —in the same fioor space as a single unit. Laundries also benefit from faster drying times and reduced utility consumption when a 60-pound load is split between the two dry- ing cylinders.

"Some of our stack tumblers were originally designed for laun- d r o m a t s seeking more drying capacity in a smaller space," said Barry Christenson, national sales manager of Huebsch brand of laundry equipment. "Increasingly, we are seeing interest in the stack product from managers of on- premises laundries."

Still not convinced you should replace your 20-year-old, hard- working drying tumblers for more energy-efficient units? Consider that dryers account for roughly 70 percent of your laundry's energy consumption. It pays to be Green, and it doesn't take long before the savings offset the purchase price of new models.

Choosing the Right Formula Wash cycles, because of the vari-

ous steps and energy input, are an area where laundries can Green up very quickly with just small chang- es. For example, small reductions in water use in a 60-pound capac- ity washer-extractor, multiplied by the number of loads processed daily and multiplied out for the year really add up. That's where laundries that have washers with more than just the standard three or five water levels have a signifi- cant advantage in efficiency.

"Some might view features, such as 30 or more programmable water levels, as overkill, but in truth, this fiexibility enables laundries to fine-tune water consumption to the lowest level, while still maintain- ing quality wash results," Shady

Wa.s/i cycles, becaiitie of tke various step.'i and energy input,

are an area where laundries can Gr-een up very quickly with just

small changes.

says. Tailored wash cycles also help extend linen life.

In addition, managers must look even deeper than just fill levels. They also should be reviewing the wash formulas themselves that are being utilized for various loads.

For example, is your operation using a prewash step for all loads? This step is only necessary for the laundry's heavy soil loads, thougb many laundries make it part of every load. This can add 15 to 25 percent more water to the cycle.

Chlorine bleach baths are an- other barrier to a Greener laundry (oxygen bleaches and hydrogen peroxide are considered more environmentally-friendly). They are definitely not a requirement for all loads and add 15 percent more water into the cycle. Cutting them out, when possible, is a Green initiative not only for the water savings, but because they are hot water fills, the laundry reduces utility consumption because it isn't heating as much water.

Jim Mitchell, principal techni- cal support specialist at Ecolab, a leading supplier of laundry chemi- cals, says bleaching remains the key method for oxidizing residual

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4 Executive Housekeeping Today/May 2009

EXECUTIVE CORNER

(continued from page 2)

GREENER APPROACH

(continued from page 3)

during this tough economy, the As- sociation Office has implemented a salary freeze for all staff. Addition- ally, two other options are heing considered in order to save money. They are: 1) Give each staff mem- ber a week off this year, without pay or. 2) Have each staff member work an additional four work hours per week, without pay, for a 12- week period. This would reduce the need for part-time staff.

All cuts or reductions that have already been implemented, or that are presently under consideration by the IEHA Association Office, have been made in order to benefit YOU —the member! The money that's being saved hy our Office staff is able to be put back into the Association in order to provide YOU with additional benefits. Two excellent examples of those cost savings being put into use are: 1) The Association has the funds available during this difficult economy to put the highly-ac- claimed Self Study program online hy the end of 2009, and 2 ) We also have the ability to purchase the Housekeeping Channel® (www. housekeepingchannel.com t, which will further heighten awareness of the IEHA brand to the public.

I am very proud of our IEHA As- sociation Office staff and the steps they've taken to trim and hold our operating costs. If you'd like to express your thanks to our great staff, I encourage to you send them an e-mail at

stains, though many laundries may not be getting solid results in their bleaching process. One reason for an underperforming bleach cycle may rest in the laundry lowering the temperatures of its hot water to cut costs.

"The hot water temps in hos- pitality laundries are dropping rapidly... most are running in the range of 110 to 130 F (43 to 54 C) now," Mitchell says. "This is very ineffective for oxygen bleach. Chlorine bleach will perform bet- ter at lower temps, but far less effectively."

If a Greener laundry is the goal, it's important to work with your chemical company representative to fine tune wash programs and ensure you will not be sacrificing wash quality by adjusting cycles.

More Easy Fixes Sometimes it's easy for us to

overlook the obvious, and that's the case in the laundry as well. A fast way to conserve valuable resources is to maximize the capacity of your equipment. If staff is frequently under loading machines, obviously that translates into more loads throughout the day. Taking time to educate staff on what a "full load" looks like can help save water and utilities.

"Some controls on the market today can help managers identify if there's a prevalence of under loading in the laundry," Shady says. "Some of our new washer- extractors offers operations reports that track error codes such as out- of-balance loads," he adds.

Frequent out-of-balance condi- tions usually are a result of staff consistently underloading the washer.

Pretreating stains is another quick fix that can help the laun- dry reduce its water and energy consumption. Housekeeping staffs are on the front line in dealing with stains, seeing them before

the laundry staff. If they can pre- treat stains before linens enter the laundry, it could greatly reduce re-wash totals. Not only could that mean fewer loads daily and utility savings, but it may help scale back stafí time as well.

These are just a few tips that apply to the laundry. Certainly, when the property embraces the entire Green concept, other small changes can be identified to gener- ate additional savings and create an overall more environmentally- friendly facility. ^

Randy F. Radtke is a Public Relations Specialist far Alliance Laiindiy Sys- tems, located in Ripon. W/, He may be readied at randy.iadt/ie@alliancels.

Setting Up a Web Site for Your Chapter

A New Program Being Offered by IEHA July h 3009 ,

Are you ready to get started?

IEHA encourages its local Chapters to work with IEHA's Webmaster, Paul Rathey, to set up their Chapter Web site. The initial cost for your site is $250 with an annual fee of $50 for maintenance. Paul will handle the HTML basics, the domain name, the page template with proper logos, and all necessary links. In addition, Paul will make some recommendations forvarioustypesofcontent to include in your site's pages. By having Paul develop each ofthe Chapter sites, you can be assured that the aesthetics will be excellent and will show continuity and relationship to the overall IEHA site. If you are interested in working with the Association Office and Paul Rathey to assist you with your new Chapter site, please contact Sarah Larsen, Deputy Director at (800) 200- 6342, ext. 103.

Thanks so much for your consideration of this offer.

Beth B. Risinger CEO/Executive Director

12 Executive Housekeeping Today/fAay 2009

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