Factors Influencing Hotel Outsourcing Decisions in Thailand

Abstract

The article aims to examine the effects of variables on outsourcing decisions in the hotel industry in Thailand. It specifically seeks to test and evaluate a theoretical framework that combines transaction cost economics (TCE) factors with additional factors affecting the level of outsourcing that emerged from a preliminary study. There is evidence to suggest that TCE explains outsourcing effectively in developed countries, but it may have less explanatory power in developing economies. Drawing on a sample of 391 hotels, each offering three activities for analysis, the results provide little support for TCE. Supplier availability, hotel experience, and hotel size are the strongest predictors of outsourcing in this study. Furthermore, because the low level of supplier market competition in hotel supporting industries in Thailand does not normally offer more efficiency and lower production cost than in-house operations, as assumed by TCE, the theory fails to provide a clear explanation for hotel outsourcing practices.

Keywords

outsourcing transaction cost economics hotel Thailand

Since the 1990s, there has been significant growth in outsourcing and downsizing (Blumberg, 1998), so that outsourcing has become common management practice in today’s competitive environment (Rollins-Hinkle, 2001). This growth has been unparalleled and is expected to continue (Kakabadse and Kakabadse, 2005; Lankford & Parsa, 1999). Unsurprisingly, the academic literature on the subject has mirrored the growth of this business practice (Lonsdale & Cox, 2000), albeit with a focus on manufacturing firms. However, outsourcing has assumed an increasingly important role in service industries, most notably in the hotel industry, where a wide range of activities, such as laundry, cleaning, spas, security, and food and beverage outlets are now outsourced (Espino-Rodriguez & Padron-Robaina, 2004). But research into outsourcing by hotels has been limited, with one study in the Canary Islands (see Espino-Rodriguez & Gil-Padilla, 2005a, 2005b; Espino-Rodriguez & Padron-Robaina, 2004, 2005a, 2005b), another in Shanghai (Lam & Han, 2005), and two separate studies in Australia (see Lamminmaki, 2005, 2007, 2008, 2009, 2011). This article contributes to hotel outsourcing research in three ways. First, it develops and tests a revised model of outsourcing based on transaction cost economics (TCE). Second, it investigates this phenomenon in a different geographical area, namely Thailand. Finally, it compares the factors that influence outsourcing across three different types of hotel activity—laundry, restaurant, and guest transportation—to investigate if outsourcing decisions are generic or contingent.

The term outsourcing has been defined in various ways (see, for instance, Domberger, 1998; Espino-Rodriguez & Padron-Robaina, 2006; Lankford & Parsa, 1999), in this article, the term is used to refer to an externalization to independent suppliers of internal activities that could be and/or have previously been carried out in-house. Furthermore, the outside expert may undertake either whole or partial responsibility for the activity being outsourced.

TCE has been selected as the theoretical framework of this study for three reasons. First, TCE specifically addresses sourcing decisions and is therefore claimed to better describe this phenomenon than other theories (Gronhaung & Haugland, 2005). Second, it is a well-established theory and is widely used by researchers to interpret organizational reality (Lacity & Willcocks, 1995). Third, outsourcing behavior of organizations is generally explained by two main approaches: the economic perspective of TCE and the strategic approach based on resource-based view. Whereas outsourcing research in the hotel sector was initially dominated by the strategic approach (such as Espino-Rodriguez & Padron-Robaina, 2004, 2005a, 2005b; Lam & Han, 2005), a number of studies have more recently applied TCE to hotel outsourcing, including Lamminmaki (2005, 2007, 2009, 2011), Espino-Rodriguez and Gil-Padilla (2005a, 2005b), Espino-Rodriguez, Lai, and Baum (2008), and Wan and Su (2010).

The aim of this article is to examine the effects of a number of variables on three different outsourcing decisions in the hotel industry. It seeks to test and evaluate a theoretical framework developed by combining established TCE factors with additional factors derived from a study by Promsivapallop, Jones, and Roper (2007). The article is organized into four sections. The first section critically reviews TCE theory and its application to outsourcing. The second section outlines the design of the research and the methodology used. The third section presents the findings of the study and discusses these in the context of the literature. The final section draws conclusions as to those factors influencing outsourcing as derived from this study.

Transaction Cost Economics

This section reviews the literature relating to TCE and its application to outsourcing. The origin of TCE can be traced back to Coase (1937), who discovered that many organizations choose to produce goods and services (inputs) internally rather than obtain them from the market because of the costs involved in searching for prices, negotiating, and concluding a contract for each transaction. Williamson (1975, 1979, 1981) proposed later TCE theory, which positions the firm as a governance structure. The firm’s objective is to efficiently manage its various transactions by correctly matching different sourcing alternatives to achieve the economic optimum of both production expenses and transaction costs. TCE argues that because of the economies of scale and specialization provided by the market, markets are a more efficient governance structure, unless a transaction is surrounded by special circumstances. TCE proposes three circumstances under which markets (i.e., outsourcing) will not be used, namely, when the transactions are high in asset specificity, high in uncertainty, or high in frequency.

Asset Specificity

Asset specificity refers to the degree to which transactions are supported by durable and transaction-specific investments (Williamson, 1975). This denotes the extent of customization or specialization of the asset required for a particular transaction or user. High asset specificity indicates that the asset must be specially designed for the use of just one particular transaction or user. Such an asset cannot be used for other buyers without huge adaptation. In general, transactions with high asset specificity favor in-house production for two reasons. First, opportunism risks are normally significant in the transactions that require high specific asset investment (Rindfleisch & Heide, 1997). High specificity creates a sunk cost and the party that commits assets is vulnerable to holdups (Vining & Globerman, 1999). A second reason is that high asset specificity leads to a decline in market production cost advantage (Williamson, 1981), and it subsequently increases the comparative governance costs of markets. This is because the highly specific asset is fully specialized to a single use or user. Hence, the market is unable to attain economies of scale, so that the production costs are the same in the market and in-house, but the latter has much lower transaction costs.

According to Williamson (1985, 1988, 1991) and Lohtia, Brooks, and Krapfel (1994), asset specificity comprises six types, including the following:

Site specificity, which pertains to the investment made to locate the operations within physical proximity of each other to save on transportation and inventory expenses (Williamson, 1991).

Physical asset specificity, which refers to investment in equipment that is specific to the transaction and will have low values at the end of the contract because it can be of virtually no use to another transaction (Joskow, 1988).

Dedicated assets, which refer to “a general purpose asset, as opposed to a specialized asset, that has been purchased for a specific long-term trading relationship. Should the relationship expire, excess capacity will result” (Lamminmaki, 2005, p. 518)

Human asset specificity, which addresses the specialization of skills that arises from learning-by-doing (Williamson, 1991).

Brand name capital, which relates directly to reputation investment. It is the situation where one party can damage the brand name reputation of another party, if entering into a contract. In the situation where high brand name capital asset specificity is present, companies will not outsource an activity that allows its supplier to damage its reputation (Lohtia et al., 1994).

Temporal specificity, introduced by Masten, Meehan, and Snyder (1991), refers to the investment where timely performance and coordination of activities is critical. This means timing and coordination represents the specific asset (Lamminmaki, 2005). For example, the timing of laundry delivery from the laundry company to the hotel is a critical transaction cost. This asset specificity dimension matches the meaning of “time specificity” explained by Malone, Yates, and Benjamin (1987).

In addition, Zaheer and Venkatraman (1994) propose a seventh dimension, which is procedural asset specificity. This refers to the contractor’s work processes that are customized in accordance with the outsourcing company’s requirements. This is particularly imperative to service transactions, as service performance is characterized by procedures and processes.

Uncertainty

The second factor, uncertainty, entails the inability of decision makers to specify a complete contract (Williamson, 1975). It refers to the level of unforeseen changes in conditions surrounding a transaction (Grover & Malhotra, 2003) and thus indicates a lack of information (Aubert, Rivard, & Patry, 1996b). Therefore, TCE refutes the assumption underpinning classical economic theory that all economic agents are well-informed. TCE theory argues that most transactions carry some degree of uncertainty. Grover and Malhotra (2003) and Rindfleisch and Heide (1997) classify uncertainty into two dimensions: environmental and behavioral. Environmental uncertainty is the unpredictability of the environment and demand volume. Walker and Weber (1984) identify two elements of this construct—technological and volume. Technological uncertainty relates to the likelihood of change in component specifications and volume uncertainty involves the fluctuation level of demand for the component or activity. The second dimension is behavioral uncertainty, which refers to difficulties in performance evaluation or in verifying whether there has been compliance with established agreements. This issue of behavioral uncertainty adding to the challenge of measuring performance is particularly pertinent in this study as it has been noted that it is difficult and costly to measure the performance of suppliers in the service sector (Aubert, Rivard, & Patry, 1996a). Predicting future contingencies based on limited information undermines the company’s ability to specify accurate elements in a supplier contract (Bello, Dant, & Lohtia, 1997). In summary, both types of uncertainty raise transaction costs and hence lead to in sourcing decisions.

Frequency

The frequency with which transactions occur refers to the repetitiveness and volume of similar transactions (Lamminmaki, 2007). Where specific assets are not required and suppliers are competitive, transactions are commonly organized through the market because it provides more production efficiency. Companies are willing to bear the relatively high transaction costs as they are outweighed by the external expert’s production cost advantages. On the other hand, if volume is high and internal economies of scale and returns can be generated, these may favor in-house production (Williamson, 1979).

Among these three dimensions of transaction costs, asset specificity has been substantiated by research evidence more than uncertainty and frequency (Shelanski & Klein, 1995). Strong substantiation for asset specificity is provided by a number of studies (Everaert, Sarens, & Rommel, 2006; Morrill & Morrill, 2003; Spekle, van Elten, & Kruis, 2007; Watjatrakul, 2005). In addition, Lyons (1995) found that asset specificity had a higher impact than scale or scope economies for in-house production on outsourcing in three industries. Aubert, Rivard, and Patry (2004) found that uncertainty had a negative relationship with IT outsourcing. Behavioral uncertainty has been found to have had a significant positive relationship with in-house production for complex production activities in the car manufacturing industry (Navak & Eppinger, 2001) and drug manufacturers (Azoulay, 2000). Walker and Weber (1984) demonstrated in their outsourcing study of U.S. car manufacturing that environmental uncertainty leads to in sourcing decisions. Furthermore, Coles and Hesterly (1998a) found evidence to support the view that in the presence of asset specificity, higher uncertainty will increase in-house operations of hospital activities. Furthermore, Murray and Kotabe (1999) examined the role of frequency as a moderator to the asset specificity and outsourcing relationship in 100 U.S. service firms and found that the relationship between asset specificity and internal sourcing is negatively moderated by frequency.

However, support for TCE-based studies and findings from research are mixed. Miranda and Kim (2006) noted contradictions in the results of various outsourcing studies. An inverse relationship of asset specificity with outsourcing was found by Aubert et al. (2004) and Murray and Kotabe (1999), but no support was found by Nam, Rajagopalan, Rao, and Chaudhury (1996). Ang and Cummings (1997) found asset specificity affected outsourcing in large but not small banks. They also demonstrated that uncertainty had a positive rather than a negative effect on outsourcing. In addition, Everaert et al. (2006) found a contrary effect of environmental uncertainty to TCE, as they revealed that it has a positive effect on the level of outsourcing. Walker and Weber (1987) found the relationship between uncertainty and level of outsourcing to be different under different levels of supplier availability. They found that high uncertainty regarding volume led to in-house operation decisions in low competition markets but not in high market competition. On the other hand, technological uncertainty led to outsourcing when market competition was high. In addition, Rieple and Helm (2008) used data obtained from various secondary sources in seven airlines to examine the level of outsourcing from a TCE perspective. They observed that the degree of outsourcing of the activities under examination were not always consistent with TCE doctrine. Clearly, this needs to be investigated further.

Few outsourcing studies in the hotel industry have adopted the TCE concept. Lamminmaki (2005) in her initial qualitative study found that activities requiring high specific asset investments were generally insourced. This was partially supported in the quantitative phase of the same study (Lamminmaki, 2007). Another study by Espino-Rodriguez and Gil-Padilla (2005b) found that leisure activities, which are highly specific to hotels, perform better when they are carried out in-house. In addition, Lamminmaki (2007) also addressed frequency and uncertainty as key factors influencing hotel outsourcing decisions. She concluded that activities conducted frequently and, to some extent, in an uncertain environment tended not to be outsourced. These studies were conducted in Australia and Spain. However, previous TCE studies of hotel outsourcing in a developing country such as Thailand have been nonexistent. Therefore, this study will be the first that applies TCE to investigate hotel outsourcing in an emerging economy.

Meanwhile, using the resource-based approach, Lam and Han (2005) examined outsourcing strategy as perceived by hotel managers in Shanghai and found that the outsourcing market in the city was immature as it was hindered by two key factors: the incompleteness of local laws to protect hotel investors when outsourcing conflicts arise and the cultural incompatibility between hotels dominated by Chinese managers and outsourcing suppliers. In addition, Lai (2007) researched the application of outsourcing in the Taiwanese hotel industry and found a low level of hotel outsourcing in Taiwan because of the uncertainty of the quality of outsourcing services available in the market. Although these hotel outsourcing studies were conducted in a developing country, they did not adopt the TCE approach.

Modifications of Transaction Cost Economics

As a result of the variability of findings applying TCE, a growing number of scholars have realized that TCE on its own may not necessarily provide a complete explanation to the outsourcing phenomenon (Holcomb & Hitt, 2007; McIvor, 2009). For this reason, studies have been conducted to incorporate other theories into TCE-outsourcing research. Bello et al. (1997), Ang and Cummings (1997), Sorrel (2007), and Walker and Weber (1984), for example, combine TCE with production cost–related variables, including comparative production cost with suppliers, capital investment, supplier competition, and firm size. Other variables, such as customer contact (Murray & Kotabe, 1999), manager risk aversion (Everaert et al., 2006; Lamminmaki, 2007), and institutional context (Ang & Cummings, 1997; Miranda & Kim, 2006; Sorrel, 2007) have also been investigated together with TCE. Firm’s knowledge and experience appears to have been more widely merged with TCE than other variables (e.g., Bigelow & Argyres, 2008; Everaert et al., 2006; Holcomb & Hitt, 2007; Poppo & Zenger, 1998).

Since previous research has found that modifying the original TCE model helps to understand hotel outsourcing, it was decided to investigate TCE variables in relation to hotel services in Thailand through a preliminary study. This was conducted with a sample of hotels located in Phuket, Thailand. From this preliminary study, additional variables emerged as important. Hence, the next section will develop the modified TCE model and propose the hypotheses to be tested.

Research Design and Methodology

Based on the literature discussed and a previous qualitative study (Promsivapallop et al., 2007), a number of TCE variables were identified. The role of these independent variables in influencing the outsourcing decision (the dependent variable) was researched by looking at three different activities within the hotel—restaurant services, laundry, and guest transportation. These were chosen because they possess different characteristics; two are front of house (restaurant and guest transportation) and one has no guest contact; two are revenue generating (restaurant and guest transportation) and one is not (laundry in this study focuses on hotel linen only); and two process materials (restaurant and laundry) and one does not. The study seeks to address whether all outsourcing in the hotel industry is driven by the same factors, or whether it is contingent on the activity being outsourced.

Development of the Transaction Cost Economics Model

Based on the literature review discussed above, the following hypotheses were formulated:

Hypothesis 1: Asset specific investments required for a service transaction decrease the level of outsourcing.

Hypothesis 2: Environmental uncertainty decreases the level of outsourcing.

Hypothesis 3: Behavioral uncertainty decreases the level of outsourcing.

Hypothesis 4: More frequently conducted activities are outsourced less.

In addition to these factors, the preliminary qualitative study (Promsivapallop et al., 2007) was designed to identify other possible variables. Twenty-two managers were interviewed using the “critical incident technique” to elicit their opinions about outsourcing decisions they had made. From this, 64 separate outsourcing incidents were identified. These generally provided strong support for the TCE framework. Asset specificity appeared to be the most dominant factor, but the other TCE dimensions of environmental uncertainty, behavioral uncertainty, and frequency also exerted strong influences on the sourcing decisions made in the hotels investigated. In addition, the findings revealed nontraditional TCE factors. These included supplier availability, capital requirement, hotel experience, level of profit, guest contact, size of hotel, and level of service. These factors are discussed below.

Supplier Availability

The study revealed that hotels would prefer to outsource activities that had a higher level of supplier availability. Outsourcing activities that had a low level of supplier availability were perceived by the key informants to be too risky for the hotel. This result is consistent with Walker and Weber (1984), Vining and Globerman (1999), Bello et al. (1997), and Ono (2007). Generally speaking, high supplier availability leads to a reduction of opportunistic behaviors (Williamson, 1975) and thus a lower transaction cost. This is due to the threat to the contracts of a viable contractor replacement.

Capital Requirement

It was found that transactions requiring a high level of capital investment tended to be outsourced. Many hotels perceived that high capital committed activities would lead to higher production costs. This would especially be the case for smaller scale operators, as external suppliers tend to have lower production costs derived from serving multiple clients. This result confirms the proposition developed by Bello et al. (1997) who stated that transactions requiring high capital investment would generally relate positively to outsourcing.

Hotel Experience

More experienced hotels have a greater tendency to insource. As the hotel gains experience and expertise in doing a task, it learns to do it more effectively and efficiently. The cost of doing the work declines as the experience accumulates. This variable relates closely to the buyer experience concept identified by Walker and Weber (1984) and experience effect concept of Bello et al. (1997).

Level of Profit

The study revealed that hotels were reluctant to outsource anything that yielded high profit, such as restaurant and spa activities. Level of profit of the activity has not been identified as an outsourcing factor in the previous literature. The lack of attention to this factor is not surprising, given that outsourcing of revenue-generating transactions has been rarely investigated. Past research (such as Espino-Rodriguez et al., 2008; Espino-Rodriguez & Padron-Robaina, 2005a; Gilley & Rasheed, 2000; Gorg & Hanley, 2004) has examined only the relationship between the firm’s overall profit or performance and the level of outsourcing. However, the result of the preliminary study illustrates that level of profit is a factor affecting the outsourcing of revenue-generating activities.

Guest Contact

Many hotels reported that they tended not to outsource high guest contact activity because they did not want to put the hotel’s reputation at risk. This is consistent with Murray and Kotabe’s (1999) study as they discovered that firms would be more likely to source high guest contact services internally. In addition, high guest involvement increases the tight coordination needs of supply and demand (Erramilli & Rao, 1993) and close interaction of the employees between the service unit and the hotel. Hence, outsourcing would be problematic.

Size of Hotel

It was reported that larger hotels tended to outsource less. This was mainly because they felt that the larger size of hotel allowed them to gain cost efficiency in operating activities in-house. The informants explained that it would also help the hotel ensure the quality level of the operations. This result is consistent with the argument by Sorrel (2007) and Walker and Weber (1984) who found that larger firms were inclined to internalize internal audit activities, whereas smaller firms tended to outsource these services.

Level of Hotel Service

It was noted by Lamminmaki (2007) that type of hotel might be relevant to hotel outsourcing decisions. The previous study by Promsivapallop et al. (2007) also provided supporting evidence that upper market hotels tended to outsource less than hotels that offered lower levels of services, as they were not confident that the suppliers would be able to meet the level of services required by the hotel and hence it could subsequently damage the hotel’s reputation.

This led to the development of seven more hypotheses, as follows:

Hypothesis 5: Supplier availability increases the level of outsourcing.

Hypothesis 6: Capital requirement increases the level of outsourcing.

Hypothesis 7: Hotel experience decreases the level of outsourcing.

Hypothesis 8: Level of profit of the activity decreases the level of outsourcing.

Hypothesis 9: Guest contact decreases the level of outsourcing.

Hypothesis 10: Hotel size decreases the level of outsourcing.

Hypothesis 11: Level of service decreases the level of outsourcing.

These hypotheses are summarized in Figure 1.

Figure 1

Conceptual framework: Proposed Relationships Between Independent and Dependent Variables

Method

This study was cross-sectional using a postal survey, designed for self-completion by hotel managers. The General Manager was chosen to complete the questionnaire because it was apparent from the preliminary study that he or she made most of the key outsourcing decisions. Before administering the main survey, the questionnaire was pretested with 11 hotel managers in Phuket and the results were used to revise the questionnaire accordingly. Section 1 of the questionnaire requested the respondents to indicate their opinion about the factors affecting outsourcing decisions. Section 2 asked for the level of outsourcing in percentage terms. Section 3 required information about the hotel property. Other questions asked the respondents to indicate the actual figures relating to their hotel operations. A 7-point numeric-type scale was used to measure the opinion-related questions, where 1 = not at all and 7 = to a large extent. The choice of 7-point scale is driven by two reasons. First, it has been successfully used in previous hotel-outsourcing studies with an acceptable reliability (Espino-Rodriguez & Padron-Robaina, 2005a, 2005b; Lam & Han, 2005; Lamminmaki, 2007). Second, seven or more points are more suitable for multivariate analysis than smaller ranges (Tabachnick & Fidell, 2007). The fieldwork for this research was undertaken in Thailand during May-July 2007.

Variables and Measures

This section provides details of the measurement items and the scaling of the variables. The measurement instrument was adopted from past research, the conclusions drawn from the preliminary study, and the results of questionnaire pretest.

Dependent Variable—Level of Outsourcing

The dependent variable—outsourcing—was measured as a continuous variable rather than a discrete event. Respondents were asked to identify the level of outsourcing of each activity on a percentage basis. This is consistent with Lamminmaki (2007) and Poppo and Zenger (1998). Any response above 0% was judged to be a case of outsourcing, and the majority of these cases identified were completely outsourced (i.e., 100%).

Independent Variables

Asset specificity

Asset specificity is a multidimensional construct. The preliminary study’s results revealed three key elements of asset specificity, including procedural specificity, temporal specificity, and site specificity. Hence, the measurement of asset specificity in this research has been centered on these three dimensions. Furthermore, as in Lamminmaki (2007), an additional element of overall asset specificity has been incorporated to the measurement to pick up the general notion of asset specificity.

This study adopted the 7-point scale of measurement developed by Lamminmaki (2007), where 1 = not at all and 7 = to a large extent. The measure of each dimension was based on the definition of the concept discussed earlier. Procedural specificity was measured by the degree of work customization of the contractor to meet the hotel’s requirement. The extent of the effect on the hotel’s overall performance if the contractor fails to deliver the service on time was used to quantify the temporal dimension. Site specificity was measured by the degree of necessity for the contractor to conduct the service on-site. The measurement of general asset specificity was based on Lamminmaki (2007) by focusing on the level of investment the contractor would lose if the outsourcing contract was to be terminated.

The correlation level and the Cronbach’s alpha of the four items of asset specificity were low and thus they were treated as four separate variables. These variables were labeled as “procedural specificity,” “temporal specificity,” “site specificity,” and “general asset specificity.” Separating asset-specificity variables was adopted by Lamminmaki (2007) who also treated temporal specificity as a distinct variable. Likewise, Coles and Hesterly (1998b) also perceived that asset specificity consisted of different unique dimensions and thus used physical asset and human asset as two separate constructs for their hospital outsourcing research.

Environmental uncertainty

Environmental uncertainty was represented by two dimensions: operation requirement uncertainty and volume uncertainty. The former was measured by the likelihood of short-term unpredictable changes in each activity. The level of predictability of amount of work of each activity over the past 3 years was used as a proxy for the latter before the scale was later reversed on SPSS. This question was worded similarly to Lamminmaki (2007). Respondents were asked to rate their opinion for these two items based on a 7-point numeric scale where 1 = not at all and 7 = to a large extent.

However, these two items demonstrated low levels of both inter-item correlation and Cronbach’s alpha. Therefore, they were treated as two separate variables, namely “operation requirement uncertainty” and “volume uncertainty.”

Behavioral uncertainty

Two items were designed to gauge the behavioral uncertainty construct. One question asked to determine the level of difficulty for the hotel to judge the contractor’s quality of performance and another question asked the level of difficulty in determining whether the contractors comply with their established agreements with the hotel. Respondents were asked to rate on a numerical scale of 1 to 7, where 1 = not at all and 7 = very difficult.

Two questions, including “level of difficulty for the hotel to judge the contractor’s quality of performance” and “level of difficulty in determining whether the contractors comply with their established agreements with the hotel” were asked to measure this construct. The two indicators’ reliability was acceptable in all activities (Cronbach’s alpha between .693 and .759) and hence the two items were combined to represent one construct.

Frequency

Respondents were asked to indicate for each activity the number of employees the hotel would require if the job was conducted in-house to gauge the volume of work or transaction frequency.

Supplier availability

One item was used to measure supplier availability for each activity by asking the respondent to indicate their opinion about the level of availability of qualified suppliers in the market on a 7-point numeric scale of 1 to 7.

Guest contact

The level of guest interaction was used to measure guest contact levels. The word “interaction” was used rather than “contact” to emphasize the nature of relations between the service provider and the guest in the hotel service delivery rather than the employee simply being visible to the guest without actual interaction. The respondents were asked to rate their opinions on a 7-point numeric scale.

Capital requirement

Measuring capital requirement was based on the findings of the preliminary study. A single item was used to capture the level of capital required to operate the activities. Capital refers to long-term nonhuman assets. Each respondent was asked to indicate the level of capital investment required for each activity if it was performed internally on a scale of 1 to 7. Examples of buildings and equipment were included in brackets at the end of the survey question to clarify the meaning of “capital.”

Hotel experience

Hotel experience refers to the knowledge and expertise of the hotel management team in operating a certain activity. This variable was measured with a single item and it was adapted from Walker and Weber (1984). Each respondent was asked to rate the level of expertise they would consider themselves having in operating the activity on a scale of 1 to 7.

Level of profit

Respondents were asked to indicate the most representative profit percentage of the restaurant operation and guest transportation in terms of the hotel’s total profit figure.

Size of hotel

Two questions were used to measure the size: the number of guest rooms and number of employees. Number of guest rooms has been used to indicate hotel size by previous researchers, such as Sahadev and Islam (2005) and Chen and Dimou (2005). Number of employees has been used to specify the size of the organization in other research, such as that carried out by Harrington and Kendall (2006). These two items showed a high level of reliability of all activities (Cronbach’s alpha between .847 and .869). As the two items were both transformed by logarithm, they were combined to form one construct.

Level of service

Two indicators, including the ratio of number of employees in hotels per number of available rooms and the room rate were used to measure the level of hotel service. They illustrated a satisfactory reliability of scale in all activities (Cronbach’s alpha between .754 and .766). Since all items have been transformed by logarithm, these two items were combined to form one measure.

Results and Discussion

This section presents the findings of the study and discusses these in relation to both the TCE and outsourcing literatures. It begins by reviewing the characteristics of the sample before presenting the results of the multiple regression of each of the outsourced functions being researched.

The population for this study was drawn from a comprehensive list of all hotels in Thailand as identified by the Tourism Authority of Thailand as of January 2006. Hotels unlikely to offer restaurant and/or guest transportation were removed. This left 2,765 hotels across Thailand as the total population. At the end of the survey period, 391 completed questionnaires were returned providing a return rate of 14.6%. This response rate is similar to other outsourcing studies that employed a postal survey, such as Everaert et al. (2006) who gained a response rate of 11.3% and 17% reported by Gilley and Rasheed (2000).

The sampled hotels were compared with the population and no major differences were found. Therefore, the responding hotels reflect the actual mix of the geographic population of hotels in Thailand. After data were reviewed, there were 306 responses in relation to restaurant outsourcing, 328 responses for laundry, and 312 responses for guest transportation valid for analysis. On average, guest transportation was outsourced highest (29% of the responses), whilst the sampled hotels outsourced 24% of their laundry and only 11.4% of their main restaurant. The demographic profile of respondent hotels is shown in Table 1.

Table 1

Respondent Demographic Profile

Variable	Frequency	Percentage
Types of hotels
Hotel with international chain affiliate	39	10.2
Local hotel	344	89.8
No of rooms
<100	225	58.7
100-199	104	27.2
≥200	54	14.1
Annual average room rate (per night)
<1,000 Baht (US$28.58)	160	48.6
1,000-1,999 Baht (US$28.58-57.12)	98	29.8
≥2,000 Baht (US$57.15)	71	21.6

Note. Currency conversion rate as at March 2009 (US$ 1 = 35 Baht).

Data were screened and cleaned based on the recommendation of Tabachnick and Fidell (2007) and Hair, Black, Babin, Anderson, and Tatham (2006). Data input was inspected for accuracy. Outliers were screened and the distribution of variables was examined. A number of variables exhibited skewness and outliers and thus were transformed to achieve normality and the outliers subsequently disappeared. To test for multicollinearity, a correlation matrix of the dependent variable (the outsourcing decision) and independent variables was prepared. Multicollinearity was not a concern in both tables, as none of the correlation exceeds .70, the cutoff level suggested by Pallant (2006).

Correlation

The correlation statistics between the predictor variables and dependent variable in each data set is reported in Table 2. The correlation results provided mixed outcomes to a number of hypothesized relationships. The bulk of the statistically significant predictor variables had the expected correlation signs, except site specificity of guest transportation and volume unpredictability and guest contact of restaurant.

Table 2

Pearson Product–Moment Correlations Between Dependent and Independent Variables

Variable	Level of Outsourcing
Variable	Restaurant	Hotel Laundry	Guest Transportation
Procedural specificity	−0.06	0.04	0.06
Temporal specificity	−0.13*	0.06	0.04
Site specificity	−0.06	0.05	0.13*
General asset specificity	−0.07	−0.23**	−0.11*
Operations unpredictability	−0.01	−0.03	0.02
Volume unpredictability	0.22**	0.06	0.03
Behavioral uncertainty	0.00	−0.04	−0.08
Frequency	−0.16**	−0.02	0.04
Supplier availability	0.19**	0.31**	0.26**
Guest contact	0.20**	−0.02	0.00
Capital requirement	0.00	0.072	0.11*
Hotel experience	−0.08	−0.22**	−0.10**
Level of profit	−0.23**	n/a	−0.10
Size	−0.22**	−0.13*	−0.10
Level of service	−0.07	0.06	0.10

Note. Signs have been changed for variables being transformed by reflecting. n/a = not applicable.

p < .05. **p < .01.

Overall, the relationships between the predictor variables and the dependent variable were not very strong. The majority of the variables had correlation r less than .1. However, variables that demonstrated significant statistic (p < .05) and correlation r higher than .1 or −.1 in at least two data sets included general asset specificity, supplier availability, and hotel experience and hotel size.

In order to examine the effects of all independent variables on level of outsourcing, standard multiple regression analysis was conducted in each activity. Tolerance and variance inflation factor were examined and multicollinearity was not a concern. Table 3 reports the results of multiple regression on each activity and the summary of hypotheses testing results can be found in Table 4.

Table 3

Results of Multiple Regression

Variable	Restaurant				Hotel Laundry				Guest Transportation
Variable	Standard β	SE	t	Significance	Standard β	SE	t	Significance	Standard β	SE	t	Significance
Constant		.46	1.39	.16		.37	2.20	.03		.78	2.02	.05
Procedural specificity	.10	.03	1.50	.13	.04	.03	.65	.51	.07	.05	.67	.50
Temporal^a	.08	.20	1.14	.25	−.09	.20	−1.53	.13	−.07	.36	−.64	.52
Site specificity	.00	.02	.07	.94	.08	.02	1.41	.16	.08	.05	.72	.47
General asset specificity	−.12	.02	−1.90	.06	−.23	.03	−4.08	.00	−.15	.05	−1.37	.17
Operations unpredictability	.03	.03	.43	.67	−.06	.03	−1.02	.31	−.12	.05	−1.06	.29
Volume unpredictability	.22	.03	3.23	.00	.07	.04	1.24	.22	−.06	.07	−.55	.58
Behavioral uncertainty	.12	.01	1.76	.08	−.02	.02	−.30	.76	.00	.03	.02	.99
Frequency	.15	.21	1.52	.13	.13	.20	1.72	.09	.18	.32	1.28	.21
Supplier availability	.24	.02	3.75	.00	.22	.03	3.81	.00	.21	.05	2.04	.04
Capital requirement^a	.02	.19	.25	.80	−.14	.20	−2.18	.03	.11	.06	1.03	.31
Hotel experience	−.04	.19	−.59	.55	−.24	.03	−4.05	.00	−.27	.06	−2.46	.02
Guest contact^a	−.15	.15	−2.18	.03	.02	.02	.30	.76	.03	.33	.32	.75
Level of profit	−.20	.00	−3.19	.00	n/a	n/a	n/a	n/a	−.16	.21	−1.42	.16
Size of hotel	−.14	.08	−1.46	.14	−.23	.08	−2.78	.01	−.37	.16	−2.52	.01
Level of service	−.11	.08	−1.61	.11	.14	.09	2.25	.06	.13	.16	1.13	.26
d/f	15/219				14/255				15/102
R ²	.23				.26				.25
Adjusted R²	.17				.22				.12
F	4.29, significance = .00				6.42, significance = .00				1.96, significance = .03

Note. Dependent variable = level of outsourcing; SE = standard error; n/a = not applicable.

Variables that were reflected and they must be interpreted in an opposite direction.

Table 4

Overview of Hypotheses Testing Results Across Different Activities

Variable	Hypothesis	Hotel Operational Activity
Variable	Hypothesis	Restaurant	Hotel Laundry	Guest Transportation
Procedural specificity	1
Temporal specificity
Site specificity
General asset specificity			***
Operations unpredictability	2
Volume unpredictability		* ( − )
Behavioral uncertainty	3
Frequency	4
Supplier availability	5	***	***	*
Guest contact	6	* ( − )
Capital requirement	7		*
Hotel experience	8		***	*
Level of profit	9	**	n/a
Hotel size	10		**	*
Level of service	11

Note. Dependent variable = level of outsourcing; n/a = not applicable.

p < .05. **p < .01. ***p < .001. ( − ) indicates reverse result.

Overall, the model illustrated a low predictive power in all three data sets with the adjusted R² of 17% for restaurant, 22% for hotel laundry, and 12% for guest transportation. The F test was significant in all three data sets. Hotel laundry has the highest predictive power and it has five variables contributing to the explanation of the laundry outsourcing.

Supplier availability (Hypothesis 5), hotel experience (Hypothesis 8), and hotel size (Hypothesis 10) were the most consistent predictors, whereas level of profit (Hypothesis 9) was supported in one of two data sets and capital requirement (Hypothesis 7) was substantiated in one of the three activities. Asset specificity (Hypothesis 1) was partially supported in the laundry data set. Volume unpredictability, which is part of environmental uncertainty (Hypothesis 2), and guest contact (Hypothesis 6), however, were inversely related in the restaurant data set. All other variables were statistically insignificant and thus did not support the respective hypotheses.

As predicted, supplier availability had a positive relationship with the level of outsourcing and it was successfully confirmed in all three data sets (restaurant, β = .24, t = 3.75, p < .01; laundry, β = .22, t = 3.81, p < .01; guest transportation, β = .21, t = 2.04, p < .05). The result indicated that the respondent hotels tended to outsource more where there were more suppliers available in the market. This variable demonstrated the most consistent results, more than any other variable. Therefore, Hypothesis 5 was strongly supported. This finding confirms previous literature, such as Ono (2007), Pisano (1990), and Bello et al. (1997) in the generic business context and research studies undertaken by Lamminmaki (2007), Lai (2007), and Lam and Han (2005) in the hotel industry, where it was found that the level of outsourcing tended to increase as the supplier market became more competitive. Lamminmaki (2007), in particular, demonstrated that outsourcing in Australia was greater in hotels located close to a big city because access to specialist suppliers was less problematic. Furthermore, the finding is also consistent with Ono’s (2007) study, which found that the level of outsourcing is greater in “thicker” local markets.

Asset specificity was expected to have a negative relationship with the level of outsourcing (Hypothesis 1). This hypothesis was partly validated only in the laundry data set (general asset specificity, β = −.23, t = −4.08, p < .01) but no support was evidenced in any other data sources. This was contrary to most TCE studies of outsourcing, which found this factor to be the most influential. It was also found that capital requirement was positively related to level of outsourcing (Hypothesis 7) in one of these three data sets, namely laundry (β = .14, t = 2.18, p < .05). This indicates that the sampled hotels were reluctant to operate laundry in-house because it needed a high level of capital commitment. However, the hypothesis was not substantiated in restaurant and guest transportation.

Hotel experience was hypothesized to relate negatively to the degree of outsourcing (Hypothesis 8). It was expected that hotels would hesitate to outsource anything in which they had operational expertise and would prefer to outsource the activities where they lacked know-how and experience. The finding provided substantiation to this hypothesis in two data sets, namely laundry (β = −.24, t = −4.05, p < .01) and guest transportation (β = −.27, t = −2.46, p < .05). The hotel experience result is consistent with the empirical finding of Bigelow and Argyres (2008) and it confirms the production cost hypothesis of Bello et al. (1997). But it contradicts Walker and Weber’s (1984) contention for a positive relationship between buyer experience and outsourcing based on the transaction cost perspective. These results indicate that the experience of the organization may affect its production costs more than transaction costs. Furthermore, prior knowledge and experience of the hotel in operating certain activities is consistent with the capability concept of resource-based view. This finding can be compared with the resource-based view–motivated observations made by Espino-Rodriguez and Padron-Robaina (2005a) and Espino-Rodriguez and Gil-Padilla (2007). These researchers found that hotels do not outsource activities where they have greater capability than external suppliers. Hotels would have lower operational capability in the activities they choose to outsource. Other hotel outsourcing research, including Lamminmaki (2007), also validates hotel experience as a strong predictor of outsourcing.

Likewise, the level of profit of the activity was anticipated to have a negative effect on the level of outsourcing (Hypothesis 9). In other words, hotels would hesitate to outsource any activity that could generate a high direct profit. This hypothesis was confirmed fort he restaurant activity (β = −.20, t = −3.19, p < .01) but no support was found in guest transportation.

As expected, because of the variation in the characteristics of each activity being investigated, separate analysis provided inconsistent results. It appears that the level of outsourcing was only slightly explained by the predictor variables in all data sets. Each activity tended to have a different mix of significant variables in explaining them. Therefore, this suggests that the activities were not homogenous in terms of outsourcing factors. This mirrors the different characteristics of each activity explained earlier. Furthermore, as expected, the variability of the features among these activities made each one unique for the managers to make outsourcing decisions. In other words, if these variables were to be used in a generic outsourcing model, it might successfully predict outsourcing of one activity but would not effectively predict outsourcing of other activities.

Supplier availability, hotel experience, and hotel size can be identified as providing the most consistent results across the data sets in terms of significant contribution to the model and confirmation of the hypotheses. Furthermore, the results indicate that only supplier availability can be generalizable to all data sets. The decision to conduct a preliminary study to identify additional factors was vindicated since these were shown to be the factors that had influence in at least one activity that was outsourced.

In addition, the results have demonstrated that the activities are not homogenous in terms of factors explaining their levels of outsourcing. As laundry operations require a high level of capital investment and the hotel is small in size, outsourcing would be preferred to take advantage of the contractor’s economies of scale, provided that the contractor can capture these scale economies. Smaller hotels were not able to afford in-house operations of a high capital activity like laundry operations because it was uneconomical. Decisions to outsource guest transportation were similar to laundry operations, but the outsourcing decision of the former is less complex. Hotels determined whether to outsource these services based on the availability of suppliers in the market, as well as the level of expertise in these operations possessed by hotel management.

On the other hand, hotel restaurant operations are characterized as high in level of profit and level of volume unpredictability and hence should not be outsourced. The attractiveness of hotel restaurant profitability observed in this research is similar to the finding of a study into profitability of hotel food and beverage in Thailand by Chaiyasain (2003). The latter found that, apart from income from accommodation, the food and beverage department in a hotel in Thailand normally generates a much higher level of profit than any other service. One of the reasons explaining this is because guests tend to spend a much larger proportion of their budget on food and beverage in the hotel than laundry, transportation, and any other in-house services. According to the Tourism Authority of Thailand (2007), the size of tourist spending on food and beverage was ranked third after accommodation and shopping. In addition, a number of hotel managers in the preliminary study clarified that they would never outsource their restaurants because of their high profitability. Hence, this finding may suggest that only a high level of profit of the activity matters to the outsourcing decisions. Therefore, hotel outsourcing decisions are contingent on the type of hotel operational activities.

Conclusion

This study examined the effects of factors on hotel outsourcing by drawing on TCE and additional factors identified from the preliminary study. The results showed some support for TCE and mixed support for nontraditional TCE factors. The most dominating factors confirmed in this study were supplier availability, hotel experience, and hotel size. But probably the most significant finding of this research is the relative failure of this modified TCE model to explain outsourcing in the hotel industry in Thailand. This is despite the fact that it incorporated seven new factors that were derived from discussions with hotel operators. Given the results of previous TCE studies, it would seem that this is either to do with the business environment (i.e., Thailand) or the sector under investigation (i.e., hotels).

With regard to the business environment, Thailand is a developing economy in which market conditions are different to developed economies where TCE originated. Olsen, Sharma, Echeveste, and Tse (2008) have argued that theories originating in developed countries may not apply to the developing world because of different business environments. TCE theory is based on the assumption that supplier market competition offers more efficiency and lower production cost than in-house operations. But this scenario generally occurs only in a competitive market as the suppliers are faced with competitive pressures to price low and provide efficient and quality services to survive the competition (Vining & Globerman, 1999). For this reason, in developed countries, products and services such as transportation, restaurants (Hallam & Baum, 1996; Ruggless, 2004), and laundry (Leger, 2004) are commonly provided outside the hotel industry. In these instances, the suppliers are abundant and compete to provide numerous outsourcing options to hotels and the outsourcing decisions could well be dictated by the cost of each transaction. In developing countries, this is not necessarily the case; suppliers are relatively few, they are not necessarily efficient, and they do not necessarily compete on price. Hence, the results of this study are similar to other hotel outsourcing studies in developing economies, such as Lai (2007) in Taiwan and Lam and Han (2005) in China, where the supplier markets are immature. Another potential factor is the relatively low cost of labor in developing countries, such as Thailand. This means that providing services in-house is not costly and there is no incentive to find a cheaper, market-based supplier.

With regard to the hotel industry itself, there are other factors that may mitigate against outsourcing. Historically, hotel operators have adopted control over all aspects of the operation that deliver service in order to control standards and support the brand. In developing countries, the hotel industry is still dominated by owner operators and these comprise mainly small- and medium-sized hotels (Table 1). The owners and managers of such hotels clearly make outsourcing decisions that cannot be explained by TCE. Evidence from the qualitative study suggests that personal or family contacts play a role in this.

In summary, this research identified that traditional TCE on its own might not fully explain outsourcing practices and therefore TCE was modified by incorporating additional new non-TCE variables. Hence, this study adds a revised outsourcing framework to the literature. The findings highlighted the fact that each activity is different in terms of the factors influencing its outsourcing and thus a generic outsourcing framework may not be applicable. Furthermore, it is suggested that TCE is less useful in an economy that lacks supplier competition.

In addition, a number of practical and managerial implications are raised from this research.

Hotel managers are advised to reflect on the findings when making outsourcing decisions by following the effects of the determinant factors, types of properties, and types of activities. It is likely that the degree of outsourcing will be greater in small hotels. Activities that are possible to be outsourced will be those that are high in supplier availability, low in hotel experience, and those unable to generate a high level of direct profit to the hotel. The findings have highlighted the importance of supplier availability. As the outsourcing market is immature, a very small number of quality outsourcing suppliers are currently available to hotels in Thailand. This is comparable with the situation in China as described by Lam and Han (2005) and thus a similar recommendation to these authors is offered. As a consequence of low supplier availability, hotel management had difficulty naming trustworthy and competent contractors. It is very important for hotel management to evaluate the expertise, past performance, and reputation of identified potential contractors when making outsourcing decisions. Management should make site visits to other hotels where the supplier is operating and test the services, if practical, to gather background information for consideration.

Furthermore, this study has indicated that a combination of low-quality suppliers and high capital requirement present great pressures to smaller properties. Small hotels are advised to outsource a high capital activity, such as laundry to realize the economies of scale provided by the contractors. However, as there are currently only a few competent suppliers available for outsourcing, hotels in Thailand are presented with the following recommendations:

Independent small- and medium-sized properties are advised to form alliances in order to coinvest and share the high capital costs of operations, such as laundry. The alliance operation can be a separate company that acts as a profit center but its ultimate goal is to provide the efficient services expected by the member hotels. However, investing in and running operations such as a laundry by the networked group of hotels may require special and different sets of expertise other than hotel management skills. An alternative option is to make use of the sheer size of the group to negotiate and put pressure on existing suppliers to provide improved services to alliance members.

Chain hotel properties within close proximity are also advised to operate a central operation to deal with the high capital services by offering services to the hotels within the chain. This can also help reduce temporal and procedural specificity required by the chain. This strategy has been successfully practiced by a number of chains in Phuket.

From a policy perspective, the Thai government should consider creating programs and incentives to support the development of companies that provide supporting services to the hotel sector. This might increase the quality of suppliers and the rivalry amongst them, ultimately resulting in a more competitive hotel industry.

One limitation of the study is that it only examined a selection of activities in hotels in Thailand; hence, the findings may not be generalizable to other activities and hotels in other countries. Similar studies, comparing emerging countries and developed economies and different hotel activities or different industries would be helpful in illustrating whether TCE is in fact applicable to other types of economies, hotel activities, and industries. In addition, there are other theories that explain outsourcing, such as the resource-based view and agency theory. Hence, future studies could usefully extend the analysis to these themes as well.

Footnotes

Pornpisanu Promsivapallop, PhD (promsivapallop@gmail.com) is a lecturer and Head of the Hospitality Management Research Unit, Faculty of Hospitality and Tourism, Prince of Songkla University, Phuket, Thailand. Peter Jones, PhD (profpjones@gmail.com) is Brahim Chair at the Universiti Putra Malaysia, Professor Emeritus at the University of Surrey and Founder of . Angela Roper, PhD (angela.roper@uwl.ac.uk) is Professor and Director of the International Centre for Hotel and Resort Management, the University of West London, London, United Kingdom.

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