The meetings, incentives, conferences, and exhibitions (MICE) industry:
Determinants of Thai organizational effectiveness
Songsiri Bandhuseve
Doctoral Candidate
Faculty of Administration and Management
King Mongkut’s Institute of Technology Ladkrabang (KMITL)
1 Chalong Krung, Thanon Chalong Krung, Lat Krabang
Bangkok 10520, Thailand
Sunpasit Limnarat
Assistant Professor
Industrial Engineering – Faculty of Engineering
King Mongkut’s Institute of Technology Ladkrabang (KMITL)
1 Chalong Krung, Thanon Chalong Krung, Lat Krabang
Bangkok 10520, Thailand
Sorasak Tangthong
Dr.
Faculty of Administration and Management
King Mongkut’s Institute of Technology Ladkrabang (KMITL)
1 Chalong Krung, Thanon Chalong Krung, Lat Krabang
Bangkok 10520, Thailand
Reference to this paper should be made as follows:
Songsiri Bandhuseve, Sunpasit Limnarat and Sorasak Tangthong
Bandhuseve, S., Limnarat, S., & Tangthong, S. (2017). The Meetings, Incentives, Conferences, and Exhibitions (MICE) Industry: Determinants of Thai Organizational Effectiveness. Asian International Journal of Social Sciences, 17(1), 125 – 175. Retrieved from https://aijss.org/index.php//
Abstract
Studies have shown that there is more money in business tourism than leisure travel, and on average, business travellers spend more money. To understand this phenomenon, this study aimed to investigate the effect of relationships between capacity management, customer relationship management, information computer technology (ICT), service quality, supplier relationship management, and supply chain management on Thailand’s meetings, incentives, conferences, and exhibitions (MICE) industry organizational effectiveness. The researchers embraced a descriptive survey methodology designed to assess how the 500 managers surveyed viewed their organization’s effectiveness. The design employed the self-administration of questionnaires to a sample of individuals which was aimed at finding each individual’s attitudes and opinion about how the 21 observed variables impacted their operations. Of the 10 hypotheses and 21 observed variables, nine hypotheses were proven, with the findings confirming that service quality and information computer technology having a significant effect on MICE organizational effectiveness.
Keywords: capacity management, customer relationship management, eTourism, information computer technology, service quality, supplier relationship management, supply chain management, tourism
Introduction
Travel & Tourism in 2016 generated US $7.2 trillion (9.8% of global GDP) and supported 284 million jobs, equivalent to 1 in 11 jobs in the global economy (World Travel & Tourism Council, 2016). International tourist arrivals also surged, reaching nearly 1.237 billion, with visitors from emerging economies now representing a 46% share of these international arrivals (up from 38 % in 2000). In 2017, early data shows global direct Travel & Tourism GDP growth remained resilient in 2016 and continued to grow faster than the wider global economy of 3.1% (World Travel & Tourism Council, 2016). From the above statistics, a significant share is represented by the meetings, incentives, conferences, and exhibitions (MICE) industry, with Thailand in 2015 welcoming a total of 1,086,229 international MICE visitors, which generated US$ 2.673 billion in revenue (Thailand Convention and Exhibition Bureau, 2016).
“Let’s meet in Thailand”
In opening ceremony statements from the Sixth Tokyo International Conference on African Development in August 2016, it was stated that both studies and experience have shown that there is more money in business tourism than leisure travel, and on average, business travellers spend more than leisure travelers (Mureithi, 2016; CBI, 2014). A World Tourism Organization (2014) report also confirms this and stated that there are immense benefits to the broader economy as it generates on average a higher spending level, reduces seasonality, contributes to the regeneration of destinations, spreads knowledge and enhances innovation and creativity (Reeve, 2014). Additionally, business travelers focus more on exploration and individual restoration events and destinations, thereby creating even higher expenditures per trip over group tourism.
According to the International Association of Professional Congress Organizers (IAPCO), the MICE industry is defined as ‘Meetings, Incentives, Conventions, and Exhibitions’. In this segment in Thailand, growth is robust, catering to 12 key economic sectors in five primary metro areas of Bangkok, Pattaya, Chiang Mai, Phuket, and Khon Kaen (Thailand Convention and Exhibition Bureau, 2014). This is consistent with World Tourism Organization (2014) research which indicated that destinations must become a brand for business sources within the meetings world, and Thailand is becoming a leader in this area.
The main Thai MICE markets are the surrounding growing Asian economies. The Thailand Convention and Exhibition Bureau (TCBE) (2016) ranked the top ten Thai MICE visitor nations as China, India, Singapore, Malaysia, US, Korea, Indonesia, Vietnam, Japan, and France.
As part of Thailand’s MICE strategy, the Kingdom’s TCBE has developed what it refers to as ‘three fundamental pillars’ which includes ‘destination, business and people’. There is also a great emphasis placed on corporate social responsibility (CSR) in MICE tourism with sustainable beach destinations and grassroots ecotourism encouraged, where activities are designed in collaboration with local villages, based on the principle of community-based tourism (Thailand Convention and Exhibition Bureau, 2016). Furthermore, TCEB has implemented a 5-year MICE Sustainability Master Plan (2015-2019) to turn Thailand into the world’s leading sustainable destination for MICE and initiated a ‘Farm to Functions’ project to encourage Thai MICE operators to purchase organic agriculture products directly from farmers (Thailand’s MICE industry, 2016).
Meetings
The ‘M’ in MICE stands for the word “meeting” (Table 1), which is defined as a gathering of 10 or more participants for a minimum of 4 hours in a contracted venue (World Tourism Organization, 2014; CBI, 2014). Meetings can partially be analysed globally by data from the International Congress and Convention Association (ICCA), which for the first time in 2015 released a report on international association meetings (ICCA, 2015).
ICCA represents the main specialists in organizing, transporting, and accommodating international meetings and events, and comprises almost 1,000 member companies and organisations in over 90 countries worldwide. From the report, the top five countries by estimated total number of participants is made up of the same countries as the ranking by number of meetings. Also, Paris was the number one meeting city with 130,516 participants attending 214 meetings, while Singapore had 57,497 attendees going to 142 meetings and was the only city in ASEAN to make the top 10 list ICCA (2015). Based on these numbers, it appears that ‘M’ is but a small part of Thailand’s total MICE equation.
Other research by the World Tourism Organization (2014), indicates however that the meetings industry can be a key driver of the tourism sector’s development and an important generator of income, employment, and investment. It can also be a key driver of tourism destination development, and therefore an important generator of income, employment, and foreign investment.
| Meetings | A gathering of 10 or more participants for a minimum of 4 hours in a contracted venue (World Tourism Organization, 2014; ICCA, 2015; CBI, 2014). |
| Incentives | Incentive travels include leisure trips emphasizing pleasure and excitement and which may appear to have little or no connection to business (Thailand Convention and Exhibition Bureau, n/d). |
| Conventions | An event where the primary activity of the attendees is to attend educational sessions, participate in meetings/discussions, socialize, or attend other organized events. (Thailand Convention and Exhibition Bureau, n/d). |
| Exhibitions | Exhibitions can generally be portrayed as ‘presentations of products and services to an invited audience with the object of inducing a sale or informing the visitors’ (Thailand Convention and Exhibition Bureau, n/d). |
Incentives
Disney’s US$2.2 billion in profits in 2014 came from 11 theme parks strategically located around the world (Sylt, 2014), and the theme park’s connection to a world icon ‘mouse’. Although ‘Mickey Mouse’ has nothing to do with Thailand’s MICE industry, it is a key factor in Disney’s corporate MICE segment as employee families and their children play an important role in many corporate decisions concerning convention locations.
This might be a good example of the letter ‘I’ in MICE where incentives are used to motivate location decisions, which in this case is ‘family entertainment’. In Thailand in 2010, spending on incentive travel within in the MICE sector represented 20 % of the total spending and was estimated to be around US$ 300 million (Thailand Convention and Exhibition Bureau (n/d).
Conventions
Convention visitors are usually there for the convention itself, and not for the destination in which it is being hosted. Further, attendees often come at times of the year when other kinds of visitors won’t, which helps support the development and maintenance of tourism infrastructure. Additionally, delegates are typically bigger spenders than the average tourist visitor, and are often on corporate expense accounts.
This is supported by Australian research which indicates that international business events delegates spend, on average, 21 % more than other international visitors over the course of their trip and 77 % more per day. To be specific, while general international visitors spend $2,456 per trip and $84 per day, visitors to international business events spend $2,960 per trip and $149 per day (Abeysinghe, 2016). This means they stay, on average, in more expensive accommodation and are able to indulge in more costly restaurants and transportation options.
Exhibitions
Exhibitions can generally be portrayed as ‘presentations of products and services to an invited audience with the object of inducing a sale or informing the visitors’ and according to the Thailand Convention & Exhibition Bureau (n/d), net exhibition space in Asia is experiencing a compound annual growth rate of 20 %. Hosting an exhibition can also be one medium to channel products and services to potential buyers on a regional and global scale and can also be a potential for foreign direct investment (FDI), spurring technology and innovation. This is supported by the World Travel and Tourism Council (WTTC, 2016), which indicated that Travel & Tourism investment was US$ 814.4 billion, or 4.3% of total investment. In the coming years, this is expected to increase by 4.6% yearly over the next ten years to US$ 1. 34 trillion in 2025 (4.9% of total).
Thailand’s 2015 MICE Industry – A ‘Snapshot’
Fortunately for researchers, Thailand’s TCEB maintains some detailed and up-to-date online statistics of the MICE industry. Figure 1 shows the total number of MICE events (as well as ‘mega-events’=G) beginning in 2006 through 2015. Meetings by far lead the overall numbers hovering at nearly 8,000 events over the past several years. Mega-event flagship (MEF) events are also tracked by the TCEB as indicated by the letter ‘G’, which research from Deng (2012) has indicated helps accelerate urban renewal.
When the researchers went through the series of graphs available online however, upon reaching the ‘length of stay’ graph (Figure 2), there appears to be a significant and continuous decline in the total number of days stayed in Thailand for each event. As of yet, the causes for this are undetermined and a potential subject for future research.
Figure 1 MICE events in Thailand from 2006 to 2015
Note. Thailand Convention and Exhibition Bureau (2015)
Figure 2 Average number of MICE days spent in Thailand from 2004 to 2015
Note. Thailand Convention and Exhibition Bureau (2015)
Problem Statement
The Meetings, Incentives, Conference, and Events (MICE) segment is a vital component of Thailand’s large and growing tourism sector, with Thailand becoming a MICE hub to the 622 million members of the Association of Southeast Asian Nations (ASEAN). Therefore, understanding how the MICE industry’s organizational effectiveness is influenced is therefore crucial.
Literature review
Supply Chain Management (supplyCM)
Effective supply chain management (SCM) has become a key component in securing competitive advantage and improving organizational performance since competition is no longer between organizations, but among supply chains (Li, Ragu-Nathan, Ragu-Nathan, & & Rao, 2006). Additionally, both the level of information sharing and the quality of information within SCM lead to an organization’s’ better competitive advantage.
The TCEB discusses MICE strategy and implementation using the same terms one finds in modern supply chain and logistics management literature. Logistics in the case of MICE therefore becomes the operations/conceptual term for MICE coordination, planning, and organizational management within Thailand’s 12 key economic sectors. These sectors include automotive, electronics, food & agribusiness, medical & healthcare, rubber, agriculture, energy, plastics, infrastructure, tourism, education, and professional services (Thailand Convention and Exhibition Bureau, 2014).
Speed (X1) – SCM aims to satisfy customer needs at the right time with the right products. In today’s competitive tourism environment, the sheer number of tourism service suppliers provides abundant input possibilities for tour operators to assemble tour packages, and it is believed that effective integration of suppliers with tourism product development processes could increase the competitive edge of tour operators as well as the tourism supply chain as a whole (Zhang, Song, & Huang, 2009).
Cost (X2) – Simchi-Levi, Kaminsky, and Simchi-Levi (2003) defined SCM as “a set of approaches utilized to efficiently integrate suppliers, ……in order to minimize system-wide costs while satisfying service level requirements.”
Efficiency (X3) – Lee, Padmanabhan, and Whang (1997) stated that ICT (information computers and technology) in the SCM has proven to have a positive impact, particularly in relation to procurement, as ICT enhances collaboration and improves information quality shared between suppliers and buyers. ICT also reduces interaction and transaction costs (Aiello, Dulskaia, and Menshikova, 2016).
From the above and other research, the researchers therefore developed the following hypothesis:
H1: Information Computers and Technology (ICT) has a direct positive affect on Supply Chain Management (supplyCM).
Supplier Relationship Management (supplyRM)
Supplier relationship management (SRM) is the discipline of strategically planning for, and managing, all interactions with third party organizations that supply goods and/or services to an organization in order to maximize the value of those interactions.
To compete in today’s global markets, it is imperative that organizations deliver their products and services in both an efficient and effective manner. One of the strategies is through Supplier Relationship Management (SRM), as increased competition motivates organizations to improve efficiencies and anticipate uncertainties in their supply chain process (Hong & Zailani, 2011).
Agility (X4) – Studies have found the top procurement teams that have the agility to reduce risk factors (Rizza, 2015). This is consistent with Agarwal, Shankar, and Tiwari (2007) which indicated that agility is the fundamental characteristic of a supply chain needed for survival in turbulent and volatile markets.
Supplier relationships (X5) – SRM technology is the key to effective SRM, as it allows easy access to suppliers and helps with the analysis of risk (Rizza, 2015), and according to Agarwal et al. (2007), poor relationships with suppliers and customers are the recipes of business failure.
Performance Evaluation (X6) – The evaluation of a supply chain is crucial for an organization to survive in a competitive market in a globalized business environment (Fan and Zhang, 2016).
From the above and other research, the researchers therefore developed the following hypothesis:
H2: Information Computers and Technology (ICT) has a direct positive affect on Supplier Relationship Management (supplyRM).
Information Computers and Technology (ICT)
Internet (Y1) – In the Philippines, a very pricey and snail-paced internet service has been noted as one of the biggest obstacles to tourism growth, most particularly with Cebu’s positioning as a MICE destination (Dagooc, 2016).
In Singapore, the critical nature of seamless connectivity to the Internet for MICE attendees has been recognized with the newest spaces such as Singapore Expo’s new Max Atria being designed with seamless, free, and high-speed Internet connectivity (World Tourism Organization, 2014). Similar technology was rolled out at Suntec Singapore after a S$180million project that saw the 18-year-old venue undergo a major refurbishment. Key to its refurbishment project was the installation of a new system that allows for up to 6,000 devices to be logged into the internet at the same time. Increasingly, ICTs play a critical role for the competitiveness of tourism organisations and destinations as well as for the entire industry as a whole.
Knowledge Management (Y2) – Kyriakidou and Gore (2005) discussed knowledge management (KM) and stated that KM plays a vital role in the knowledge and information flow in shared and collaborative settings of missions and strategies. This was consistent with a meta-analysis study conducted by Hallin and Marnburg (2008) which concluded that for hospitality companies, KM is especially relevant for building up competitive advantage.
Information Technology Management (Y3) – Sheldon (1997) argued that the tourism and hospitality industry is one of the largest users of information technology (IT), with service delivery occurring as a result of interaction between customers and employees and where it is required that employees are knowledgeable of customers’ needs in order to achieve customer satisfaction (Kotler, Bowen, & Makens, 1999; Kahle, 2002).
From the above and other research, the researchers therefore developed the following hypotheses:
H3: Information Computers and Technology (ICT) has a direct positive affect on Customer Relationship Management (CRM)
H4: Information Computers and Technology (ICT) has a direct positive affect on Service Quality (SQ).
Customer Relationship Management (CRM)
According to Piccoli, Connor, Capaccioli, and Alvarez (2003), customer relationship management (CRM) is a managerial philosophy that calls for the use of Information Technology (IT) to capture, store, manipulate and distribute substantial information about customers, with CRM being one of the major sources of competitive advantage in the hotel sector (Al-Azzam, 2016). CRM is also the process of carefully managing detailed information about individual customers and all customer touch points to maximize customer loyalty (Kotler and Keller, 2012).
Business Strategy (Y4) – CRM is often thought of as a management tool but it can also be part of an organization’s overall strategy for interaction and management of their customers (Osarenkhoe and Bennani, 2007). For organizations wishing to become more ‘customer focused’, CRM is an often sought (Swartz and Iacobucci, 2000).
Customer Retention (Y5) – Thryambakam and Bethapudi (2013) indicated that in India, modern CRM strategies involve capturing customers heart, as compared to capturing their mind. This is stated to be done through offering a differentiating value preposition through various innovative ideas. The CRM process is a continuous learning process where information about individual customer is transformed into a customer relationship (Osarenkhoe and Bennani, 2007).
People Management (Y6) – Although CRM is technologically focused, relying on technology alone is a recipe for failure (Chen and Popovich, 2003), as it is the individual employees who are the building blocks of customer relationships.
From the above and other research, the researchers therefore developed the following hypotheses:
H5: Customer Relationship Management (CRM) has a direct positive affect on Service Quality (SQ).
H6: Customer Relationship Management (CRM) has a direct positive affect on capacity.
H8: Customer Relationship Management (CRM) has a direct positive affect on MICE industry organizational effectiveness (MICE).
Service Quality (SQ)
Tangibles (Y7) – Tangibles is the defined as the appearance of physical facilities, equipment, personnel and communication materials (Parasuraman, Zeithaml, and Berry, 1985, 1988, 1991). Jasinskas, Streimikiene, Svagzdiene, and Simanavicius (2016) discussed hotel service quality and customer loyalty and indicated that conformity of expected quality with the quality experienced has a significant influence on the customer’s loyalty, which in turn increases the hotel’s competitive ability by giving the hotel the ability to retain a higher number of loyal customers.
Empathy (Y8) – Assurance and empathy were two elements of the 5-dimension model referred to as ‘RATER’ (Grönroos, 1984, 1990; Parasuraman et al., 1985, 1988, 1991; Shahin and Nassibeh, 2011). Assurance was stated to be the ‘knowledge and courtesy of employees and their ability to inspire trust and confidence’, while empathy was identified as the ‘caring and individualized attention that an organization provides to its customer’.
Assurance (Y9) – In an analysis of the Taiwan MICE industry by the Ministry of Economic Affairs (MOEA), it was determined that ‘assurance’ and ‘empathy’ both played significant roles in explaining customer satisfaction. Furthermore, offering timely consideration and attention had a significant effect on the level of participants’ satisfaction (Chen, Chiou, Yeh, & Lai, 2012).
From the above and other research, the researchers therefore developed the following hypotheses:
H7: Service quality (SQ) has a direct positive affect on capacity management (capacity).
H9: Service quality (SQ) has a direct positive affect on MICE industry organizational effectiveness.
Capacity Management (capacity)
To grow healthfully and profitably, the MICE industry needs to evaluate its current capacity efficiency and carefully plan its future capacity (Yang & Gu, 2012). Capacity management (CM) research however is somewhat limited in the tourism industry as it is a topic often discussed in the manufacturing sector (Ayia-Koi & Sackle-Sackey, 2015), but if hoteliers adopt CM it can help ensure the survival and growth of their hotels through profitability in this competitive era.
Armistead and Clark (1994) indicated that capacity management is the ability to balance demand from customers, and the ability of the service delivery system to satisfy that demand. Ayia-Koi and Sackle-Sackey (2015) concluded that the most significant strategy used by hoteliers to manage capacity during low demands was discounting (42%).
Employee rewards (Y10) – Padilla-Meléndez and Garrido-Moreno (2014) indicated that employee motivation was crucial to a hotel’s organizational success.
Management Experience (Y11) – CM is also involved with the management of the limits of an organization’s resources, such as its labour force.
Motivation (Y12) – In research complied from a study of 128 Spanish hotels, it was suggested that although investment in IT was necessary, other factors for success included effective leadership and employee motivation (Padilla-Meléndez and Garrido-Moreno, 2014). Research has indicated that over 600,000 individuals each year leave the hospitality industry (Barbosa-McCoy, 2016) with staff turnover being an issue when planning capacity management.
Furthermore, Barbosa-McCoy (2016) concluded that hotel employee motivation plays a significant role in enhancing employee performance. Various techniques and rewards should be used so that employees can strengthen and improve their performances in a productive manner.
From the above and other research, the researchers therefore developed the following hypothesis:
H10: Capacity (capacity) has a direct positive affect on Thai MICE industry organizational effectiveness (MICE).
MICE (meetings, incentives, conferences, events) Industry Organizational Effectiveness
The MICE industry includes the local firms providing tourist accommodation, restaurants, activities, transport, etc. (Chauvière le Drian & Chaponnière, 2008). To support these activities, the researchers determined that there were three major components necessary to sustain MICE organizational effectiveness. The components identified included innovation (Y13), learning and growth (Y14), and organizational culture (Y15), which were included as the observed variables for the study.
Innovation (Y13) – Organizational effectiveness is defined as an external standard of how well an organization is meeting the demands of the various groups and organizations that are concerned with its activities which approximately is a construct for doing the right things or having validity of outcome (Borgström, 2005). Additionally, organizational effectiveness is achieved through customer orientation and innovation and is often used to describe an organizations’ performance.
Learning & Growth (Y14) – Kaplan and Norton in the development of the Balanced Scorecard (BSC) suggested four perspectives, one of which was learning and growth, which includes employee training and corporate cultural attitudes related to both individual and corporate self-improvement (Jusoh et al., 2008). In the current climate of rapid technological change, it is becoming necessary for knowledge workers to acquire 21st century skills and be in a continuous learning mode with contributes to a competitive advantage and thus, organizational effectiveness (Reeve, 2016).
Organizational culture (Y15) – Although the literature contains many different conceptualizations of culture, the majority agree that culture is a collective phenomenon, with individuals who belong to the same culture thinking and behaving similarly in key respects. Most researchers agree that organizational cultures have both ideational and observable aspects (Kopelman et al., 1990).
Methodology
Sample and Data Collection
The population from which the study’s sample was drawn belonged to the Thailand Convention & Exhibition Bureau (TCEB), which included the Professional Convention Organization (PCO) which is involved in various international conferences for professional associations. A second group was also identified, which included the International Professional Exhibition Organization (PEO), which serves by assisting organizers and vendors arrange meeting locations as well as booth rental for sellers to display products. Additionally, the Destination Management Company (DMC) was also part of the study’s total population. DMC is involved with providing travel arrangements for international awards for corporate clients and creating exotic and interesting tourism program. Finally, the Convention and Visitors Bureau (CVB) which represents tour agencies and tour operators was also contacted as well as other associated MICE industry companies.
Questionnaire research design was an outline that was used to generate answers to research problems/questions, which is an arrangement of conditions of data collection and analysis. For this study the researchers embraced a descriptive survey methodology designed to assess how managers viewed their organization’s effectiveness within the Thai MICE sector. The design employed the self-administration of questionnaires to a sample of individuals which was aimed at finding each individual’s attitudes and opinion about how the 21 observed variables impacted their operations. Primary and secondary data was obtained, with primary data coming from data collected from the questionnaire. Secondary data was gathered from the documents, literature, websites, research articles, research reports, and academic dissertations.
Measurement tools
Each questionnaire was divided into five parts. Part 1 consisted of the respondent’s general and industry participation information and consisted of 10 items. Part 2 included 60 items rated on a 5-level scale (5-very important to 1-not important) concerning the factors affecting the MICE industry supply chain. Part 3 had 26 items dealing with the MICE industry supply chain. Part 4 was concerned with MICE organizational effectiveness and had 25 items. Part five was focused on the individual’s opinions and suggestions as to how they would help build capacity management, capability, and competitiveness within the ASEAN’ community. The format of this section used an open-ended questionnaire. Therefore, from the five levels of frequency, the interpretation of these responses was calculated by using the formula:
Interval = 
A 0.8 (rounded) interval level for the 5 levels of frequency was used and is detailed in Table 2.
Table 2 Levels of frequency
| Mean range | Scale Responses |
| 04.21- 05.00 | 5 – Very important |
| 03.41- 04.20 | 4 – Somewhat important |
| 02.61- 03.40 | 3 – Important |
| 01.81- 02.60 | 2 – Less important |
| 01.00- 01.80 | 1 – Not important |
Research Instrument Quality Verification
Further feedback and recommendations led to use of the Index of Item-Objective Congruence (IOC) which was used to find the content validity. In this process, the questionnaire was checked by the following seven experts in their related fields:
- Deputy Managing Director – Thai tourism industry
- Assistant Professor – University Department of Tourism
- Senior Manager – TCEB
- Customer Service Director – Safari theme park
- Assistant Project Manager – Exhibition Organizer
- Senior Project Manager – Thai Public Company
- Project Manager – Tourism industry
The Item-Objective Congruence (IOC) was used to evaluate the items of the questionnaire based on the score range from -1 to +1. The items that had scores lower than 0.5 were revised or eliminated.
Reliability
From a ‘try-out’ assessment of 30 questionnaires, reliability of the questionnaire was determined to ensure that the responses collected through the instrument were reliable and consistent. The reliability value was calculated by using Cronbach’s alpha (Cronbach, 1951) to ensure internal consistency within the items. In general, a score of more than 0.7 is considered acceptable, although some authors suggest even higher values of 0.90-0.95 should be the norm (Tavakol and Dennick, 2011). From even the most stringent criteria of Cronbach’s Alpha, the study’s questionnaires were deemed to be highly reliable as the score was 0.94.
From literature reviews and theory, the following latent variables and observed variables were analyzed (Table 3).
From literature reviews and theory, the following latent variables and observed variables were analyzed (Table 3).
Table 3 Summary of latent variables and the observed variables along with associated theory related to Thailand’s MICE industry
| Latent Variables | Observed variables | Literature Review and Theory |
| Information Computer Technology (ICT) |
Internet (Y1)
Knowledge Management (Y2) Information Technology Management (Y3) |
(Dagooc, 2016; World Tourism Organization, 2014; Hallin & Marnburg, 2008; Kyriakidou & Gore, 2005; Kotler et al., 1999; Sheldon, 1997; Kahle, 2002) |
| Customer Relationship Management (CRM) |
Business Strategy (Y4)
Customer Retention (Y5) People Management (Y6) |
(Al-Azzam, 2016; Thryambakam & Bethapudi, 2013; Kotler & Keller, 2012; Osarenkhoe & Bennani, 2007; Piccoli et al., 2003; Chen & Popovich, 2003; Swartz & Iacobucci, 2000) |
| Service Quality (SQ) |
Tangibles (Y7)
Empathy (Y8) Assurance (Y9) |
(Jasinskas et al., 2016; Chen et al., 2012; Grönroos, 1984, 1990; Parasuraman et al., 1985, 1988, 1991; Shahin & Nassibeh, 2011) |
| Capacity management (capacity) |
Employee Rewards (Y10)
Management Experience (Y11) Motivation (Y12) |
(Barbosa-McCoy, 2016; Ayia-Koi & Sackle-Sackey, 2015; Padilla-Meléndez & Garrido-Moreno, 2014; Yang & Gu, 2012; Armistead & Clark, 1994) |
| Meetings, incentives, conferences, events (MICE) organizational effectiveness |
Innovation (Y13)
Learning & Growth (Y14) Organizational Culture (Y15) |
(Reeve, 2016; Jusoh et al., 2008; Chauvière le Drian & Chaponnière, 2008; Borgström, 2005; Kopelman et al., 1990) |
| Supply Chain Management (supplyCM) |
Speed (X1)
Cost (X2) Efficiency (X3) |
(Aiello et al. 2016; Thailand Convention & Exhibition Bureau, 2014; Zhang et al., 2009; Simchi-Levi et al., 2003; Li et al., 2006; Lee et al., 1997) |
| Supplier Relationship Management (supplyRM) |
Agility (X4)
Supplier Relationships (X5) Performance Evaluation (X6) |
(Fan & Zhang, 2016; Rizza, 2015’ Hong & Zailani, 2011; Agarwal et al., 2007) |
Results
Respondent’s Characteristics
Table 4 shows the Thai MICE industry respondent’s characteristics from the 2015/2016 survey. An analysis of the participant’s answers shows the Thai MICE industry as having young managers (66% less than 36) which have less than a decade of experience (64%). By far, males dominate the sector at 73% with everyone surveyed indicating they have at least an undergraduate degree.
Table 4 Thai MICE industry respondent’s characteristics
| Characteristics | Number | %age | Rank |
| 1. Gender | |||
| male | 366 | 73.20 | 1 |
| female | 134 | 26.80 | 2 |
| Total | 500 | 100 | – |
| 2. Age | |||
| Less than 30 years old. | 182 | 36.40 | 1 |
| Between 30-35. | 149 | 29.80 | 2 |
| Between 36-40. | 70 | 14.00 | 3 |
| Between 41-45. | 57 | 11.40 | 4 |
| Over 45 years old. | 42 | 8.40 | 5 |
| Total | 500 | 100 | – |
| 3. Education level | |||
| – Bachelor Degree | 295 | 59.00 | 1 |
| – Graduate Degree | 205 | 41.00 | 2 |
| Total | 500 | 100 | – |
| 4. Monthly income (The January 2017 exchange rate was 10,000 Thai baht equalled $US283.00) | |||
| Less than 40,000 baht per month. | 299 | 59.80 | 1 |
| Between 40,001-50,000 baht per month. | 42 | 8.40 | 3 |
| Between 50,001-60,000 baht per month. | 37 | 7.40 | 4 |
| Between 60,001-70,000 baht per month. | 36 | 7.20 | 5 |
| Over 70,000 baht per month. | 86 | 17.20 | 2 |
| Total | 500 | 100 | – |
| 5. MICE industry experience | |||
| Less than 10 years. | 321 | 64.20 | 1 |
| Over 10 years. | 179 | 35.80 | 2 |
| Total | 500 | 100 | – |
Data Analysis
Table 5 shows that the factors that affect Thai mobile phone customer loyalty includes customer satisfaction, perceived service quality, customer trust, corporate image, perceived value and perceived switching costs. Six of the seven variable had levels which indicated sentiment as ‘very important’, with an average of 04.21- 05.00 on the survey’s 5-point Likert scale (Best & Kahn, 1998; Likert, 1932). ICT was judged to be of the greatest importance with a mean score of 4.45 (Table 5).
Table 5 Mean, standard deviation, and the order in which the opinions of the MICE industry on the variables involved.
| Level | Rank | |||
| ICT | 4.45 | 0.44 | Very important | 1 |
| CRM | 3.94 | 0.56 | Somewhat important | 7 |
| SQ | 4.22 | 0.55 | Very important | 6 |
| capacity | 4.29 | 0.48 | Very important | 4 |
| MICE | 4.34 | 0.51 | Very important | 3 |
| supplyCM | 4.25 | 0.50 | Very important | 5 |
| supplyRM | 4.44 | 0.52 | Very important | 2 |
Confirmatory Factor Analysis
A confirmatory factor analysis (CFA) with LISREL (LInear Structural RELationships) 9.10 was used to analyze the latent variables by use of a structural equation model of the relationship factors concerning the Thai MICE industry organizational effectiveness (Magistris and Gracia, 2008). Figure 4 and Figure 5 shows the model’s path analysis after adjusting the model to determine the consistency of models with the empirical data which was found to be consistent through the assessment model. The data criteria are shown in Table 5.
Wong (2013) has also stated that for marketing research, a significance level of 5%, a statistical power of 80%, and R2 values of at least 0.25 are viewed as normal.
Information Computer Technology (ICT) – Using SEM, the researchers specified the CFA model where the ICT (Figure 3) is influenced by the Internet (Y1), Knowledge Management (Y2), and Information Technology Management (Y3). From the modeling, the 2 was indicated to be 0.00, with a p value of 1.000 and RMSEA (root mean square error of approximation) of 0.000 which indicates an excellent fit with the model. This ensures that the observed variables are sensitive to ICT and are suitable for further analysis.
Customer Relationship Management (CRM) – Using SEM, the researchers specified the CFA model where the CRM (Figure 3) is influenced by Business Strategy (Y4), Customer Retention (Y5), and People Management (Y6). From the modeling, the 2 was indicated to be 0.00, with a p value of 1.000 and RMSEA of 0.000 which indicates an excellent fit with the model. This ensures that the observed variables are sensitive to CRM and are suitable for further analysis.
Service Quality (SQ) – Using SEM, the researchers specified the CFA model where the SQ (Figure 3) is influenced by Tangibles (Y7), Empathy (Y8), and Assurance (Y9). From the modelling, the 2 was indicated to be 0.00, with a p value of 1.000 and RMSEA of 0.000 which indicates an excellent fit with the model. This ensures that the observed variables are sensitive to SQ and are suitable for further analysis.
Capacity Management (CM) – Using SEM, the researchers specified the CFA model where the SQ (Figure 3) is influenced by Employee Rewards (Y10), Management Experience (Y11), and Motivation (Y12). From the modelling, the 2 was indicated to be 0.00, with a p value of 1.000 and RMSEA of 0.000 which indicates an excellent fit with the model. This ensures that the observed variables are sensitive to CM and are suitable for further analysis.
MICE Industry Organizational Effectiveness (MICE) – Using SEM, the researchers specified the CFA model where the SQ (Figure 3) is influenced by Innovation (Y13), Learning & Growth (Y14), and Organizational Culture (Y15). From the modelling, the 2 was indicated to be 0.00, with a p value of 1.000 and RMSEA of 0.000 which indicates an excellent fit with the model. This ensures that the observed variables are sensitive to MICE and are suitable for further analysis.
Supply Chain Management (supplyCM) – Using SEM, the researchers specified the CFA model where the SQ (Figure 4) is influenced by Speed (X1), Cost (X2), and Efficiency (X3). From the modelling, the 2 was indicated to be 0.00, with a p value of 1.000 and RMSEA of 0.000 which indicates an excellent fit with the model. This ensures that the observed variables are sensitive to supplyCM and are suitable for further analysis.
Supplier Relationship Management (supplyRM) – Using SEM, the researchers specified the CFA model where the SQ (Figure 4) is influenced by Agility (X4), Supplier Relationships (X5), and Performance Evaluation (X6). From the modelling, the 2 was indicated to be 0.00, with a p value of 1.000 and RMSEA of 0.000 which indicates an excellent fit with the model. This ensures that the observed variables are sensitive to supplyCM and are suitable for further analysis.
Figure 3 CFA of ICT, CRM, SQ, capacity, and MICE
Chi-Square=25.62, df=37, p-value =0.92079, RMSEA=0.000
Chi-Square=1.32, df=5, p-value =0.93257, RMSEA=0.000
Analysis of LISREL 9.10 was also used to prove the validation of the model, while the Goodness of Fit Measurement was used to measure the level of harmony of functions (Table 6). Results from the variables in the model using path analysis of the latent variables are shown in Table 6, Table 7, and Figure 10. Validity is measured by use of convergent, discriminant and construct validity. To evaluate model fit, RMSEA is used which is an informative criterion (Steiger, 2007). Values less than 0.05 indicate good fit, values up to 0.08 reasonable fit and ones between 0.08 and 0.10 indicate mediocre fit. An AVE value of 0.50 and higher indicates a sufficient degree of convergent validity, meaning that the latent variable (constructs) explains more than half of its indicators variances (Fornell & Larcker, 1981).
Table 6 Criteria, theory, and results of the values of goodness-of-fit
| Index | Acceptance Level |
Results | Theory & Literature |
| Chi-square (X2) | p >0.05 | passed | Jöreskog and Sörbom (1993) |
| Relative X2 χ2/df | ≤ 2.00 | passed | Byrne et al. (1989) |
| RMSEA (root mean square error of approximation) |
≤ 0.05 | passed |
Hoe (2008)
Hu and Bentler (1999) Steiger (2007) |
| GFI (Goodness of Fit Index) |
≥ 0.90 | 0.99 – passed | Jöreskog and Sörbom (1993) |
| AGFI (adjusted goodness of fit) |
≥ 0.90 | 0.98 – passed | Kenny (2015) Hooper et al., (2008) |
| CFI (comparative fit Index) |
≥ 0.90 | passed | Hu and Bentler (1999) |
|
SRMR
(standardized root mean square residual) |
≤ 0.08 | 0.01 passed |
Hu and Bentler (1999)
Kenny (2015) |
| Convergent Validity (AVE) average variance extracted |
AVE > 0.50 | passed | Fornell and Larcker (1981) |
| Cronbach’s Alpha | ≥ 0.70 | 0.94 – passed | Cronbach (1951), George and Mallery (2010), Tavakol and Dennick (2011) |
Following Chin (1998), standardized path coefficients should be at least 0.20 and ideally above 0.30 in order to be considered meaningful (Table 7).
Table 7 The correlation coefficient between latent variables (below the diagonal), reliability of latent variables (C) and the average variance extracted (AVE).
| Latent Variables | ICT | CRM | SQ | capacity | MICE | supplyCM | supplyRM |
| ICT | 1.00 | ||||||
| CRM | 0.50 | 1.00 | |||||
| SQ | 0.51 | 0.73 | 1.00 | ||||
| Capacity | 0.45 | 0.83 | 0.73 | 1.00 | |||
| MICE | 0.40 | 0.63 | 0.74 | 0.68 | 1.00 | ||
| supplyCM | 0.72 | 0.36 | 0.37 | 0.33 | 0.29 | 1.00 | |
| supplyRM | 0.74 | 0.37 | 0.38 | 0.33 | 0.30 | 0.30 | 1.00 |
| C (Construct Reliability) | 0.90 | 0.83 | 0.92 | 0.86 | 0.91 | 0.82 | 0.85 |
| V (AVE) | 0.74 | 0.62 | 0.80 | 0.67 | 0.76 | 0.61 | 0.71 |
| 0.86 | 0.79 | 0.89 | 0.82 | 0.87 | 0.78 | 0.84 |
SEM Results
SEM has become a quasi-standard in marketing and management research when it comes to analysing the cause-effect relations between latent constructs (Hair, Ringle, & Sarstedt, 2011). Thus, this study used SEM to investigate the relationships between the degree of MICE organizational effectiveness and the other variables by use of LISREL 9.10 software. The SEM results (Figure 10, Tables 8 – 9) from the Thai MICE industry survey showed that the overall reliability (α) was 0.96, with structural reliability (C) between 0.62 to 0.83 of the analyzed variables. Another advantage of SEM analysis is that the SEM can simultaneously assess relationships between each independent variable and the dependent measure (Jöreskog & Sörbom, 1993). After several model adjustments, the results revealed the model was in harmony with the empirical data (Figure 10).
Figure 10 Final model of variables affecting the Thai MICE industry organizational effectiveness
Note. Chi-Square=51.69, df=64, p-value=0.86573, RMSEA=0.000
Table 8 shows the direct effect, indirect effect, and total effect of each construct with the sum of direct and indirect effects being referred to as the total effect (Zou & Fu, 2011; Bollen, 1987). The “p” value is the ‘level of significance’ with a p <0.05 indicating that the probability that the result is observed due to chance is 5% (a “false positive” result). Conventionally, the p value of 5 % (p = 0.05) or 1 % (p = 0.01), which means 5 % (or 1 %) chance of erroneously reporting a significant effect is accepted.
Table 8 Coefficient of influence in relation to the administration of the MICE industry
|
Independents
variables |
Effect | Dependents variables | ||||
| ICT | CRM | SQ | capacity | MICE | ||
| R2 | – | 0.82 | 0.25 | 0.56 | 0.72 | 0.59 |
| supplyCM | DE | 0.55** | – | – | – | – |
| IE | – | 0.27** | 0.28** | 0.25** | 0.22** | |
| TE | 0.55* | 0.27** | 0.28** | 0.25** | 0.22** | |
| supplyRM | DE | 0.57** | – | – | – | – |
| IE | – | 0.29** | 0.29** | 0.26** | 0.23** | |
| TE | 0.57** | 0.29** | 0.29** | 0.26** | 0.23** | |
| ICT | DE | – | 0.50** | 0.20** | – | – |
| IE | – | – | 0.31** | 0.45** | 0.40** | |
| TE | – | 0.50** | 0.51** | 0.45** | 0.40** | |
| CRM | DE | – | 0.63** | 0.63** | 0.01 | |
| IE | – | – | 0.17** | 0.56** | ||
| TE | – | 0.63** | 0.80** | 0.57** | ||
| SQ | DE | – | 0.27** | 0.52** | ||
| IE | – | – | 0.08* | |||
| TE | – | 0.27** | 0.60** | |||
| capacity | DE | – | 0.29** | |||
| IE | – | – | ||||
| TE | – | 0.29** | ||||
**Sig. < .01, *Sig. < .05
Further information for the hypotheses support can be seen in Table 9 and the final model in Figure 10, which was found to be consistent with empirical data. The harmonized index of all the criteria was: p = 0.98, GFI = 0.99, AGFI = 0.98 and the SRMR = 0.01.
Table 9 Hypotheses test results.
| Hypotheses | Coef. | t-test | Results |
| H1: Information Computers and Technology (ICT) has a direct positive affect on Supply Chain Management (supplyCM). | 0.55 | 10.81** | supported |
| H2: Information Computers and Technology (ICT) has a direct positive affect on Supplier Relationship Management (supplyRM). | 0.57 | 11.15** | supported |
| H3: Information Computers and Technology (ICT) has a direct positive affect on Customer Relationship Management (CRM) | 0.50 | 10.66** | supported |
| H4: Information Computers and Technology (ICT) has a direct positive affect on Service Quality (SQ). | 0.20 | 5.16** | supported |
| H5: Customer Relationship Management (CRM) has a direct positive affect on Service Quality (SQ). | 0.63 | 13.54** | supported |
| H6: Customer Relationship Management (CRM) has a direct positive affect on capacity. | 0.63 | 8.49** | supported |
| H7: Service Quality (SQ) has a direct positive affect on Capacity Management (capacity). | 0.27 | 4.30** | supported |
| H8: Customer Relationship Management (CRM) has a direct positive affect on MICE industry organizational effectiveness (MICE). | 0.01 | 0.09 | not supported |
| H9: Service quality (SQ) has a direct positive affect on MICE industry organizational effectiveness (MICE). | 0.52 | 8.85** | supported |
| H10: Capacity has a direct positive affect on Thai MICE industry organizational effectiveness (MICE). | 0.29 | 3.00** | supported |
**Sig. < 0.01
Discussion
Tourism represents one of the main sources of income for many developing countries, which must be integrated into both national and international development strategies (Rios-Morales, Gamberger, Boskovic, & Jenkins, 2013). This is supported by a 2016 World Tourism Organisation (UNWTO) report, in which it was reported that of the world’s 1.237 billion international tourists, 46% were from emerging economies (World Travel & Tourism Council, 2016). It was also stated that 284 million jobs, or 1 in 11 jobs in the global economy were supporting this industry.
Tourism/MICE ICT, or ‘eTourism’ (Buhalis, 2003) revolutionizes all business processes, including the entire value chain as well as stakeholder strategic relationships with tourism organizations and all their stakeholders. ICT as employed in ‘eTourism’ is used throughout many related sectors including airlines, hotels, travel agencies, tour operators and destinations management organizations. ICT as implemented in eTourism increasingly determines the competitiveness of the organization, and therefore, it is critical for the competitiveness of the industry in the longer term.
Within the tourism industry, 23.4 % is related to business, with the MICE sector accounting for 54 % of the total business travel market (CBI, 2014). From the research and survey, efficiency and reliability seem to be two key themes (Dahan, 2016). Knowledge management has also been confirmed to be especially relevant for building up a competitive advantage (Hallin & Marnburg, 2008; Buhalis, 2003) and to have effective empowerment, management must adopt and practice involvement in which power, knowledge, information, and rewards are distributed to employees at the lower levels of the organizational hierarchy (Sulistyo, 2016).
For MICE attendees, fast and reliable Internet connectivity has become a pre-condition for venue selection (World Tourism Organization, 2014). Digitalization is changing business models, customer relationships and perceptions together with markets and competitive landscapes (KPMG, 2014; Ernst & Young, 2015).
KPMG (2014) has also identified nine global megatrends and has stated that they are all highly interrelated. Part of this reason comes from ICT and the fact that over 75 % of the world’s population has access to mobile phone technology and 50 % have access to the Internet. Knowledge management and information computer technology are thus transforming societies, with some developing nations ‘leap-frogging’ technologically over older industrial nations.
Additionally, tourism along with the MICE sector has become a main source of income for many developing nations (Rios-Morales et al., 2013). However, staff turnover is high (Barbosa-McCoy, 2016) and more organizations are requiring entrants to hold formal academic qualifications. This appears to be the case in Thailand, as 100% of the surveyed participants indicated they held at least a bachelor’s degree.
Conclusion
The relationship of the model variables developed for MICE organizational effectiveness was done with precision, with the model being consistent with the empirical data. The harmonized index of all the criteria, including the Chi – Square is not statistically significant, with the values for the RMSEA = 0.00, p = 0.86, GFI = 0.99, AGFI = 0.96, and the SRMR = 0.02.
From the model’s variables, it was concluded that three variables had a direct and positive influence on the organizational effectiveness of the MICE industry. This could be explained by the variability of MICE organizational effectiveness (R2) being 59 percent. The causal variables that influenced MICE industry organizational effectiveness directly included SQ, CRM, and capacity, with the influence of 0.52, 0.01, and 0.29 respectively.
The variables having an indirect influence included supplyCM, supplyRM, ICT, CRM and SQ, influenced by the value equal to 0.22, 0.23, 0.40, 0.56, and 0.08 respectively.
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