The evidence for the benefits of happiness

It is widely argued that uncertain factors generate unstable processes along supply chains, which in the end worsen their performance. This research assesses reliability and validity seven uncertain factors (supply, demand, process, planning and control, competitor behavior; government policy and climate uncertainty). Data for the study were collected from rice millers and rice exporters, and the measurement scales were tested on reliability and validity using Partial Least Squares (PLS) and Multicollinearity as there are the formative measurement models. The results support that the formative measures of uncertain factors are reliable and valid. It is expected that this study will provide a useful measurement instrument to assess any effects in agri-supply chains for further research. Measurement of uncertain factors in Thai rice supply chain According to the previous studies, uncertain factors in organisations were considered in general and task environment. The general environment refers to political, social, economic, demographic, and technological trend. Meanwhile, the task environment is composed of competitors, suppliers, customers and regulation bodies (Bourgeois, 1980). However, the review of literature analysis indicates that there are seven uncertain factors (supply, demand, process, planning and control, competitor, government policy, and climate) influencing Thai rice supply chain. Additional, each factor can be measured in many dimensions such as quantity, quality and time dimensions. Thus, multi-dimensional measurement are applied in order to increase reliability, and decrease measurement error (Churchill, 1979). There are three perspectives of environment in organization which are (i) objects, (ii) attributes, and (iii) perceptions (Duncan, 1972, Bourgeois, 1980). To measure uncertain factors in organization, the perceptions of them are considered in this study because managers make decisions on their perceived factors leading that the perceptions of these perceived uncertain factors are more crucial than the objective uncertain factors (Duncan, 1972, Bourgeois, 1980). In addition, two attributes of uncertain factors are (i) degree of change or unpredictability, and (ii) complexity or diversity of environmental factors (Duncan, 1972, Downey et al., 1975). In this study, unpredictability of factors is focused. The reason is that the degree of unpredictable factors reflects more to variability of perceived uncertainty than complexity (Duncan, 1972, Dill, 1958). Moreover, unpredictable factors create more risk and difficulty for managers’ decision making and affect to effective strategy making (Bourgeois, 1978). Consequently, the summary of the characteristics of measured uncertain factors in this study is shown in Table 1. Table 1: The summary of the characteristics of measured uncertain factors Characteristics of uncertain factors Measurement in this study Perspectives Perception Attitude Degree of change or unpredictability Phatcharee T. Thongrattana 2 There are three aspects (quality, quantity and time) of supply, demand, process, and planning and control uncertainty to be measured, and these are defined in Table 2 (adapted from van der Vorst 2000). Table 2 Typology of sources of Rice supply chain uncertainty and the aspects they concern (adopt from van der Vorst, 2000, p.76 ) Quantity aspects Quality aspects Time aspects Supply Inbound (paddy or milled) rice quantities Inbound (paddy or milled) rice quality Inbound (paddy or milled) rice timing to millers Demand Customer demand of outbound rice quantities Customer demand of outbound rice specifications Timing of customer order Process Mill yield, packing yield Milled rice quality, Milled rice quality after storage Process throughput times Planning and control Information availability Information accuracy Information throughput times Competitor behavior uncertainty is measured under three aspects: their actions, competition in domestic and in international markets, a minor modification of Li’s 2002 study. The government policy uncertainty measurement have four aspects: policy affecting rice production, trading, paddy rice mortgage scheme, and any new government regulations (Badri et al., 2000, Javidan, 1984, Bran and Bos, 2005). Finally, climate uncertainty related to rice production is monitored in three aspects: drought, flooding (both in terms of occurrences and duration), and warmer temperatures (Cruz et al., 2007). Each drought and flood event is characterized by its duration, deficit volume (severity) and time of occurrence, and its occurrence depends on the ratio between water demand and water availability (Tallaksen and Hisdal, 1997, p.142). However, as this measurement instrument is perception, the specific time of occurrence is considered to avoid in the questionnaire because it is too more detail to be recognised by respondents. Thus, to assure that the question delivery a common understanding to respondents, Drought and Flooding occurrences are the simple words to be used. The final list of measurement items ia shown in Table 3. Formative and Reflective Construct Specification Two basic types of measurement model are reflective and formative indicators (Hulland, 1995). Measurement specification that is deciding whether to use reflective or formative indicator construct is very essential (Bollen and Lennox, 1991). Misspecification of formative and reflective construct indicators can lead to underestimate in theoretical framework testing (Diamantopoulos and Siguaw, 2006, MacKenzie et al., 2005). In addition, these two different measurement models are in conceptual distinctions (MacKenzie et al., 2005) required different statistical analysis procedures such as validity test, reliability test and structural model test (Petter et al., 2007). Moreover, the measurement model misspecification will not be detected with many the most commonly used goodness-of-fit indices (MacKenzie et al., 2005, p.728). Thus, the summary of differences between formative and reflective measurement model and decision rules for determining whether a construct is formative or reflective is explained in Table 4. Phatcharee T. Thongrattana 3 Table 3: The final list of measurement items in Thai rice supply chain. Uncertain factors Aspects of measurement The question in the questionnaire References Quantity SU1: Rice quantity from rice producers is unpredictable Quality SU2: Rice quality from rice producers is unpredictable Supply Time SU3: Rice producers’ delivery time is unpredictable (van der Vorst, 2000, Li, 2002, Paulraj and Chen, 2007) Quantity DU1: The volume of customer demand is difficult to predict Quality DU2: Customers’ rice preference changes over the year Demand Time DU3: The lead time 1 of customer order is unpredictable (Li, 2002, Paulraj and Chen, 2007) Quantity PU1: Yield of rice processing (e.g. milling, packing) can vary Quality PU2: The quality of rice after processed (e.g. milled, storied ) can be changed Process Time PU3: The throughput time of rice processing can vary (van der Vorst, 2000) Quantity PCU1: Information of stock level of rice and rice production capacity is complete at this moment Quality PCU2: Information of stock level of rice and rice production capacity is accurate Planning and Control Time PCU3: Information of stock level of rice and rice production capacity is timely (van der Vorst, 2000) Actions CU1: Competitor’s actions are unpredictable Domestic market CU2: Competition is intensified in domestic market. Competitor International market CU3: Competition is intensified from different countries (Li, 2002) Rice production GU1: Government policies in rice production directly impacting on your firms are unpredictable Rice trading GU2: Government policies in rice trading (e.g. FTA, tax) directly impacting on your firms are unpredictable Paddy rice mortgage scheme GU3: The guarantee price from government regulation is unpredictable over the year Government policy New government GU4: The new government regulation is introduced unexpectedly (Badri et al., 2000, Javidan, 1984, Bran and Bos, 2005) Drought CMU1: Drought occurrences are unpredictable in each year CMU2: The duration of drought is unpredictable over the year Flooding CMU3: Flooding occurrences are unpredictable in each year CMU4: The duration of flooding is unpredictable over the year Climate Temperature CMU5: The temperature is vary unpredictably over the year (Cruz et al., 2007) Phatcharee T. Thongrattana 4 Table 4: Summary of differences between formative and reflective measurement model (Jarvis et al., 2003, p.201) Composite Latent Variable (Formative) Model Principle Factor (Reflective) Model Direction of causality is from measure to construct Direction of causality is from construct to measure No reason to expect the measures are correlated (Internal consistency is not implied) Measures expected to be correlation (Measures should posses internal consistency reliability) Dropping an indicator from the measurement model may alter the meaning of the construct Dropping an indicator from the measurement model does not alter the meaning of the construct Takes measurement error into account at the construct level Takes measurement error into account at the item level Construct possesses “surplus” meaning Construct possesses “surplus” meaning Scale score does not adequately represent the construct. Scale score does not adequately represent the construct. In this study, supply, demand, process, planning & control, competitor, and government policy uncertainty is composite in different dimensions such as quantity, quality and time dimensions that differs in monological net. Thus, they are the formative measurement model. Climate uncertainty is composite in drought, floods and temperature dimensions that do totally not share a common theme, while drought and floods dimension are also measured in formative model in terms of duration and frequent aspects. Thus, climate uncerta