Developing and validating a hierarchical model of

Inf Syst Front DOI 10.1007/s10796-014-9503-8

Developing and validating a hierarchical model of external responsiveness: A study on RFID technology Mohammad Alamgir Hossain & Mohammed Quaddus

# Springer Science+Business Media New York 2014

Abstract Information Systems researchers demonstrate that organizations are very often influenced by external environment; success of an organization and its associated industry is largely dependent on how they respond to the external factors. Although a number of external factors have been explored in literature, still little is known on their degree of impact and hence their relative importance. Therefore, advancing research on organizational external (environmental) responsiveness requires clarifying the theoretical conceptualizations and validating the associated dimensions. After conducting an extensive literature search followed by a qualitative and quantitative study, the current study develops and validates a multi-dimensional hierarchical model of external responsiveness and investigates its effect on adoption intention. The findings of the study show that; in the context of Radio Frequency Identification (RFID) technology, external responsiveness is a third-order, reflective construct which is reflected by external pressure (further is reflected by government pressure, market pressure, mimetic pressure, and normative pressure), external support (reflected by government support, vendor support, and associative support), and external uncertainty (reflected by market and technology uncertainty). Moreover, the impact of the third-order and second-order constructs on the endogenous variable (i.e. intention to adopt RFID) is examined and found to have positive influences. This study is the first reported attempt that categorizes the dimensions of external responsiveness and validates with M. A. Hossain (*) School of Business, North South University, Bashundhara, Dhaka 1229, Bangladesh e-mail: [email protected] M. Quaddus School of Marketing, Curtin Business School, Curtin University of Technology, Kent street, Bentley, WA 6102, Australia e-mail: [email protected]

empirical data. This study concludes with implications and future research directions. Keywords External environment . Responsiveness . RFID . Higher-order hierarchical model

1 Introduction Information System (IS) scholars advocate that there is a reciprocal relationship between external environment and organizations; organizations should view the environment through the lens of IS (Laudon and Laudon 2012). More frequently, organizational decision toward adopting an IS/IT is directly or indirectly influenced by the external environment. Numerous empirical studies established that external environment plays a significant role influencing an industry and its organizations to adopt a specific IT/IS (e.g. Li and Visich 2006). For instance, though has been invented during World War II, the revolution of Radio Frequency Identification (RFID) technology has not been started before it was mandated by several industry key players; we mention a couple here. In November 2003, Wal*Mart mandated its top hundred suppliers to attach RFID tags at case/palette level by January 1, 2005 (Bansal 2003; Roberti 2003; Jones et al. 2005) while the remaining suppliers should follow within 1 year (Wu et al. 2006). Around the same time Tesco, Target, and Metro AG mandated their selected suppliers to be RFIDenabled (Poirier and McCollum 2006). Similarly, the Department of Defense (DoD) of United States, that maintains the position of world’s largest cargo tracking system across 2,000 sites in 46 countries (Whitaker et al. 2007), mandated its more than 43,000 suppliers (Bacheldor 2003) to attach RFID tags on cases and pallets by January 2005 (Jones et al. 2005). In food and drug business, Albertsons and the U.S. Food and Drug Administration (FDA) launched their RFID program

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and recommended their suppliers to use RFID tags on products (Garry 2004; Poirier and McCollum 2006). In livestock sector, the demand for a precise method of meat traceability was first introduced by European Union (EU) in late 1990, which was then followed by Japan, South Korea, and United States. In the latest case (livestock), the pressure is exercised at the industry level whereas earlier cases targeted the associated organizations. However, as not every farm has “the same enthusiasm and urgency for incorporating RFID technology” (Nolz 2008), the industry-players (e.g. government agencies, livestock associations) could not ignore the external pressure but diffused it among the organizations (i.e. livestock producers). Anecdotal evidences suggest that organizations differ significantly in terms of exposed environment (Zhu et al. 2006a, 2006b; Sun and Qu 2014) – which actually influences the readiness or preparedness and responsiveness of the organizations to the environment, that ultimately drive adoption (Husted et al. 2000; Parasuraman 2000). In other words, it is the responsiveness of the adopting-organizations that drives the adoption where environmental factors work as the drivers to responsiveness. Depending on the strength of the external variables and the exposure to the environment, organizations differ in external responsiveness i.e. how organizations respond to the external environment – more responsiveness results more intention to adopt the IS/IT (Parasuraman 2000). That means, in spite of the presence of strong environmental factors, if organizations do not respond accordingly, adoption will not take place. Although prior studies used a number of external factors discretely, to the best of the authors’ knowledge, there is a paucity of research in IS which demands a systematic approach in compiling the variables of external responsiveness and examining their associated relationship to adoption. Researchers and practitioners always recognize that it is critical to define and operationalize such concepts and variables (Akter et al. 2013; Sethi and King 1991). Measure of external responsiveness is also required in order to guide future empirical research that can use the variables and items accordingly. The primary objective of the current study is, therefore, to develop and measure the determinants of external responsiveness. Going further, Sethi and King (1991) emphasized the investigation of the impact of the measured construct to an antecedent or consequent (p. 463); for instance, Akter et al. (2013) and Hossain et al. (2014) developed third-order hierarchical models explaining service quality of m-health and retail banks, respectively, and then investigated its effect on customer satisfaction and continuance intention (Hossain and Dwivedi 2014). Hence, this study investigates the impact of external responsiveness on adoption intention – one of the most frequently researched endogenous variables in IS studies. The current study investigates intention to adopt, not the actual adoption on the following ground. Scholars actually proposed research agenda examining the antecedents of both

‘intention’ as well as the ‘use’ of RFID technology which would inspire future adopters toward adoption of this technology (Curtin et al. 2007; Ngai et al. 2008). But, ‘use’ actually refers to the ‘degree and manner’ of a technology in use (Dwivedi et al. 2013); in the industry (i.e. livestock) we investigated the developed model, RFID is used mostly for identification purpose, the degree of use is still evolving (Trevarthen and Michael 2007) shifting from infancy stage. Therefore, many organizations are examining the feasibility and profitability of this technology and yet to adopt it. Moreover, Parasuraman (2000) postulated that more responsiveness results more intention to adopt an IS/IT. Therefore, in the current context, intention is a better representative than the actual use. Measures of external responsiveness are important and necessary for prioritizing the external factor(s) during adopting and diffusing an IS/IT (Sethi and King 1991), whereas simultaneous ‘impact assessment’ is essential to understand the relative impact. In order to develop the measures of external responsiveness, this study specifies it as a hierarchical model. Variables and dimensions are explored and examined from an extensive literature-search which is further contextualized by a qualitative study. Finally, the variables are examined with empirical data. For empirical data analysis, Partial Least Square (PLS)-based Structural Equation Modeling (SEM) technique is used. SEM has been gaining immense interest among IS researchers especially because it is “particularly useful in IS research, where many, if not most, of the key concepts are not directly observed” (Roldán and SánchezFranco 2012, p.194). The rest of the paper is organized as follows: the next section discusses the theoretical background. Then, we conceptualize the research model and propose our hypotheses. The subsequent section discusses the empirical findings. Finally, the study discusses the implications of the research in terms of theoretical and practical contributions, and provides the concluding remarks with limitations and future research directions.

2 Background This section starts with a brief introduction on RFID technology followed by specifying the research domain of the current study. 2.1 RFID: A general overview RFID is a generic term which implies a combined architecture of RFID hardware and IS. The hardware component consists of RFID tags (transponders), and RFID readers (transceiver/ interrogators); the IS component includes a network system with predefined protocol for the information to be transferred (middleware) (Banks et al. 2007). The tag is attached to an item and the readers can be attached to a panel or can be mobile (handheld). RFID identifies an object uniquely and

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transmits the identity and other relevant information wirelessly - using radio waves (Bacheldor 2003; Violino 2005), and share the data in a networked environment; therefore, it is considered as a huge enabler of Internet of things (Whitmore et al. 2014). As RFID uses radio signal it identifies the object from distance and does not require ‘line of sight’ and manual interventions (Yan et al. 2008). Furthermore, RFID can store enormous amount of data for many years which can automatically be retrieved as information as required (Ngai et al. 2009). RFID is considered as one of the most enabling innovations (Curtin et al. 2007) which possesses all three levels of complexity associated with a technological- innovation (Marquis 1969): (1) a very complex system which involves many years to implement and requires organizational and industrial resources, (2) major breakthroughs in technology which alter the character of a whole industry, and (3) new product, process, or product improvements carried out within a firm. Therefore, in this study, we considered that RFID represents technological innovations (Curtin et al. 2007). 2.2 Specifying the domain Organizations adopt and use an innovation for organizational achievements and, sometimes, as response to the change in external environment (Cooper and Zmud 1990; Zmud 1984; Parasuraman 2000). Scott (2001) indicates that, in order to survive, organizations must conform to the rules and demands prevailing in the environment. Similarly, Social Cognitive Theory (SCT) argues that organizational factors of innovation are necessary but not sufficient; organizations require interaction with and abide by the rules of the environment (Bandura 2001). Similarly, Institutional Theory assumes that organizations cannot operate in isolation rather they need to respect and follow the trends and demands of the external environment where they operate. It also illustrates that organizations not always take decision from internal judgments but have to respond to the external environmental issues as well (Teo et al. 2003; Lai et al. 2006). Moreover, Technology-OrganizationEnvironment (TOE) framework argues that decision to adopt a technological innovation is based on technological, organizational, and environmental factors where environmental factors are of significant importance (Tornatzky and Fleischer 1990). One common theme that is shared by the above-mentioned theories is acknowledging the important influence of environment to organizational decisions. In general, environment is defined as the “totality of physical and social factors that are taken directly into consideration in the decision-making behaviour” of the organizations (Duncan 1972, p. 314). Institutional theories define environment as an integrated set of political, economic, social, and legal conventions that establish the foundation of a productive business environment (Lai et al. 2006; Oxley 1999). In literature, environment is synonymously and generally used as external environment

because internal environmental factors are exerted by and within an organization and therefore are added with the organizational factors. Hence, to be precise, external environment consists of those relevant factors outside the boundaries of the organization (‘global’ factors) which are beyond organization’s control but are important in functioning and decisionmaking behaviour (Quaddus and Hofmeyer 2007). IS researchers are divided identifying the environmental factors. Jeyaraj et al.’s (2006) meta-analysis explored 16 unique independent variables of environment from 16 studies with 23 relations. Under the environment construct, Gibbs and Kraemer (2004) examined external pressure, government promotion, and legislation barriers while Wang et al. (2010) examined competitive pressure, trading partner pressure, and information intensity. Similarly Zhu et al. (2003) examined consumer readiness, competitive pressure, and lack of trading partner readiness; Scupola (2003) examined competitive pressure, government, and infrastructure, while Chau and Tam (1997) examined only market uncertainty as the external environment. Although there is no consensus on these factors, they describe how external factors make organizations to respond to those factors - which we call external responsiveness. External responsiveness can be defined as the degree to which changes in a measure of an organization (e.g., adoption) over a specified timeframe relate to corresponding changes in a reference measure of external environment (e.g. market demand) (Husted et al. 2000). In this definition, the measure itself is not of the primary interest; it is the relationship between changes in the measure and changes in the external standard. Therefore, understanding external responsiveness will assist us to learn about the relative importance of external factors; and, how organizations respond to them. It is intuitive that organizations do not and cannot respond to these all factors equally; the relative importance of these factors can itself a significant research topic.

3 Development of the research model External environment is more complex than is perceived. In order to develop our initial research model we started examining existing literature on IS domain. Through the investigation we identified that, although not considered as multidimensional or hierarchical construct; perception on external responsiveness can be reflected with three primary variables, that are, external pressure, external support, and external (environmental) uncertainty. Moreover, different studies examined the sub-dimensions of the primary variables - not necessarily did they group under related concepts. Therefore, we conducted an exploratory qualitative field study to explore the more frequently examined sub-dimensions and, more importantly, to confirm the contextual appropriateness of the

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dimensions and sub-dimensions (Akter et al. 2013). Table 1 examines the different factors of environment and our conceptualization, which is supported by the field study data. For collecting qualitative data, we conducted face-to-face, one-to-one, infield, and in-person interviews with eight livestock farms in Western Australia. As like many qualitative studies, we employed purposive sampling rather than random sampling (Corbin and Strauss 2008). The average interview time was around 1 hour. Reliability was achieved by using the same interview-protocol for each case. Qualitative data were analyzed using ‘content analysis technique’ with NVivo8. With the permission of the participants, the interviews were recorded, then we synthesized and sorted the factors (free nodes) into different but relevant categories to identify the core dimensions, and sub-dimensions (tree nodes); finally we developed their associations. For detail on the qualitative study, see Hossain and Quaddus (2011) and Hossain (2014). Throughout the process, we found support for three primary dimensions (i.e. external pressure, external support, and

external uncertainty), and these dimensions possess nine sub-dimensions (government pressure, market pressure, mimetic pressure, normative pressure; and government support, vendor support, and associative support; and market uncertainty, and technology uncertainty). Although we grouped the sub-dimensions under three primary dimensions based on the underlying themes identified in the qualitative study, we reexamined theoretical linkage to support our findings. The next section presents the variables of our initial research model and demonstrates hypotheses. First, we examine the relationship between the highestorder construct (i.e. External Responsiveness - ER) and the endogenous variable (i.e. intention to adopt) believing Sethi and King’s prescription: “Predictive validity examines the relationship of the measure to a single antecedent or consequent” (Sethi and King 1991, p. 463); as mentioned before, many prior studies applied this approach (Hossain and Dwivedi 2014; Akter et al. 2013). Intention to adopt an innovation is influenced by the external responsiveness of

Table 1 The factors of environment detailed in literature Dimension

Reference

Conceptualized in this study

(External) Environment

Schmitt and Michahelles 2009; Gibbs and Kraemer 2004; Scupola 2003; Jeyaraj et al. 2006; Grandon and Pearson 2004 Premkumar and Roberts 1999; Jeyaraj et al. 2006; Hossain and Quaddus 2014 Kuan and Chau 2001; Wang et al. 2010 Zhu et al. 2006; Zhang et al. 2007; Xu et al. 2004; Shih et al. 2008; Gibbs and Kraemer 2004 Kuan and Chau 2001; Scupola 2003 Wang et al. 2010; Li and Visich 2006 Matta and Moberg 2007; Sharma et al. 2007; Strauss and Fleisch 2002; Quaddus and Hofmeyer 2007 Kuan and Chau 2001; Brown and Russell 2007; Wang et al. 2010 Brown and Russell 2007; Wang et al. 2010; Looi 2005; Zhu et al. 2006b; K Zhu et al. 2003 Zhu et al. 2006; Xu et al. 2004; Grandon and Pearson 2004; Patterson et al. 2003; Premkumar and Roberts 1999; Quaddus and Hofmeyer 2007 Lee and Shim 2007 Kuan and Chau 2001; Lee and Shim 2007 Teo et al. 2003 Teo et al. 2003; Karahanna et al. 1999

Responsiveness to external environment i.e. External Responsiveness External Pressure

External pressure Government pressure Regulatory environment Government policy Trading partner pressure Coercion Industry pressure Competitive pressure Competition

Vendor pressure Industry pressure Mimetic pressure Normative pressure; prestige; image External support

Brown and Russell 2007; Premkumar and Roberts 1999; Hossain and Quaddus 2014 Government (agency) Support Lin and Ho 2009; Premkumar and Roberts 1999; Lin 2006; Gibbs and Kraemer 2004; Looi 2005; Tan and Teo 2000; Min et al. 2007; Teo et al. 1997–98 Change agents’ support Brown and Russell 2007 Vendor support Scupola 2003; Premkumar and Roberts 1999; Yap et al. 1992; Quaddus and Hofmeyer 2007 External/environmental Schmitt and Michahelles 2009; Lin 2009; Uncertainty Patterson et al. 2003; Lin 2006 Market uncertainty Lee and Shim 2007; Chau and Tam 1997

Government pressure Government pressure; market pressure Government pressure; government support Market pressure Market Pressure Market pressure Market pressure Market pressure

Market pressure Market Pressure Mimetic pressure Normative pressure External Support Government Support

Associative support Vendor support External Uncertainty External Uncertainty

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the organization. “We care [environment] and accepted [RFID technology]”, “we cannot ignore [environment], [and therefore we] adjusted ourselves in the way to move [along RFID]”. Therefore, our first hypothesis becomes: H1 External responsiveness will have positive influence on adoption-intention of RFID technology. Three more primary hypotheses have been developed from the three primary dimensions of external responsiveness. However, hypotheses have not been developed from the sub-dimensions because the sub-dimensions are not the antecedents rather they explain “the complexity of the construct” (Martínez García and Martinez Caro 2010). Nonetheless, it is not conceptually plausible that greater government pressure will lead to greater external pressure, for example. However, the influence of the sub-dimensions on the endogenous variable was investigated by Hossain and Quaddus (2013). 3.1 External pressure The motivation to adopt a technology may come from pressure of the external environment which is termed as external pressure (Gatignon and Robertson 1989; Hossain and Quaddus 2014): formal or informal pressures from outside the organization. External pressure has consistently been considered as a significant factor in adoption research (Premkumar and Roberts 1999); not surprisingly is treated the same for RFID adoption (Matta and Moberg 2007; Schmitt and Michahelles 2009). Most studies found a positive relationship between external pressure and adoption; however, the alternative relationship has not been rejected either. For example, Hossain and Quaddus (2013) found that external pressure might develop negative attitude among the adopting firms toward adoption while Scupola (2003) suggested that external pressure may slowdown the rate of adoption. However, our hypothesis checks that: H2 External pressure will have positive influence on adoption-intention of RFID technology. But, the nature of external pressure is not well clarified in the literature. Existing literature as well as our field study confirmed several distinct sources of external pressure: government pressure, market pressure, mimetic pressure, and normative pressure. The first theme, government pressure, represent that government exercises its power to force the targeted people or industry to adopt a system in order to gain compliance to a desired practice (Oni and Papazafeiropoulou 2012; Choudrie and Papazafeiropoulou 2007). This is a “regulative process” (Lawrence et al. 2001). RFID researchers too considered government pressure as one of the leading challenges for RFID adoption (Shih et al. 2008; Luo et al. 2007).

Our field study supported that government pressure is an important dimension of external pressure as exemplified by the following comments: “government diffused the international [market] pressure into us which we cannot ignore”, “we just had to do it”, “business facilities [from government] cannot be accessible if we don’t have [RFID]”. The second theme, market pressure, is the strongest dimension of external pressure (Schmitt and Michahelles 2009; Li and Visich 2006). Business organizations frequently experience fierce competitive pressure (Iacovou et al. 1995; Looi 2005); therefore, they make themselves aware of what competitors are doing, and how new tools and/or technology may provide competitive advantage (Brown and Russell 2007). The respondents emphasized that RFID use is a non-negotiable requirement for food commodities - market pressure; three respondents found RFID system as a prerequisite to do business in valuable markets (e.g. the EU, Korean, and Japanese). Our analysis concluded that the terms competitive pressure, competition, competitiveness, market demand, consumer demand and so on are generally considered, by the farms, as market pressure. However, half of the interviewed farms do not find much pressure from the market but consider RFID as an “emerging market preference”. The third dimension of external pressure is mimetic pressure that “involves the perception of some value of mimicking a behaviour from other referent actors, because the behaviour or form appears to be associated with effectiveness” (Lawrence et al. 2001, p. 628). Mimetic pressure is exerted in an organization by itself when the organization perceives that other organizations in the same industry are getting benefit of an innovation or practice and thus feels pressure to act in a same manner (Teo et al. 2003). Mimetic pressure has been arrived in RFID study too (Sharma et al. 2007). Respondents, particularly the sheep farms, perceive that other farmers who adopted RFID system are getting operational efficiency, and/or getting customer preference. Finally, normative pressure is recognized as an important dimension of external pressure. Social Cognitive Theory (SCT) recognizes that gaining social recognition and status are the main motivators for adopting an innovation. Organizations may tend to share the norms in a given environment to be treated differently or to receive respect from the other members of the society. An organization with a direct or indirect relation to other organizations that have adopted an innovation may get interest to the innovation and adopt the innovation to gain similar social recognition and benefits (Teo et al. 2003; Lawrence et al. 2001). Three of our respondents considered RFID as a means of “image” or “prestige” or “try to be different” or “demonstrating a leadership role” in their community as “small farms around us expect that they would learn from our experience as some of the new things are too expensive”. This construct is more popular as image or prestige (Karahanna et al. 1999).

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3.2 External support

3.3 External uncertainty

External support for relevant technology is considered as an important factor that matters to potential adopters (Huyskens and Loebbecke 2007; Hossain and Quaddus 2014). Many companies may not have the internal knowledge about RFID issues and also may not have the required skill, expertise, and resources to test and implement RFID projects, and would therefore tend to rely on external support (Brown and Russell 2007; Premkumar and Roberts 1999). Hence, we develop our hypothesis as:

Lee and Shim (2007) demonstrated that environmental uncertainty is important (Lin 2006) for firms to help understanding RFID technology because organizations usually pay more attention on innovations when they face an environment with higher instability and chaos (Kimberly and Evanisko 1981; Gatignon and Robertson 1989; Patterson et al. 2003; Lin and Ho 2009b) – which postulates a positive relation between them (Lee and Shim 2007). However, other studies found that external uncertainty hinders adoption (Chau and Tam 1997; Hossain and Quaddus 2014). The qualitative data too support a negative relation. Therefore, we hypothesize that:

H3 Greater external support will have positive influence on adoption-intention of RFID technology. As have done with external pressure, Premkumar and Roberts (1999) used different indicators related to external support: vendor support, agency support, and support from business community (Oni and Papazafeiropoulou 2012). Similarly, Brown and Russell (2007) claimed that external support can have different sources varying from country to country, and from region to region within a same country. First, government is considered as an important source of support (Lin 2006; Tan and Teo 2000) for large-scale adoption of an innovation (Teo et al. 1997–98; Oni and Papazafeiropoulou 2012; Choudrie and Papazafeiropoulou 2007); same is obtained for RFID technology (Lin and Ho 2009b). Ideally, because of the high industry-wide investment requirement for RFID, government can support the infrastructure, information provision, research and development, incentives, demonstrate pilot projects, provision of tax-breaks, and consultancy and counselling services (Luo et al. 2007; Lin 2009; Lin and Ho 2009a, 2009b; Looi 2005; Lin 2006). The second source, vendor support, comes from technology providers (e.g., manufacturer, distributor, and retailer) (Huyskens and Loebbecke 2007; Yap et al. 1992). Vendor support is considered as an important sub-dimension of external environment, especially at the initial stage of a large-scale integration (Lee and Shim 2007; Oni and Papazafeiropoulou 2012). Our respondents stated that vendors may also provide training, discounted products, trial use, customized solutions, and troubleshooting services, and conduct pilot projects. The respondents argued and emphasized on a third distinctive source of support which can be called associative support. It is found that organizations have different formal associations (through which they bargain with externals, and market their products) and informal networks (to share and seek knowledge). The level and frequency of involvement with those networks affect the speed and degree of RFID adoption. Six participants realized that the association should examine, prioritize, and decide on RFID practices than common RFID issues. Hence, we consider it as an important dimension of external support.

H4 Greater external uncertainty will have negatively influence on adoption-intention of RFID technology. By analyzing qualitative data, two different types of uncertainty appeared. First, organizations are not interested to adopt RFID if they do not know how long the demand will be for a RFID-based information system. Similarly, customers’ requirements are diversified; different markets require different detail of information. Some farmers are not interested to invest in keeping more information to entertain a certain market (e.g. Japanese) as they are not certain about other markets’ future intention and timing of such extension of requirements. We call this market uncertainty; Lin (2006) too examined customers’ requirements and found that they vary quickly, and are diversified. In the context of open systems, Chau and Tam (1997) examined market uncertainty as the only dimension of environment. Adding another means of uncertainty, two respondents worried that a better technology may replace the RFID technology which, they think, deters them to adopt RFID technology – technology uncertainty; Rai and Patnayakuni (1996) called it environmental instability. 3.4 Presenting the conceptual model Based on literature and the qualitative study, we propose a hierarchical model as well as a structural model. The hierarchical model is presented as a third-order reflective model; external responsiveness is a third-order construct that is reflected with 3 second-order constructs namely external pressure, support, and uncertainty. External pressure is reflected by government pressure, market pressure, mimetic pressure, and normative pressure whereas external support is reflected by government support, vendor support, and associative support; finally, external uncertainty is better expressed by market uncertainty and technology uncertainty. The structural model proposes that external responsiveness increases intention to adopt RFID. Moreover, external pressure, and external support positively influence adoption

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intention while external (environmental) uncertainty has negative influence (figure 1).

4 Validating the measures with empirical data The model developed earlier has been validated with empirical data collected from a survey. Empirical data were analyzed using PLS path modeling technique. In the following sections, first, we present the rationale of using PLS path modeling, followed by a brief note on the survey; and finally, examine the reliability, validity, and relationships of the collected data. 4.1 PLS path modeling Structural Equation Modeling (SEM) “… has become de rigueur in validating instruments and testing linkages between constructs” (Gefen et al. 2000); it assesses both the

measurement and structural models in a single comprehensive analysis (Roldán and Sánchez-Franco 2012). SEM can be carried out via one of the two statistical techniques: covariance-based (dominated by LISREL, AMOS) and variance-based or component-based (mostly represented by PLS). Either technique could handle our causal model and assess “the reliability and validity of the measures of the theoretical constructs and estimating the relationships among these constructs or variables” (Barclay et al. 1995). However, over LISREL or AMOS, PLS was chosen for this study because of the following reasons. First, general recommendations are in favor of PLS (Henseler et al. 2009; Hulland 1999; Wetzels et al. 2009); Akter et al. (2010) argued that, PLS is generally used in modeling with a level of abstraction higher than that first order constructs under hierarchical reflective or formative framework. Second, PLS is advantageous to studies that are at initial development phase of theory building and used within a new measurement context (Barclay et al. 1995). Third, PLS can accommodate small sample size and does not

First Order Model Third Order Model

Government pressure

Second Order Model

Structural Model

Market pressure External pressure Mimetic pressure

)

+ 2( H

Normative pressure H3(+)

Government support

Vendor support

External support

External responsiveness

Associative support

H4 Market uncertainty

Technology uncertainty

External uncertainty

Fig. 1 The research model to examine the dimensions of external responsiveness and its consequent

(-)

H1(+)

Intention for adoption

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require normally-distributed data. Although we have used data from 372 respondents, compared to the number of constructs, the sample size is still ‘not large’. Fourth, PLS is better suited for complex causal modeling like ours (Hulland 1999; Teo et al. 2003). Fifth, at least two of our hypotheses can have either direction, and “PLS parameter estimates better reveal the strength and direction (i.e., positive vs. negative) of the relationships among variables compared to correlation coefficients” (Calantone et al. 1998, p. 28). Moreover, in PLS, all relationships are modeled simultaneously rather than one at a time (Gefen et al. 2000; Fornell and Bookstein 1982) that eliminates multicollinearity. Therefore, PLS is popular among IS researchers while developing models with higher-order latent constructs (Compeau and Higgins 1995; Chin 1998a, 1998b); prior studies have used the same approach (Akter et al. 2010, 2013; Hossain and Dwivedi 2014). Using PLS principle, the manifest variables (MV) were used repeatedly for the first order construct, second-order latent variable, and finally for the third-order latent variable. We used SmartPLS (version 2.0.M3) over PLS-Graph because of softwareavailability, easy data-handling, easily-obtainable results with more analytical capability (e.g. cross-loading matrix), and better graphical presentation. 4.2 Pilot testing As suggested by Frazer and Lawley (2000), to test the validity of the questionnaire and to rectify any measurement-problem, the questionnaire was pilot-tested with thirteen (13) people. Seven questionnaires were distributed to a group of researchers from multi-disciplines on the basis that “they understand the study’s purpose and they have similar training as the researcher” (Frazer and Lawley 2000, p. 34). Two ‘laymen’ were involved in this research who had no relation to the topic specifically. As for the potential respondents, four questionnaires were distributed among some randomly selected farms to ensure the applicability and relevance of the research topic. Based on the feedback, some modifications were made to the questionnaire. For example, adoption-intention was initially meant to be measured with four items while the fourth item was: “we plan to adopt RFID in next , , , , , ”. But, responding to this question, some respondents wrote “never”, without selecting the offered options. Therefore, the fourth item was excluded from the final questionnaire. Moreover, some textual adjustments were performed for fine tuning the questionnaire. 4.3 Sampling Because of the strong ‘privacy’ provision with most agencies and association in Australia, we found it very difficult during

collecting the contact information of the potential respondents. However, later, a couple of government-agencies were convinced with the research strength; then they provided a couple of databases of the contacts. From the contact list, 1,700 randomly selected farms were invited to participate the survey. Along with a paper-based questionnaire, a temporary URL (Web link) was provided to the respondents so that they could alternatively participate the online survey in Survey Monkey. In return, with a reasonable 22 % response rate, 372 usable responses were used for data analysis. Among the responding farms, 73 % are managed by owner-manager while the rest are corporate farms; 58.2 % are broadacre farm that have other agricultural activities along with livestock, the rest are dedicated livestock farms; and, in terms of annual cash receipt, 41.8 % are in