Several threats affect the survival of small, independent retail companies. Adoption and use of Point-of-Sale (POS) systems may offer important benefits to counter these threats. POS systems are not widely used by these retailers, however. This research investigates the determinants of the adoption of POS systems using a conceptual model based on existing adoption theories. Based on this, a survey has been held among 37 Dutch small, independent retailers, to answer the question what the most important determinants for POS system adoption are. This study furthers theory on IT adoption, specifically for small organizations. The practical relevance is that its findings may help in improving POS system adoption. .
The Dutch retail sector consists for 94% of small retail organizations (≤10 employees), altogether employing around 250,000 persons. The retail sector is noticeably present in the trade-driven Dutch economy and acts as an intermediary between industry and consumer. The sector is an important and relevant subject of study from an economical, social and cultural perspective. In this paper we focus on in-store retailing. The environment of this type of retail trade is under pressure. Several interacting threats, like globalization, demanding consumers, increasing administrative burden and an economic recession force the retailers into action. Information and communication technology (ICT) is a double-edged sword in this context (cf. Turban, King, Viehland and Lee, 2004). On the one hand, it can be a threat to smaller retailers for its disintermediation effects and competition through e-tailing (cf. Chircu and Kauffman, 1999), and by its supply chain management effectuation of the larger(franchise) organizations (cf. David, 2008).
On the other hand, ICT likewise provides opportunities to smaller retailers, like opening up new sales channels, reducing administrative tasks and/or enabling strategic management of their enterprise (Turban et al., 2004). A specific type of retail ICT that can be employed to achieve effective store management is a ‘Point-of-Sale’ (POS) system. POS systems are defined in many different ways. On Wikipedia, a retail POS system is defined as “a computer, monitor, cash drawer, receipt printer, customer display and a barcode scanner”. Webopedia.com defines a POS system as “the capturing of data and customer payment information at a physical location when goods or services are bought and sold”. YourDictionary.com defines it as: “A comprehensive computerized checkout system that includes a bar-code scanner, receipt printer, cash drawer, credit and debit card scanner, monitor, and inventory management software.
A point-of-sale system tracks sales and identifies inventory levels in real time”. There are many different types and brands of POS systems available. eBay.com and BuyerZone.com provide a web-based ‘Point of Sale System Buying Guide’, containing over 4,000 different POS equipments for retailers, and 91 different types of POS software. The POS system market in The Netherlands contains no less than 150 vendors, each offering their own ‘unique’ software package. POS systems enable retailers to consult more detailed management information compared to traditional cash registers and Electronic Cash Registers (ECRs).
As this management information is based on sales figures, retailers can improve their business by maintaining a better product strategy and pursuing a more efficient replenishment process matching customer demand, alleviating what is often referred to as the ‘bullwhip effect’ (Lee, Padmanabhan and Whang, 1997). This enables inventory optimization, minimizing storage space and ‘sold-out’ situations. Moreover, cash slips can be stored electronically and the results can be brought up in the POS system immediately, both reducing time spent on administrative tasks. This is specifically relevant for The Netherlands, where the administrative burden for SMEs has increased through regulations
LITERATURE REVIEW: ADOPTION MODELS
In this section we review eight different studies on the adoption of information systems, which were found through literature study. The meta literature search focused on theories and models concerning IS/IT adoption, more specifically of small businesses, retail and/or POS systems. Below, as a result, we first describe two generic adoption models with regard to IS/IT adoption. Next, we discuss six models that address adoption within the retail or small business domain.
The first generic adoption model we refer to here is that of Rogers (2003). His Diffusion of Innovations (DOI) theory describes the adoption of innovations over time. He ascribes the dynamics of adoption behaviour in terms of different groups of people, like innovators and laggards. His theory also indicates how an individual or organisation (i.e. any decision-making unit) decides to adopt (or not) an innovation. This adoption process consists of five different stages: knowledge acquisition, persuasion, adoption, implementation and confirmation.
Rogers specifies three groups of determinants that influence this process: characteristics of the decision-making unit, characteristics of the innovation and information channels. Based on DOI theory, factors concerning the decision-making unit that positively influence adoption are e.g. high social status, low age and financial flexibility. According to DOI, important characteristics of an innovation include: relative advantage, compatibility, complexity, trialability (the degree to which it can be experimented with), and observability (the visibility of its results). Information channels (personal and mass communication channels) are required to spread knowledge of an innovation.
The second generic adoption model is based on Venkatesh, Morris, Davis and Davis (2003), who reviewed technology acceptance models, among which the Technology Acceptance Model (Davis, 1986) and the Theory of Planned Behaviour (Ajzen, 1985). They used elements of each model for a new unified model, called the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Contrary to Rogers’ model, UTAUT concentrates on the adoption behaviour of individuals. In this model, four constructs are defined as determinants of a user’s acceptance and behaviour. Performance expectancy relates to the degree to which the technology is expected to improve job performance.
Effort expectancy concerns the ease of use associated with the technology. Social influence is defined as “[t]he degree to which an individual perceives that important others believe he or she should use the new system” (Venkatesh et al., 2003). Finally, the construct facilitating conditions deals with the degree to which a support infrastructure for the technology is believed to exist. In addition, these four constructs are modelled to be influenced by four so-called moderators, i.e. gender, age, experience and voluntariness. Retail and SME-specific models
We will discuss six main studies and their adoption models below. First, the study by Julien and Raymond (1994) can be mentioned. Their technology adoption model for the retail sector proposes eight organizational aspects as determinants of technology adoption: centralization, complexity, size, status (i.e. independent/affiliated), sector, and assertiveness, rationality, and interaction of the organizational strategy. These determinants were identified in earlier research on technology adoption in small organizations. Technology adoption in this case concerned the use of hardware (business computing, POS systems and telecomputing) and software. In the study 79 firms in food, hardware and clothing were assessed through questionnaires and semi-structured interviews. Clothing firms and large firms were less apt to use POS systems, while firms that had a longer organizational planning horizon used POS systems more often.
Secondly, Chau (1995) researched which factors are important for small businesses in software selection. His research focused on packaged software, as small organizations usually do not buy custom developed software, due to their limited resources. Chau argues that owners/managers of small organizations are less focused on budgeting techniques like ‘net present value’ or ‘internal rate of return’ to make decisions on software investments. Instead, they focus more on criteria aimed at the functionalities and popularity of the software. Also, opinions of vendors, employees, consultants or acquaintances are believed to influence decision making. Based on empirical research among 122 small businesses, he found that the importance of selection criteria varied between owners and managers.
In general, owners seem to focus more on technical aspects, while managers focus more on non-technical aspects. Third, Thong and Yap (1995) developed a model based on the notion that the adoption process of small businesses differs from that of large firms. CEOs play a major role in small firms as they are the primary decisions makers. In their research, the authors developed a causal model, which assumes that the following factors are positively correlated with the likeliness of IT adoption for small firms: business size, competitiveness of the business environment, information intensity, innovativeness, and attitude towards adoption of IT and IT knowledge.
SYNTHESIS: CONCEPTUAL MODEL AND HYPOTHESES
In the previous section, a total of eight models for adoption have been discussed. Most models view the (retail) organization and/or its owner as the decision-making unit. In small organizations, the owner-manager/CEO almost by definition determines IT investments and the IT strategy. Thong and Yap (1995), Ekanem (2005) and Chau (1995) all point out this phenomenon. Therefore, we consider personal variables of the owner (like age and gender) as key determinants of POS adoption by retailers. In addition, organizational characteristics (like size and competition) can be considered as additional, contextual determinants of the IT adoption decision.
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