دانلود مقاله ISI انگلیسی شماره 16527
عنوان فارسی مقاله

عوامل مؤثر بر پذیرش فناوری اطلاعات و ارتباطات (ICT): تجزیه و تحلیل تجربی بر اساس سطح داده های شرکت برای بخش کسب و کار سوئیس

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
16527 2004 28 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
Determinants of the adoption of Information and Communication Technologies (ICT): An empirical analysis based on firm-level data for the Swiss business sector
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Structural Change and Economic Dynamics, Volume 15, Issue 3, September 2004, Pages 315–342

کلمات کلیدی
- فناوری اطلاعات و ارتباطات مدلی جهانی و رتبه ای از تصویبنفوذ فناوری سازمان جدید در محل کار متمم از فناوری اطلاعات و ارتباطات و سازمان
پیش نمایش مقاله
پیش نمایش مقاله عوامل مؤثر بر پذیرش فناوری اطلاعات و ارتباطات (ICT): تجزیه و تحلیل تجربی بر اساس سطح داده های شرکت برای بخش کسب و کار سوئیس

چکیده انگلیسی

The paper aims at explaining empirically timing and intensity of a firm’s adoption of Information and Communication Technologies (ICT) using a large sample of firms. The analysis is based on the rank and the epidemic model of technology adoption. The explanatory variables include many dimensions of anticipated benefits from and costs of technology adoption, what allows to capture the impact of uncertainty and adjustment costs. The analysis yields results pertaining to the timing and the intensity of ICT adoption (inter- and intra-firm diffusion). Notwithstanding some interesting differences, we find quite a robust pattern of explanation across the adoption variables used. An extended version of the approach explores the role of “New Workplace Organisation” (NWO) as a determinant of the adoption of ICT, as well as the reverse relationship, i.e. the impact of ICT on the adoption of NWO.

مقدمه انگلیسی

Recent contributions to the literature showed that an ICT producing sector is not a precondition to capture the benefits of “Information and Communication Technologies” (ICT). Timely diffusion of new technology or, from a firm’s point of view, its adoption is at least as important to promote macroeconomic growth (see e.g. Pilat and Lee, 2001). Understanding the factors determining technology adoption is thus a highly relevant topic, also from the policy point of view. In this paper, we aim at explaining empirically timing and intensity of a firm’s adoption of ICT as well as of certain elements of this bundle of technologies (Internet, e-selling). To this end, we estimate a basic as well as an extended version of a model of adoption. We shall present empirical evidence regarding the most important hypotheses put forward in the theoretical literature or investigated in previous empirical work; we also take account of propositions derived from case studies or casual observation. However, the paper does not strive for further developing the theory of technology adoption. The analysis refers to ICT in a narrow sense. We do not consider the adoption of other computer-based technologies such as “Advanced Manufacturing Technologies”. In addition, we do not investigate the adoption of “old” brands of ICT such as PCs where diffusion is almost complete. Finally, diffusion of telecommunication devices such as mobile phones also are excluded from this investigation. The study, in the first place, is based on a “rank model” of technology diffusion, which, in explaining inter-firm differences of adoption time and intensity, emphasises differences among firms with respect to the profitability potential of technology adoption arising from the heterogeneity of firms. In addition, we take into account information spillovers from users to non-users, which are the main element of the “epidemic model” of technology diffusion; see e.g. Karshenas and Stoneman (1995) or Geroski (2000) for comprehensive surveys of various brands of diffusion models. According to the empirical literature, “rank effects” and “epidemic effects” are the dominant factors explaining the adoption of new technology (see e.g. Canepa and Stoneman, 2003). The data stem from a large survey on the use of ICT we conducted in the Swiss business sector in autumn 2000. We have at our disposal firm-specific information on, for example, the time period of adoption of nine technology elements, the proportion of employees using specific technologies, the range of application of Internet and Intranet, respectively, the objectives of and obstacles to the adoption of ICT, etc. Moreover, we collected information referring to various structural characteristics of the firm (size, industry affiliation, etc.). In addition, we got data pertaining to workplace organisation which also may serve as determinant of the decision to adopt ICT. The final dataset contains information for more than 2600 firms. Our paper adds to previouswork in variousways. Firstly,we can drawon a large (and new) database in terms of the number of variables and observations as well as the wide coverage of industries/sectors and size classes. Secondly, by estimating the postulated empirical model with several types of adoption measures as dependent variables, we are able to identify differences in the pattern of explanation and to separate robust from shaky relationships; we expect, for example, that the first use of Internet (a basic element of ICT with a broad range of application) is driven by somewhat different forces than the introduction of e-selling, whose profitability potential seems to vary substantially across industries (OECD, 2000). Thirdly, we are able to consider not only inter-firm diffusion but also intra-firm diffusion. To date, not much research has been devoted to this second type of technology diffusion (see Battisti and Stoneman, 2003). Fourthly, and perhaps most importantly, our approach adds to the present understanding of ICT adoption by modelling “rank effects” more comprehensively than it is the case in most empirical analyses. We specify anticipated profitability of ICT adoption by taking account of many dimensions of presumed benefits from as well as costs of adoption. To this end, we use detailed information stemming from our survey which refers to the relevance of specific objectives of and obstacles to the adoption of ICT as assessed by the firms themselves.1 In this way, we are able to take into account some factors such as (technological) uncertainty as well as information and adaptation costs which, although they seem to be highly important, are usually ignored in the empirical analysis of adoption decisions (see Karshenas and Stoneman, 1995). Finally, we explore the role of “New Workplace Organisation” (NWO), which involves practices such as team-working, flattening of hierarchical structures, decentralising of decision-making, etc., as a factor determining the adoption of ICT. The set-up of the paper is as follows: Section 2 is devoted to the conceptual framework of the empirical analysis. Section 3 provides information on the database as well as a brief description of the time path of diffusion of ICT in the Swiss business sector. Model specification and estimation results are shownin Sections 4 (“basic model”) and 5 (“extended model”), respectively. Finally, we assess the main results and draw some conclusions.

نتیجه گیری انگلیسی

The adoption behaviour of Swiss firms in the field of ICT is characterised by a basic pattern of explanation which is quite robust across model estimations with different adoption variables. All categories of explanatory variables we distinguished, though to a different extent, are relevant. Most important are anticipated benefits (primarily those dimensions reflecting market-orientation and efficiency gains) and costs of adoption (in particular, investment costs as well as know-how deficiencies and managerial problems), the firm’s ability to absorb knowledge from other firms and institutions, technological opportunities, information spillovers from adopters to non-adopters, experience with earlier vintages of a certain technology, (international) competitive pressure and firm size. In addition to these firm-specific effects, there is also evidence for industry effects (with a higher probability of adoption in some high-tech industries, modern services and wholesale trade). These empirical results, which are based on a large dataset, are in line with the hypotheses put forward in Section 3, which reflect theoretical thinking, earlier empirical work as well as some casual observations. Basically, the general pattern of explanation is quite similar to what has been found in studies related to “Advanced Manufacturing Technologies”; see Canepa and Stoneman (2003) for a comparison of several empirical studies related to the adoption of these technologies. Rank and epidemic effects are the most important drivers of the adoption of both bundles of technologies. An interesting difference pertains to firm size, one of the most prominent variables in studies of technology adoption. Firm size mostly exerts a strongly positive influence on adoption. In case of ICT, we get a more differentiated picture. We find positive size-effects only up to a threshold of about 200 employees; moreover, in the case of the intra-firm diffusion of the Internet, medium-sized firms seem to have the highest propensity to adopt the new technology. Our results strongly confirm the usefulness of modelling anticipated profitability of technology adoption more comprehensively than it is the case in most empirical models.We take account of a whole set of revenue and cost components. In particular, we are able to include factors such as technological uncertainty, information problems and adjustment costs, which are neglected in most previous studies, although their importance is stressed in the literature. We find that know-how deficiencies, managerial problems as well as costs and financing of ICT are the most important obstacles to introduce these technologies, whereas there is hardly any evidence for a negative impact of (technological) uncertainty and switching costs. Some of these results, in particular those related to management problems and switching costs, differ from those we got in earlierwork dealing with “Advanced Manufacturing Technologies”. The relative importance of the explanatory variables is thus technology-specific. The analysis also shows that ICT is not only a cost-reducing, efficiency-enhancing technology but also exhibits a great potential to generate competitive advantages based on new output characteristics (product innovations, improving customer-orientation, after-sales services, etc.). Furthermore, we identified some interesting differences among results pertaining to specific types of adoption variables, i.e. the timing of the adoption of specific ICT elements (inter-firm diffusion of Internet and e-selling) and the intensity of use of ICT (intra-firm diffusion of ICT in general and of Internet in particular). Firstly, it turned out that the adoption of e-selling is driven to a greater extent by (anticipated) benefits related to customerand market-orientation as well as by epidemic and learning effects than it is the case for the introduction of the Internet; for the Internet, efficiency gains through optimising production processes and relations with suppliers as well as absorptive capacity are more important determinants of timing decisions. Secondly, we also find some differences in explaining inter- and intra-firm diffusion of ICT. Absorptive capacity, firm size, cost of technology as well as anticipated benefits from improving internal processes are more important as determinants of adoption in the case of intra-firm diffusion, whereas quality-oriented and customer-related advantages are more relevant for timing decisions. The overall effect of the postulated explanatory variables on adoption is stronger in case of intra-firm diffusion. This result does not seem implausible, since the firms’ resource commitment often is low in an early phase of the adoption process, whereas beyond a certain level of (intra-firm) diffusion intensifying the use of ICT becomes more complex and expensive (e.g. transition from stand-alone to network technologies). Notwithstanding these differences across various adoption variables, it has to be stressed that the basic pattern of explanations is not very different for the adoption variables distinguished in this paper. Finally,we found evidence for the influential role “NewWorkplace Organisation”(NWO) plays in decisions related to the adoption of ICT. Team-working, decentralised decisionmaking and flattening hierarchical structures seem to be the most relevant dimensions of NWO that favour the adoption of ICT, whereas, not surprisingly, we do not find any impact of job rotation. However, the results regarding NWO may be affected by potential endogeneity of this variable. To circumvent this difficulty, we investigated the reverse causality running from the adoption of ICT to the introduction of NWO, and we found evidence for this proposition as well. In addition, we introduced time lags, alternatively for the ICT and the NWO variable; the results provide some indication of a more sluggish adaptation of organisational structures as compared to technology adoption (“organisation” as a quasi-fixed factor in the short run). These findings seem to be consistent with those of some recent studies which found that ICT and NWO, at least in the longer run, are complementary elements of a strategy to increase efficiency of production and quality of products. However, the conclusions regarding the role NWO plays in the process of ICT diffusion are very preliminary. Further research is required to investigate in greater detail the relationship between the seemingly complementary variables ICT and NWO. As stressed by Brynjolfsson and Hitt (2000, p. 35), correlations between NWO and ICT are not sufficient to prove complementarity. However, the same authors also indicate that after an empirically evaluation of possible alternative explanations, complementarity is often the most plausible interpretation.10 To get more insight, the use of simultaneous estimation techniques with ICT andNWO (and perhaps human capital as well) as endogenous variables would be helpful. Moreover, panel estimation, provided that suitable data are available, could contribute to uncover the dynamic relationship between ICT and NWO.

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