This paper analyses the determinants of technology intensity of Multinational Enterprises (MNEs) subsidiaries in the Greek food industry by using a unique unpublished firm level data-set. The results indicate that (i) MNEs’ in-house R&D intensity depends on marketing intensity through the use of local advertising and the transfer of intermediate inputs, and that, (ii) the transfer of technological inputs is determined by firms market power, the working of a formal R&D department and training of employees. Size was also found to have a positive effect on technology transfers.
It is widely accepted that MNEs develop an efficiently integrated international network of production facilities. The benefits of this type of organisation derive from the development of a more refined division of the roles of MNE subsidiaries. Therefore the role of an MNE subsidiary in such a network is believed to confer a dynamic advantage (economies of scale and scope) in addition to (subsidiary-level) ownership advantages (market power) as a key factor of improved performance against domestic firms. However, there may be industries in which products are nationally differentiated, and MNE subsidiaries have to remain nationally responsive (Doz, 1986). In these industries domestic conduct may be an important factor. The food industry constitutes a particularly interesting case in manufacturing which experiences the co-existence of smaller national firms and large diversified MNEs (Anastassopoulos, 1997). These MNEs have an extensive network of ‘globalised’ activities (Dunning, 1993) and their competitive strategies are comparable to those of established and sophisticated MNEs in other manufacturing sectors such as electronics, pharmaceuticals, etc. (Anastassopoulos et al., 1995).
The aim of this paper is to investigate the determinants of technological activities of food MNEs’ subsidiaries in Greece, a country with a strong presence of indigenous food firms. Technology intensity is hypothesised to be a main ownership advantage (O) (Hymer, 1960; Kindleberger, 1969; Dunning, 1993), which provides a competitive edge and hence a better performance. The analysis relates this advantage with other firm level advantages borrowed from the industrial organisation theory taking also into account heterogeneity as well as size as a major determinant of economies of scale.
It would be expected that the competitiveness of MNE subsidiaries would be dependent on the nature and extend of their O advantages and on the ways in which they organise the deployment of these advantages in the host country. Nevertheless, the remaining two factors of the eclectic paradigm, i.e. internalisation (I) and location (L) contribute, as well, to the specificity of subsidiaries’ roles (Dunning, 1993).
Empirical studies have broadly used two approaches. Firstly, they directly compare MNE subsidiaries with domestic enterprises (DMEs) using firm or group level data and employing several performance criteria (Vernon, 1971; Hughes et al., 1977; Kumar, 1984; Kumar, 1990; Shaked, 1986; Michel and Shaked, 1986). Secondly,they rely on industry level data to identify the characteristics of sectors, which have a greater employment of these assets than others. The pioneering study of this type was that of Caves (1974a,b) for Canada and the UK but see also Buckley and Dunning, (1976); Dunning, (1980) for the UK, Saunders, (1982); Owen, (1982); Lall and Siddharthan, (1982); Kim and Lyn, (1987) for the US; Anastassopoulos and Traill, (1998) for Greece and Jenkins, (1990) for a review article on MNEs in Less Developed Countries (LDCs).
The main findings of the literature show that MNEs are firms which have the following characteristics: high levels of R&D relative to sales, high levels of product differentiation and a large share of professional and technical workers in their workforce. These constitute the most significant O advantages of MNEs (Markusen, 1995). Moreover, ‘it is clear that the significance of the O advantages varies between MNEs, and is both industry and country specific’ (Dunning, 1993; p. 142).
The literature suggests that MNEs are firms that are characterised by high levels of R&D relative to sales (Vernon, 1971; Horst, 1974; Buckley and Dunning, 1976; Swedenborg, 1979; Owen, 1982; Clegg, 1987; Grubaugh, 1987; Pearce, 1989; Kogut and Chang, 1991). MNEs have relative O advantages in knowledge over DMEs for two main reasons. Firstly, by organising independent R&D departments in different national environments they may create new technology, which takes into account different local tastes and needs. Secondly, they may gain I advantages through the co-ordination of R&D departments, (overall organisational efficiency, oligopolistic use of proprietary technology, etc.), in international markets. Subsidiaries may enjoy access to this knowledge base of the network, and through a local R&D department they may transfer and adapt this knowledge to local market conditions.
R&D work may provide product differentiation advantages to food subsidiaries and increase their profitability. One of the main O advantages of MNEs derives from the acquisition of knowledge and local experience. Established brands, knowledge of consumers’ needs and tastes, flexibility of operation in the domestic market and local marketing abilities would undermine the need for local R&D in mature industries such as the food industry. Local R&D could improve their knowledge and could support the introduction of new products into the market.
In order to measure the determinants of subsidiary level R&D (RDOS) and technology transfers (RTOS) a time series of a cross-sectional random effect model is estimated. The model is presented as:
Yit = a 0 + X b it + e it + ui |
(1) |
Where Yit= RDOS or RTOS (see Table 1, for description of the variables).
RDOS: Proportion of in-house R&D expenditures of firm I, in time t (within Greece) in its total sales
RTOS: Proportion of expenditures on account of royalty and technical fees of firm i in time t,(within Greece) in its total sales.
Vector X, represents firm specific explanatory variables, e it the disturbance term, and ui is the random disturbance characterising the i th observation and has a constant distribution through time. The explanatory variables (vector X) are determined as follows:
Product differentiation
Product differentiation is one major form of non-price competition used mainly in the consumer goods industries. In the food industry, firms compete with each other mainly on a brand identification basis. This is a well-recognised strategy in order to establish and maintain brand name products and thereby increase market share. Advertising intensity is the principal approach to creating consumer loyalty, and it is widely accepted as the most effective method of product differentiation in the food industry. MNEs are firms, which have high levels of product differentiation. Product differentiation (proxied by advertising to sales ratio) was found to be significantly above average for MNEs in many empirical studies (e.g. Dunning, 1958, 1985; Wilmore, 1986; Caves, 1974a,b; Lall, 1980; Owen, 1982; Gupta, 1983; Kumar, 1990). Advertising intensity is expected to be positively related to technology intensity i.e. to both RDOS and RTOS. Advertising intensity is measured here as:
ADOS: Proportion of advertising expenditures of firm i in year t (within Greece) in its total sales.
Skill intensity
MNEs are firms which have a large share of professional and technical workers in their workforce [Caves et al., 1979; Buckley and Dunning, 1976; Dunning, 1980; Pugel, 1978, 1981; Lall, 1980; Kumar, 1990; Papanastassiou, 1995). Through their international operation in different countries, MNEs have accumulated experience and advanced managerial techniques, which may be internally transferred across national borders and cultures through their subsidiaries. Thus training intensity is expected to be positively related to technology transfers. Concerning in-house R&D much of it depends on the availability of local inputs and scientists. Thus either coefficient sign is supportable here. The skill intensity variable is measured as:
TROS: Proportion of training expenditures of firm i, in time t (within Greece) in its total sales.
Firm’s market position
A firm’s market share is usually a proxy for market power (Shepherd, 1972; Gale, 1972; Gale and Branch, 1982; Ravenscraft, 1983; Kwoka and Ravenscraft, 1986). A high market share shows the capability of a firm to exercise dominance of leadership in its market (Cotterill, 1992). Following the market power approach, MNE subsidiaries should possess or tend to acquire significant market share in the host country. It is also important to note that superior cost efficiency is associated with economies of size. The analysis here goes back to traditional organisation theory where size is an important, if not the sole determinant of firm differences. If this assumption holds in the Greek food industry, then size should be the only determinant of technology intensity. Either coefficient sign is supportable here. These variables are measured as follows:
MSHARE: Proportion of sales of firm i, in time t, in total sub-sector turnover.
SIZE: The natural logarithm of the number of employees of firm i in time t.
AGE: The age of firm i starting from the year of establishment in Greece.
Export orientation
MNE subsidiaries may exploit special export opportunities in the Greek food industry, by acquiring domestic firms and taking L advantages of local natural endowments and regional characteristics. It is hypothesised that exports were easier for MNEs, not only due to their international experience, but also because they could effectively transfer products through intra-firm channels. The above statement should be controlled by the organisation of the network of the parent company. Either coefficient sign is supportable here. Export performance is measured as:
EXPOS: Proportion of firm i exports, in time t, in its total sales.
Table.1 Description of the Variables
Variable |
Description |
|
ADOS |
= |
Proportion of firms’ advertising expenditures over sales |
AGE |
= |
The natural logarithm of the age of firm starting from the year of establishment |
EXPOS |
= |
Firms’ exports over sales |
MSHARE |
= |
The ratio of firms’ sales to total industry turnover (subsector 3-digit level) |
RDOS |
= |
The ratio of firms’ in-house (Greece) R&D expenditures to its sales |
RTOS |
= |
Proportion of firms’ expenditures on the account of royalty and technical fees over sales |
SIZE |
= |
The natural logarithm of firms’ assets |
TROS |
= |
Proportion of training expenditures over sales |
Table. 2 Statistics of the Sample
Variable |
Mean |
St. Deviation |
MSHARE |
0.09603 |
0.10025 |
AGE |
2.994 |
1.07632 |
ADOS |
0.03299 |
0.03926 |
RDOS |
0.00188 |
0.00324 |
RTOS |
0.00881 |
0.02531 |
TROS |
0.00137 |
0.00424 |
EXPOS |
0.12171 |
0.22443 |
SIZE |
14.8964 |
0.92378 |
RESULTS
In order to test the determinants of technology intensity the data is fitted into two model specifications (dependent variables RDOS and RTOS respectively). The data set has a total of 125 observations, 25 MNEs for a five-year period. Table 3 reports the estimated coefficients.
Table. 3 Parameter Estimates for RDOS and RTOS
Dependent
Explanatory variables |
RDOS (1) Coefficient (t-statistic) |
RTOS (3) Coefficient (t-statistic) |
Constant |
-0.0011 (-0.379) |
0.043 (2.047) |
MSHARE |
0.0015 (0.361) |
0.063 (2.227)** |
AGE |
0.00027 (0.668) |
0.0011 (0.434) |
ADOS |
0.0359 (3.789) *** |
-0.017 (-0.237) |
RDOS |
-
|
1.877 (2.538)** |
RTOS |
0.0168 (1.773)* |
-
|
TROS |
-0.043 (-0.720) |
1.438 (2.885)*** |
EXPOS |
0.0003 (0.208) |
-0.012 (- 1.073) |
SIZE |
0.00012 (0.214) |
- 0.008 (- 2.068)* |
R2 |
0.32 |
0.43 |
N |
125 |
125 |
*** significant at 1%, ** significant at 5%, * significant at 10%*
The determinants of in-house R&D activities
Market power, as proxied by MSHARE, and local market experience proxied by AGE are not important determinants of in-house R&D for MNEs subsidiaries in the Greek food industry. The parameter estimate for advertising in explaining R&D is positive and statistically significant at the 1 percent level (2-sided). This result confirms that marketing intensity proxied by advertising intensity is an important determinant of R&D. Though MNEs already have a high level of product differentiation and advertising, maybe need to invest in R&D in order to satisfy local tastes and explore local needs in marketing intensive sub-sectors. New product development with minor innovation (e.g., in packaging) needs R&D support, whereas in many cases the development and commercial introduction can take place in a single year due to short product-cycles. RTOS is positive and statistically significant, (at the 10 percent level, one-sided test), in explaining variation in RDOS for MNEs subsidiaries indicating that short-term transfers of technological inputs help in-house technological activities. It is not clear from this analysis whether R&D is of an adaptive or creative nature, however it confirms the importance of licensing in cases where technological competition is important. Training intensity (TROS), export performance (EXPOS) and size (SIZE) were not found to be statistically significant.
The determinants of technology transfer
The parameter estimate for MSHARE is positive and statistically significant at the 5 per cent level (2-sided test). This result suggests that MNEs subsidiaries can effectively use their market power using technological inputs from abroad. Both AGE and ADOS are statistically insignificant. This result suggests that marketing-intensive firms in Greece prefer to use their ownership advantages directly rather than entrust marketing to licensees. The RDOS variable is positive and statistically significant at the 5 percent level (2-sided test) in explaining variation in RTOS. Though MNEs seem to derive technology advantages from their multinationality per se (i.e. economies of common governance and/or scale economies) transfer of technology seem to be complement to in-house R&D, an activity that utilises local inputs and knowledge. TROS is positive and statistically significant for MNE subsidiaries at the 1 percent level, (1-sided test). The transfer of technology or technical expertise needs adaptation work and training of employees. EXPOS found to be statistically insignificant. Size (SIZE) has a negative effect on technology transfer. In particular the coefficient of SIZE is statistically significant at the 10 percent level for the group of MNEs subsidiaries (1-sided test).
CONCLUSIONS
During the 1988-92 period MNEs subsidiaries, operating in the Greek food industry, experienced both in-house R&D activities and technology transfers from the parent. The paper examined the determinants of this practice relating to subsidiary level ownership advantages. Using regression analysis, it has been confirmed that in-house R&D is determined by marketing intensity (product differentiation) and transfers of technological inputs and expertise. Technological transfers are determined by firm’s market position and dominance in market power exercise, the working of a formal R&D unit for the effective and efficient use of these resources and training activities. Size was found to exert a negative effect on technology transfers, indicating that small MNEs use extensively this practice. The results indicate that domestic conduct is equally important for a firm’s technological activities as is network membership. Ownership advantages found (through acquisition of domestic) or developed locally are necessary for the activities of MNE subsidiaries in Greece. This outcome originates issues related to the effect of MNE activities on Greek DMEs as MNEs reshape demand as well as supply conditions through the localisation of their R&D.
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Appendix: Establishing Sampling Criteria.
Information about the population of Greek food manufacturing is provided by three different sources: the National Statistical Service of Greece (NSSG), ICAP Hellas Directory; and NIELSEN Hellas. This study utilises a unique firm level data-set from the NSSG. Accounting data provided in annual reports and accounts constitute both the most accessible and reasonably standardised information; these data were supplied by ICAP. Advertising expenditures were obtained at the product level from NIELSEN Hellas and aggregated at the firm level. Firms, which do not meet in all data sets, were excluded from the study. Since it seems that the relative size of firm is associated with group homogeneity, firms were sorted according to their size. Those firms whose primary industrial activity is outside the food processing industry were excluded from the sample. The sample was established using several international and national sources notably ‘Who Owns Whom: Continental Europe’. Cross-reference was then made with the ICAP Directories to ensure that no significant divestment or exit had taken place during the study period. The final sample contains 25 firms.