Automotive Components Industry and Profitability Factors: Evidence from India

Abstract

Based upon the dataset drawn from Centre for Monitoring of Indian Economy (CMIE) Prowess database, World Bank and Annual Survey of Industries (ASI) for a period 2000–2015, this article tests the persistence of profitability and checks the validity of Resource-Based View (RBV) in elucidating the variations in profitability on an industry-specific setting that is, Indian automotive components industry under a Generalized Method of Moments (GMM) framework. The article finds that the persistence of profits is positive and moderate, indicating that the industry is reasonably competitive. The results further suggest that the past R&D intensity, export intensity, size, labour productivity growth, and GDP growth have a positive bearing on the current profitability, while current R&D intensity, A&M intensity, capital intensity, firm leverage and output of OEMs were found to have exercised negative effect. Since past R&D intensity is found to be positively influencing the current profitability, this article infers that RBV holds for this industry.

Keywords

Profitability Resource-based View Automotive Components Industry GMM R&D Intensity

1. Introduction

Indian automotive components industry is amongst the frontline industries of the country which has benefited, to a great extent, from economic liberalisation that started in the early nineties. This industry experienced a meteoric rise after the initiation of limited liberalisation in the eighties marked by the entry of Suzuki Motor Corporation in partnership with Maruti Udyog Ltd (MUL) into the Indian market in 1982. The Phased Manufacturing Program (PMP) under which firms, having collaboration with foreign firms, had to achieve local content to the tune of 95 per cent within five years of the commencement of production, had also actively contributed to the growth of this industry. The localisation process continued despite the removal of PMP in 1992 due to two main factors, firstly, due to the high import duties on completely knocked-down kits (CKD) and components and secondly, imports of CKD were subjected to licensing.

In order to meet the offset obligation under PMP, MUL motivated and nurtured local entrepreneurs to take up components manufacturing, as the existing suppliers were both technologically diffident and inefficient. MUL also facilitated tie-ups between its suppliers and suppliers of Suzuki in Japan and also helped them financially. As a result, MUL achieved local content to the tune of 96 per cent for its flagship model within one decade of its operation in the country (Okada, 2004). The economic liberalisation of 1991 removed the protective layer of licensing requirement and tariff barriers from this industry and opened it to external competition. The partial opening of the industry in the eighties had enhanced its technical capabilities, improved efficiency to nurture the industry to withstand intense competition which it was going to face in the forthcoming decades. The period following liberalisation saw the entry of many multinational Original Equipment Manufacturers (OEMs) into the market, which further contributed to the localisation of components. Moreover, India was fast emerging as an automobile and components manufacturing hub which produced high-quality products at relatively lower cost chiefly attributed to the abundance of a cheap supply of labour. The components industry grew at a Compound Annual Growth Rate (CAGR) of 17. 84 per cent during the period 2000 to 2016, the vehicle assembling segment, on the other hand, grew at a lesser rate of 16.24 per cent during the same period.

The conditions in emerging economies like India are different from that of the developed world. It has witnessed a transformation from a restrictive regime to free-market policies. The firms operating within the country tried to convert the challenges faced in each regime into opportunities and to an extent, they were successful in adapting according to the changing environment. It may be noted that the market liberalisation and the subsequent entry of multinational firms into this domain might have disturbed the existing structural characteristics of the firms, which made the competitive environment highly dynamic. This study attempts to address this crucial gap in the literature by testing persistence of profitability in the highly dynamic environment of emerging markets like India and the relevance of Resource Based View (RBV) in enhancing profitability by developing capabilities which are not easily imitable in an industry-specific setting, i.e., automotive components industry. The existing literature on this industry reported that automotive components firms used technological collaborations and joint ventures with Multinational Corporations (MNCs) as the core strategies to enhance profitability during the transition period. As reforms matured up, creation of new knowledge through increased expenditure on R&D started becoming one of the core stratagems to improve profitability in this industry (Kumaraswamy et al., 2012). This study draws data from the post-reform period to examine the role of R&D expenditure on profitability. It may be noted here that the studies done in the post-reform period had included vehicle assembly firms in their samples which could not be applied explicitly to components industry (Jaisinghani and Tondon, 2016; Jaisinghani et al., 2018). Further, earlier studies on the profit persistence were mostly undertaken on developed countries markets (Goddard et al., 2005; Athanasoglou et al., 2008; Nunes et al., 2012; Nunes and Serrasqueiro, 2015; Djalilov and Piesse, 2016) which seem to be remarkably different from the emerging markets (Glen et al., 2001). The existing literature related to the profitability across various industrial segments in India is limited to the inquiry of the impact of pro-market reforms or foreign ownership on profitability (Chhibber and Majumdar, 1999; Kambhampati and Parikh, 2003; Douma et al., 2006; Chari and Banalieva, 2015).

This study contributes significantly to the existing literature on profitability and firm performance. This study applies the procedure suggested by Blundell and Bond (1998) under the Generalised Methods of Moments (GMM) framework which provides more efficient results especially in case of short panels, i.e. when the crosssection unit is greater than the time period and minimises the loss of information in the unbalanced panel through orthogonal deviation (Roodman, 2009). This study attempts to address the shortage of industry-specific studies in emerging countries like India. The rest of the study is organised as follows: Section 2 presents a broad review of the literature with regards to profitability with particular emphasis on the significant theoretical developments over time. Section 3 discusses the data, measures and estimation techniques used in this article. Section 4 discusses the results of the estimated model, while Section 5 concludes the study.

2. Review of Literature

The initial studies related to industrial price behaviour had contended that concentration, coupled with entry barriers are the prime determinant of profitability (Bain, 1951). It led to the emergence of the structure–conduct–performance (SCP) paradigm. The SCP postulates that the behaviour of the firm is contingent upon its structure, which in turn determines its performance. Increased concentration has been reported to be leading to collusion among the rivals, which ensures superior average profits for the industry. Nevertheless, concentration alone may not enhance average profit unless it is backed up by appropriate entry barriers in the form of scale economies, product differentiation, high capital intensity, patent protection, etc. Thus, SCP predicts a positive association between concentration and profitability. Studies, undertaken by Weiss (1963), Mann (1966), Collins and Preston (1969), Miller (1969), also support the view that concentration is an important determinant of profitability. It may be noted that the SCP looks at the firm’s profitability through the industrial prism considering firms as homogenous entities, which means that SCP is extremely weak in explaining the intra-industry variation in profitability.

Most studies treated the industry as a unit and considered that entry barriers protect all the firms of an industry in an equal measure. When this assumption is relaxed, there is a possibility that entry barriers may restrict mobility among groups within the same industry (Caves and porter 1977). Such groups, known as strategic groups, are a cluster of firms within an industry that follows a similar strategy in terms of product differentiation, innovation, advertising, etc. In strategic group literature, mobility barriers are sometimes used synonymously with entry barriers that prevent equally both intra-industry mobility between strategic groups and outside firms seeking entry into a particular industry. Strategic group formation may lead to a segmentation of the industry, but it does not mean that groups are classified solely based on products that are close substitutes. Groups may also be classified based on factors affecting the conditions of sale of a product or factors that are not like product differentiation (Caves and porter 1977). This hypothesis implies that profitability differences would exist among the strategic groups in the industry. The literature, based on Strategic group hypothesis, attempts to address intra-industry differences in profitability by classifying firms in an industry with similar orientation taking each one of them as a unit of analysis and then tests profitability differences among the groups (Cool and Schendel, 1988; Mascarenhas and Aaker 1989; Fiegenbaum and Thomas, 1990). However, the lack of consensus with regards to the classification of strategic groups and methodology has resulted in the conflicting results making this literature inconclusive.

On the other hand, the strive to raise and sustain competitive advantage among firms in an industry might cause industry segmentation. The fundamental reason for industry segmentation is the heterogeneous nature of products as well as buyers prevailing in the industry. As a result, different competitive strategies are devised for diverse segments in an industry. Mobility barriers also exist among such segments in an industry (Porter, 1985). Porter (1985) subdivides an industry with the help of four variables which very well capture this heterogeneity among buyers and sellers. For instance, the industry could be segmented based on the variety of products produced within an industry or based on the types of ultimate buyers buying its products. The industry could also be segmented based on distribution network employed by the producer or based on buyers’ geographic locations. If a strategic group is grouped based on product varieties in an industry, then the dynamics of mobility barriers would be alike for both industry segment as well as the strategic group.

Homogeneity assumptions within the strategic groups were observed in studies concerning strategic group literature. Nevertheless, this assumption does not explain why some firms exhibit superior performance, even though they might be in the same strategic group. Barney (1991) relaxes the assumption of homogeneity among firms within an industry or in a group by drawing upon the postulation of heterogeneity in resources thus adding or restricting comparative advantages of the firms within the same strategic group, hereafter referred to as Resource-Based View (RBV) of competitive advantage. It may be noted that the competitive advantage gained by heterogeneity can be sustained only when the resources are not perfectly mobile (Peteraf, 1993). Thus, relative resource immobility and heterogeneity ensure sustained competitive advantage, which results in superior performance by some firms in an industry or a group. The logical progression from SCP to RBV indicates that there has been a dramatic evolution concerning the main drivers of profitability. The SCP focused on the industrial structure (industry effect) while the RBV focuses on the unique nature of firms (firm effect) in determining profitability.

In this regard, the debate between Structuralist and RBV schools has assumed much more importance. For instance, while the former argues that the industrial structure plays a critical role in determining a firm’s profitability, the latter emphasises more on the internal characteristics of firms in determining profitability. Schmalensee (1985) using variance decomposition analysis decomposed Return on Assets (ROA) for a cross-section of firms operating in the manufacturing sector of United States of America (USA) for a single year and found a significant industry effect, though the firm-effect was reported as negligible. Wernerfelt and Montgomery (1988) decomposed Tobin’s q instead of ROA on the same data and supported the prominence of industry effect. Rumelt (1991) using extended data of what employed by Schmalensee (1985), decomposed ROA on the extended model of Schmalensee (1985) and found a sizeable firm effect and relatively small industry effect. The contradictory findings of Rumelt (1991) might be due to the use of longitudinal data or differences in model specifications. Later studies confirm the relative importance of firm effects over industry effects in explaining profitability (Roquebert et al., 1996; McGahan and Porter, 1997; Mauri and Michaels, 1998). It appears that the rapidly changing business environment in this era of liberalisation and globalisation makes it difficult for firms to leverage from its industry-specific structural characteristics (Galbreath and Galvin, 2008).

Another strand of literature evolved suggests that profits revert towards the normal rate of returns as a result of competition. If the rate of reversion towards the average rate of returns is high, it is the case of a low persistence of profits on account of fierce competition. On the other hand, if the rate of reversion towards the average rate of returns is low, the persistence of profits is high, which may be accounted for by relatively lower competition. These studies, undertaken on varying numbers of firms in US, UK, France, and Germany, primarily focus on the association between competition and profitability (Mueller 1977, Geroski and Jacquemin 1988, Goddard and Wilson 1996). Interestingly, Glen et al. (2001) found that the persistence of profits is lower in emerging economies as compared to developed economies. In sum, the persistence of profits is observed across different countries though at varying rates.

Recently, the persistence of profits is measured within the GMM framework along with several firm-specific, industry-wide or macroeconomic variables within an industry or across industries. Goddard et al. (2005) test the persistence of profitability in both manufacturing and services sectors of five European countries under the GMM framework, along with several firm-specific factors, and found that profit persistence is higher in services as compared to the manufacturing sector in two countries namely Belgium and United Kingdom (UK) and lower for services as compared to the manufacturing sector in the other three countries namely France, Italy, and Spain. Athanasoglou et al. (2008) applied the procedure suggested by Arellano and Bond (1991) on a sample of Greek Banks. They found a moderate profit persistence and insignificant impact of concentration on banks’ profitability. Nunes et al. (2012) using a sample of Portuguese Small and Medium Enterprises (SMEs) found that profit persistence among old SMEs is much higher than that of new SMEs and that older SMEs accord relatively more importance to research and development. Nunes and Serrasqueiro (2015) found persistent profitability along with several significant firm-specific variables in a sample of Portuguese knowledge-intensive services firms. Djalilov and Piesse (2016) using GMM found that persistence of bank profitability is higher in late transition countries of the former Soviet Union than in early transition economies in Central and Eastern Europe.

Among the studies conducted on the firms operating in India, the existing literature mostly confined its domain of enquiry into the influence of pro-market reforms or foreign ownership on profitability across a cross-section of industries (Chhibber and Majumdar, 1999; Kambhampati and Parikh, 2003; Douma et al., 2006; Chari and Banalieva, 2015). Chhibber and Majumdar (1999) found that firms with high foreign ownership demonstrated better profitability as compared to their domestic peers. The study conducted by Douma et al., 2006 had also reached similar conclusions. Kambhampati and Parikh (2003) found that pro-market reforms have a significant influence on the profitability of Indian firms. Chari and Banalieva (2015) found a U-shaped relation between pro-market reforms and firm’s profitability among Indian firms. Among the studies conducted on the Indian automotive industry, Kumaraswamy et al. (2012) found that domestic automotive components firms were able to convert the challenges posed by pro-market reforms and subsequent entry of foreign firms into opportunities through technological collaborations or by entering into joint ventures with MNCs during the early transitions years which had a negative influence on its profitability initially. As reforms matured, these catch-up strategies had a positive impact on their profitability, and the creation of new knowledge through enhanced expenditure on R&D became one among its various strategies to improve profitability. Jaisinghani and Tondon (2016) find that profit persistence is moderate and R&D have a significant positive influence on profitability in the Indian automobile industry. Jaisinghani et al. (2018) also find that profit persistence is moderate and capital intensity have a significant negative influence on profitability in the Indian automobile industry.

3. Methodology

Data and Sample

This study draws upon the Centre for Monitoring of Indian Economy (CMIE) Prowess database comprising an unbalanced panel of 78 publicly listed Indian automotive components manufacturing firms for the period 2000–2015. This database classifies firms operating in the components industry into three segments, namely batteries, tyres and tyre products and other ancillaries. Only the firms for whom the data are available continuously for the study period are considered. Table 1 classifies the total number of firms in this industry into different segments as per the classification given in the CMIE Prowess database.

Table 1.
Classification of the Indian Automotive Components Industry

Total Firms Sample Firms

Batteries 29 3

Tyres and tyre products 46 7

Other Ancillaries 890 68

	Total Firms	Sample Firms
Batteries	29	3
Tyres and tyre products	46	7
Other Ancillaries	890	68

Source: Compiled from CMIE Prowess database.

Data on GDP growth have been taken from the official database of the World Bank and industry-wide data from the Annual Survey of Industries (ASI) of the Central Statistics Office (CSO) of India and OEMs data from various published reports, companies’ publicly available brochures as well as their websites.

Measures

Dependent Variable

Firm’s profitability in this study is measured by accounting ratios like Return on Assets (ROA) and Return on Capital Employed (ROCE) (Goddard et al., 2005; Athanasoglou et al., 2008; Nunes et al., 2012; Mistry, 2012; Chari and Banalieva, 2015). One serious drawback of these measures is that their utility is limited for comparison purposes due to the variation in accounting policies across countries. On the other hand, these measures are most suitable to be employed in case of a single industry study where the sample is relatively undiversified (Venkatraman and Ramanujam, 1986). ROA provides information about how well a firm’s assets are utilised to generate profits. Similarly, ROCE reveals information about how well a firm’s capital is used to generate profits. ROA is estimated by taking the ratio of earnings, before tax, to total assets and ROCE is estimated by taking the ratio of earnings, before interest and tax, to capital employed.

Independent Variables

Research and Development Intensity: Research and Development Intensity (R&D intensity) captures the degree of innovative activities performed by firms. R&D intensity is measured by the ratio of R&D expenditure to total sales for a given year. Studies undertaken by Scherer (1965), Grabowski (1968), Branch (1974), Jefferson et al. (2006), Jaisinghani and Tondon (2016) report a positive relationship between R&D intensity and profitability.

Advertising and Marketing Intensity: Advertising and marketing intensity (A&M intensity) is estimated by taking the ratio of advertising and marketing expenditure to total sales for a given year. Studies carried out by Frederick H. deB. Harris (1986), Majumdar (1997), Kambhampati and Parikh (2003), reveal a positive relationship between A&M intensity and profitability.

Capital Intensity: Capital intensity (CI) indicates the amount of fixed assets required to produce one unit of output. CI is measured as net fixed assets scaled by sales for a given year. The literature on the relationship between CI and profitability is not conclusive. For instance, while few studies have reported a negative bearing of capital intensity on the profitability (Schoeffler et al. 1974, Hatten and Schendel 1977, Chhibber and Majumdar 1999), others have reported a positive relationship (Johnson and Thomas 1987, Feeny et al. 2005, Tyagi and Nauriyal 2017).

Firm Leverage: Firm Leverage (FL) measures the competency of a firm to pay back its debts. It is measured as total debt scaled by total assets. Recent literature has reported negative bearing of Firm Leverage on profitability (Asimakopoulos el al 2009, Chari and Banalieva 2015).

Productivity Growth: The labour productivity growth used in this study is estimated by accounting for the annual growth of total output scaled by wages and salaries paid to the employees. The conventional literature and recent research have suggested a positive relationship between productivity growth and profitability (Athanasoglou et al. 2008).

Export Intensity: Export Intensity (EI) captures the competitiveness of the firms in the international markets. It is estimated by taking the ratio of total exports to total sales for a given year. It is hypothesised that EI would have a positive impact on profitability, even though the existing literature is, more or less, inconclusive about this association (Wagner, 2012; Grazzi, 2012; Vu et al., 2014).

Firm’s Size: Larger firms have greater access to imitable resources which leads to economies of scale and hence higher profitability. Inflation-adjusted total sales are used as a measure for a firm’s size. The literature predicts a positive association (Asimakopoulos et al., 2009; Kumaraswamy et al., 2012; Yazdanfar, 2013; Chari and Banalieva 2015; Nunes and Serrasqueiro, 2015).

Firm’s Age: The natural log of years since the date of incorporation of the firm is used as a measure of the firm’s age. Positive relations between age and profitability indicate that older firms are more profitable, while negative association indicate the contrary (Kumaraswamy et al., 2012; Yazdanfar, 2013; Nunes and Serrasqueiro, 2015).

GDP Growth: GDP growth is expected to have a positive impact on industry profitability (Chari and Banalieva 2015). India experienced a high growth rate of GDP during the study period, and this high growth is expected to trickle down to different sectors of the economy.

The output of Original Equipment Manufacturers (OEMs): As a major chunk of Indian automotive components industry’s output is consumed by domestic OEMs, any volatility in OEMs is quickly transmitted into the components industry. It is expected that the output of local OEMs has a significant impact on the components industry’s profitability.

Estimation Method

Following the works of Nunes and Serrasqueiro (2015) and Djalilov and Piesse (2016), this study applies a dynamic panel data model, i.e. two-step system GMM, an estimation of the model necessitates the treatment of lagged dependent variable as an independent variable. The system GMM approach provides more accurate and efficient results as compared to the simple panel and difference GMM estimators, especially in case of short panels, i.e. when the cross-section unit is higher than the time-period or when autoregressive parameters to be estimated are relatively large (Blundell and Bond, 1998). This approach also minimises the loss of information in the unbalanced panel through orthogonal deviation (Roodman, 2009). The models estimated for this study is as given below:

Model I

\begin{matrix} R O A_{i, t} = C + δ R O A_{i, t - 1} + β_{1} R D I_{i, t} + β_{2} A D I_{i, t} + \\ β_{3} C I_{i, t} + β_{4} F L_{i, t} + β_{5} P G_{i, t} + β_{6} S I Z E_{i, t} \\ + β_{7} A G E_{i, t} + β_{8} E I_{i, t} + β_{9} G D P_{i, t} + \\ β_{10} O O E M_{i, t} + ε_{i, t} \end{matrix}

(1)

Model II

\begin{matrix} R O A_{i, t} = C + δ R O A_{i, t - 1} + β_{1} R D I_{i, t} + β_{2} R D I_{i, t - 1} + \\ β_{3} R D I_{i, t - 2} + β_{4} A D I_{i, t} + β_{5} C I_{i, t} + β_{6} F L_{i, t} + \\ β_{7} P G_{i, t} + β_{8} S I Z E_{i, t} + β_{9} A G E_{i, t} + β_{10} E I_{i, t} + \\ β_{11} G D P_{i, t} + β_{12} O O E M_{i, t} + ε_{i, t} \end{matrix}

(2)

Model III

\begin{matrix} R O C E_{i, t} = C + δ R O C E_{i, t - 1} + β_{1} R D I_{i, t} + β_{2} A D I_{i, t} + \\ β_{3} C I_{i, t} + β_{4} F L_{i, t} + β_{5} P G_{i, t} + β_{6} S I Z E_{i, t} + \\ β_{7} A G E_{i, t} + β_{8} E I_{i, t} + β_{9} G D P_{i, t} + \\ β_{10} O O E M_{i, t} + ε_{i, t} \end{matrix}

(3)

Model IV

\begin{matrix} R O C E_{i, t} = C + δ R O C E_{i, t - 1} + β_{1} R D I_{i, t} + β_{2} R D I_{i, t - 1} + \\ β_{3} R D I_{i, t - 2} + β_{4} A D I_{i, t} + β_{5} C I_{i, t} + β_{6} F L_{i, t} + \\ β_{7} P G_{i, t} + β_{8} S I Z E_{i, t} + β_{9} A G E_{i, t} + β_{10} E I_{i, t} + \\ β_{11} G D P_{i, t} + β_{12} O O E M_{i, t} + ε_{i, t} \end{matrix}

(4)

W h e r e, ε_{i, t} = μ_{i} + u_{i, t}

(5)

Where ROA_i,t is the return on assets of firm i at time t, ROCE_i,t is the return on capital employed of firm i at time t, C is the constant term. ROA_i,t_–1 and ROCE_i,t_–1 are the respective lagged dependent variable. RDI_i,t is the Research and Development Intensity for firm i at time t, RDI_i,t_–1 and RDI_i,t_–2 are the two-period lags of Research and Development Intensity for firm i at time t, ADI_i,t is the Advertising and Marketing intensity for firm i at time t, CI_i,t is the Capital intensity for firm i at time t, FL_i,t is the Firm leverage for firm i at time t, PG_i,t is the productivity growth for firm i at time t, SIZE_i,t is the firm’s size for firm i at time t, AGE_i,t is the firm’s age for firm i at time t, EI_i,t is the export intensity for firm i at time t, GDP_i,t is the GDP growth and OOEM_i,t is the Output of OEMs. ε_i,t is the composite error term which includes an unobserved firm-specific error μ_i as well as the idiosyncratic error u_i,t.

The dynamic nature of the panel renders the panel data estimation (both fixed and random effect model) biased and inconsistent since the lagged dependent variable is correlated with the error term ε_i,t. Even though the fixed effect within estimator removes the individual effects μ_i, the correlation between the lagged dependent variable and error term u_i,t persists. Therefore, there is a need for an estimator in which error term ε_i,t is not only uncorrelated with the individual effects μ_i but also independent of the lagged dependent and explanatory variables. Arellano and Bond (1991) develop an estimator which first differences the equations (1), (2), (3) and (4) thereby eliminating the individual effects μ_i and uses lagged levels as instruments for lagged dependent and endogenous variables which are independent as compared to the error terms ε_i,t. This estimating technique is known as difference GMM, which is efficient when the time-period is relatively large.

Blundell and Bond (1998) found that when the time period is small as well as autoregressive parameters to be estimated are large; the difference GMM estimator had a significant finite sample bias and poor precision. It is mainly due to the presence of weak instruments. Blundell and Bond (1998) overcome this problem by developing an extended version of GMM estimator known as system GMM. This approach augments both the level equation and the first difference equation and estimates them together by treating them as a system. It instruments lagged differences for level equation and lagged levels for first difference equation.

4. Results and Discussion

Table 2 classifies the sample firms based on their targeted customers, i.e. OEMs. The industry segments are grouped based on the final products produced by the sample firms. Accordingly, the Components Industry is classified into various segments viz. engine and engine parts, transmission and steering parts, suspension and braking parts, electrical parts, equipment and others. It can be seen from Table 2 that the majority of firms have both local and multinational OEMs as their customers. The number of firms having only multiple local OEMs as their customers from the total sample is 20. It can be observed that very few firms are targeting single local OEM or multiple multinational OEMs only. Among the segments, most of the firms in the engine and engine parts segment have both local and multinational OEMs as their customers. On the other hand, electrical parts segment has the least number of firms targeting both local and multinational OEMs. The number of firms targeting only multiple local OEMs is very high in the equipment segment and very low in engine and engine parts segment.

Table 2.
Classification of the Sample Firms Based on Targeted Customers

Segments Single Local OEM Multiple Local OEMs Local/Multinati-onal OEMs Multinatinal OEMs Total

Engine and engine parts 0 1 12 1 14

Transmission and steering parts 1 4 9 1 15

Suspension and braking parts 0 2 8 0 10

Electrical parts 0 4 4 1 9

Equipments 0 6 10 0 16

Others 1 3 10 0 14

Total 2 20 53 3 78

Segments	Single Local OEM	Multiple Local OEMs	Local/Multinati-onal OEMs	Multinatinal OEMs	Total
Engine and engine parts	0	1	12	1	14
Transmission and steering parts	1	4	9	1	15
Suspension and braking parts	0	2	8	0	10
Electrical parts	0	4	4	1	9
Equipments	0	6	10	0	16
Others	1	3	10	0	14
Total	2	20	53	3	78

Source: Authors’ Compilation from the sample firms’ publicly available brochures as well as websites.

It may be noted that the conditions for work in emerging economies like India for doing businesses are strikingly different from that of the developed world. The resource constraints experienced in such economies, coupled with the predominant institutional voids, severely restrict the capabilities of domestic industries (Khanna and Palepu, 1997). It has been observed that in emerging economies, the firms affiliated to business groups perform better than the unaffiliated firms (Khanna and Palepu, 2000; Khanna and Rivkin, 2001). The access to common group’s resources such as financial capital, the pool of scientific manpower and accumulated technological know-how etc. enhance their prospects to perform better than the unaffiliated firms. Besides this, foreign firms may prefer to partner with business group firms because of their credibility to honour contracts (Mahmood and Mitchell, 2004).

To examine the ownership patterns and foreign collaboration in enhancing the technological capabilities of this industry, Table 3 classifies various industry segments into four groups: ‘firms affiliated to business groups’, ‘business group firms with overseas presence’, ‘unaffiliated firms’, ‘unaffiliated firms with overseas presence’. From Table 3, it can be observed that majority of the sample firms are affiliated to a business group, and the number of business group firms, having an overseas presence, out of the total firms having an overseas presence is considerably high. The overseas presence is expected to stimulate collaboration with potential partners. The R&D intensity is highest among the firms which are affiliated to a business group followed by business group firms having an overseas presence. It appears to suggest in some way that the access to common group’s resources is channelled towards the development of technological capabilities of the affiliated firms. Among the segments, R&D intensity is highest in the electrical parts segment for firms which are affiliated to a business group and least in others segments. Engine and engine part segment has the highest R&D intensity among the firms with overseas presence and least in transmission and steering parts segment. Suspension and braking parts segment has the highest R&D intensity among the business group firms with overseas presence and least in transmission and steering parts segment. Suspension and braking parts segment has the highest R&D intensity among the unaffiliated firms without any overseas presence and least in electrical parts segment.

Table 3.

Classification based on Ownership Patterns and Overseas Presence of the Sample Firms across Various Segments

Industry Segments	Number of Firms				R&D Intensity
Industry Segments	Overseas Presence of Unaffiliated Firms	Business-Group Firms	Business-Group Firms with Overseas presence	Others	Overseas Presence of Unaffiliated Firms	Business-Group Firms	Business-Group Firms with Overseas presence	Others
Engine and engine parts	5	9	3	3	0.00783	0.00782	0.01009	0.00512
Transmission and steering parts	2	8	2	7	0.00146	0.00408	0.00229	0.00309
Suspension and braking parts	2	3	1	6	0.00635	0.00943	0.01013	0.00758
Electrical Parts	4	5	3	3	0.00627	0.01082	0.00777	0.00090
Equipments	8	4	2	6	0.00667	0.00471	0.00248	0.00398
Others	6	9	6	5	0.00404	0.00358	0.00404	0.00320
Total	27	38	17	30	0.03264	0.04046	0.03684	0.02390

Source: Authors’Compilation from CMIE Prowess database, 2017.

Table 4 reports the descriptive statistics of the dependent and independent variables. It reveals that the variables, A&M intensity, capital intensity, firm leverage, age, GDP growth and output of OEMs are not volatile, as the standard deviations of these variables are below their respective means.

Table 4.

Descriptive Statistics

Variables	Mean	SD	Minimum	Maximum
ROA	0.083	0.099	–1.032	0.893
ROCE	0.106	0.216	–4.060	0.915
RDI	0.006	0.007	0.000	0.104
AMI	0.019	0.019	0.000	0.178
CI	0.319	0.290	0.030	5.372
FL	0.283	0.175	0.000	2.094
PG	0.000	0.206	–0.774	3.363
EI	0.128	0.151	0.000	0.925
AGE	3.414	0.581	0.000	4.736
SIZE	0.008	0.013	0.000	0.161
GDP	0.071	0.021	0.038	0.103
OOEM	0.915	0.502	0.262	1.824

Source: Authors’ calculations.

The variables ROA, ROCE, R&D intensity, productivity growth, export intensity, and size are volatile as the standard deviation values of these variables are above their respective mean values. The volatility in ROA and ROCE shows that there exists variation in profitability among the firms selected in the sample. The volatility in size indicates that the sample is heterogeneous. The sample consists of a mix of small and big firms that form the components industry. Table 5 reports the correlation matrix of the independent variables and brings to the fore that the values of most of the correlation coefficients are small and significant which suggest that the likelihood of bias in the results, due to multicollinearity, are very slim.

Table 5.

Correlation Matrix of the Independent Variables

		1	2	3	4	5	6	7	8	9
1	RDI
2	AMI	–0.028
3	CI	0.090^a	0.099^a
4	FL	0.022	0.088^a	0.531^a
5	PG	–0.058	0.046	0.099^a	0.119^a
6	EI	0.006	0.129^a	0.110^a	0.107^a	–0.03
7	AGE	0.104^a	0.302^a	–0.025	–0.031	–0.053	0.204^a
8	SIZE	0.031	0.033	–0.094^a	–0.114^a	–0.041	–0.012	0.254^a
9	GDP	–0.023	–0.061^b	–0.102^a	–0.065^b	0.035	0.046	0.105^a	0.060^b
10	OOEM	0.142^a	–0.142^a	0.005	–0.088^a	–0.156^a	0.087^a	0.289^a	0.250^a	0.251^a

Source: Authors’ calculations.

Note: “a” and “b” coefficients are significant at 1 and 5 per cent levels, respectively.

Table 6 reports the results of the system GMM estimation of the determinants of profitability. Here four models of profitability are estimated. The model I and III are estimated by treating the lagged profitability as an endogenous variable. It includes all the explanatory variables and one lagged value of the dependent variable for its estimation. Investment in research and development projects influences firm’s profitability through new product development or by introducing innovation in the production process. Since, R&D projects are like a long-term investment, whose fructification into a successful innovation materialises only after a time lag, it is expected that past R&D will have a positive influence on current profitability. Model II and IV are estimated to test the impact of the previous year’s R&D on current profitability. These models are estimated by treating both lagged profitability and R&D intensity as endogenous variables. One lag of profitability and two lags of R&D intensity have been introduced in models II and IV. The Hansen test confirms that the instruments used in all the four models are valid and there is an absence of second-order autocorrelation, even though the first-order autocorrelation is present in all the four models. The presence of the first-order autocorrelation, however, does not render models I, II, III and IV inconsistent.

Table 6.

Results of the system GMM estimation

Independent Variables	Dependent Variable: ROA				Dependent Variable: ROCE
	Model I		Model II		Model III		Model IV
	Coefficients	SE	Coefficients	SE	Coefficients	SE	Coefficients	SE
ROA _i,t–1	0.4101***	0.0636	0.4931***	0.0882
ROCE _i,t–1					0.3975***	0.0776	0.4324***	0.1149
RDI _i,t	–0.4643	0.2871	–0.0541	2.1028	–1.6893***	0.5906	–4.2468	4.2378
RDI _i,t–1			2.2696*	1.3176			5.7041**	2.9535
RDI _i,t–2			0.1394	0.3250			–0.4036	0.9718
AMI _i,t	–0.7220***	0.1672	–0.4133**	0.1711	–0.9268***	0.2758	–0.5246**	0.2775
CI _i,t	–0.0304*	0.0158	–0.0385**	0.0153	–0.0813***	0.0261	–0.0816***	0.0264
FL _i,t	–0.1780***	0.0210	–0.1423***	0.0345	–0.2375***	0.0360	–0.2131***	0.0582
PG _i,t	0.0147	0.0127	0.0290*	0.0157	0.0224	0.0277	0.0406*	0.0247
EI _i,t	0.0523***	0.0185	0.0490*	0.0273	0.0839***	0.0327	0.0881**	0.0412
AGE _i,t	0.0004	0.0074	–0.0025	0.0070	–0.0061	0.0110	–0.0097	0.0104
SIZE _i,t	0.7247***	0.1624	0.7849***	0.2820	0.9823***	0.3156	1.2181***	0.4800
GDP _i,t	0.2603***	0.0735	0.2158**	0.0982	0.5079***	0.1094	0.3955***	0.1486
OOEM _i,t	–0.030***	0.0044	–0.0281***	0.0046	–0.0351***	0.0087	–0.0333***	0.0074
C	0.1194***	0.0268	0.0965***	0.0273	0.1851***	0.0431	0.1680***	0.0560
Hansen	12.41	p-value = 0.494	30.82	p-value = 0.195	14.41	p-value = 0.346	27.21	p-value = 0.345
AR (1)	z = –2.12	p-value = 0.034	z = –2.04	p-value = 0.041	z = –2.17	p-value = 0.030	z = –2.12	p-value = 0.034
AR (2)	z = 1.22	p-valur = 0.224	z = 0.195	p-value = 0.144	z = 1.42	p-valur = 0.154	z = 1.56	p-value = 0.119

Source: Authors’ calculations.

Notes: “SE” is standard errors, “***”, “**”, “*” coefficients are significant at 1, 5 and 10 per cent level, respectively.

The coefficients of the lagged profitability are positive and highly significant in all the four models I, II, III and IV, indicating the persistence of profitability in the industry. The coefficient δ takes the values 0.40, 0.49, 0.39 and 0.43 in Models I, II, III and IV, respectively, indicating a moderate persistence of profitability, which means the industry is reasonably competitive. The profit persistence also suggests that the firms can sustain and expand their customer base, despite the competitive nature of this industry. The successful localisation of components has ensured a sustained growth in this industry; concurrently economic liberalisation and the consequent entry of foreign firms have also ensured enhanced competition within this industry. The results imply that the components industry of India is not very far away from a perfectly competitive market structure. It may be pointed out that this finding is in sharp contrast to the results reported by Glen et al. (2001), Glen et al. (2001). They provided evidence of the low persistence of profit among manufacturing firms in emerging countries. However, these findings cannot be generalised in the present scenario as the sample used in Glen et al. (2001) consists of top 100 manufacturing firms from a cross-section of industries from a selected set of emerging economies. Besides, the competitive structure also varies across industries. Further, some of the earlier studies conducted on the Indian automobile industry confirms a moderate profit persistence in the components industry which was a subset of the automobile industry in these studies (Jaisinghani and Tondon, 2016; Jaisinghani et al., 2018).

Moving on to the independent variables, the R&D intensity in Models I, II, III and IV exhibit a negative impact on profitability, but it is significant only in model III. It implies that current R&D intensity has a draining effect on current profitability. In this regard, it may be appropriate to examine the effect of lagged R&D intensity on current profitability which has been done in Models II and IV. The findings are that R&D intensity lagged by one year is positive and significant but R&D intensity lagged by two years is insignificant in both the models. Thus, last year’s R&D intensity seems to have a positive influence on profitability which means investment in research and development enhances the firm’s inimitable competitive capacities. This finding supports RBV, since the unique capabilities created through R&D expenditures improve the firm’s profitability. It confirms the hypothesis given by Kumaraswamy et al. (2012) that as the pro-market reforms mature, development of unique capabilities through technological up-gradation becomes one among the core strategies to enhance profitability which is in line with findings of Jaisinghani and Tondon (2016) in the Indian automobile industry. It may be pointed out that the entry of global OEMs, consequent upon liberalisation and the policy of sourcing components locally, led to the creation of an ecosystem that embraced constant technological upgradations and adoption of modern management practices such as Lean Production, Just-in-Time, etc. The linkages developed between the global OEMs and their suppliers within the country, as a result of local sourcing, contributed to the spill-over of technological knowledge in favour of suppliers within the country. The consistent pressure from OEMs to deliver quality products at the lowest possible price at the appropriate time also compelled the firms in this industry to innovate (Singh et al., 2007). Moreover, the entry of global suppliers (directly or in partnership with local suppliers) into the Indian market forced the local suppliers to enhance their technological capabilities and products (Okada, 2004).

Contrary to the expectations, A&M intensity displayed a negative and significant influence on profitability in all the models, which is in line with the findings of Jaisinghani and Tondon (2016) for the Indian automobile industry. On the contrary, Jaisinghani et al. (2018) exhibited an insignificant relationship between A&M intensity and profitability. The possible explanation for the limited role played by A&M intensity might lie in the peculiar nature of this industry. Domestic sales of OEMs constitute 55.79 per cent of the overall production of the industry (ACMA, 2017). Therefore, gaining and retaining new OEMs is one of the primary strategic goals of the suppliers. OEMs also tend to engage their suppliers in design and development, which was not done earlier (Singh et al., 2007). All these factors contribute to the establishment of a long term relationship between OEMs and suppliers. As a result, the role of marketing is limited to the facilitation of the production as per the OEMs’ requirement and coordination with other departments in the event of any incidence of design flaws for their appropriate rectification.

Capital intensity has exhibited a negative and significant relationship with profitability in all the models. This result lends credence to similar findings of the earlier studies (Schoeffler et al., 1974; Beard and Dess, 1981; Capon et al., 1990; Chhibber and Majumdar, 1999; Bharadwaj et al., 1999; Kambhampati and Parikh, 2003; Jaisinghani et al., 2018). One plausible explanation for this negative relationship might rest with the result reported by Schoeffler et al. (1974) citing high sales volume as a probable reason for this inverse relationship. Even though the present study employs a panel dataset on single industry instead of cross-sectional data for several industries for a single year as done in a study conducted by Schoeffler et al. (1974), similar parallels can be drawn from Schoeffler et al. (1974) as the components industry’s output soared over the study period at a CAGR of 17.84 per cent which might have contributed to the negative relationship between capital intensity and profitability.

As far as firm’s leverage is concerned, it is negatively and significantly related to profitability in all the models I, II, III and IV which is in sync with the existing studies (Asimakopoulos et al. 2009, Chari and Banalieva 2015). The result appears to suggest that high debts may lead to lower profitability. Thus, firms with high debts in this industry may have to part with a large chunk of its profit towards debt servicing, adversely affecting their future investment capabilities and, as a result, growth potential in the long run.

The results are summed up in Table 7.

Table 7.

Summary and Interpretation of the Estimated Results

Independent Variables	Dependent Variables		Interpretations
Lagged dependent variable	ROA: Positive & significant	ROCE: Positive & significant	Moderate persistence of profitability
Current R&D intensity	ROA: Negative & insignificant	ROCE: Negative & significant	Drain on current profitability
Past R&D intensity	ROA: Positive & significant	ROCE: Positive & significant	Capabilities created through R&D expenditures enhance the firm’s profitability
A&M intensity	ROA: Negative & significant	ROCE: Negative & significant	Limited role of advertising and marketing in enhancing profitability
Capital intensity	ROA: Negative & significant	ROCE: Negative & significant	Drain on current profitability
Firm’s leverage	ROA: Negative & significant	ROCE: Negative & significant	High debts lead to lower profitability
Productivity growth	ROA: Positive & significant	ROCE: Positive & significant	Various in-house skill development programs have enhanced firm profitability
Export intensity	ROA: Positive & significant	ROCE: Positive & significant	Greater integration with global supply chain enhance firm profitability
Firm’s Size	ROA: Positive & significant	ROCE: Positive & significant	Bigger firms are more profitable
Firm’s Age	ROA: Insignificant	ROCE: Insignificant
GDP Growth	ROA: Positive & significant	ROCE: Positive & significant	Liberalisation has positively contributed to industry profitability
Output of OEMs	ROA: Negative & significant	ROCE: Negative & significant	Competition among the suppliers has negatively impacted industry profitability

Source: The authors.

The results reported in all the models reveal a positive relationship between labour productivity growth and profitability. However, only in model II and IV, labour productivity growth and profitability are both positively and significantly related which is in sync with the findings of Athanasoglou et al. (2008). Putting it differently, the various in-house skill development programs implemented by individual firms in this industry might have contributed to the improvement of labour productivity, which in turn had a positive bearing on profitability. The increased pressure from OEMs to improve product quality and timely delivery had created demands on suppliers to upgrade the skill-set of their employees which appeared to have led to the improvement in labour productivity (Okada, 2004).

Export intensity has exhibited a positive and significant relationship with profitability in all the models, which is in contrast with the results reported by Kumaraswamy et al. (2012). The difference could be imputed to the fact that both the studies belong to different time periods, i.e., just after liberalisation and post-liberalisation period which may be marred by the difference in the integration of Indian automobile industry with the global supply chain. It may be noted here that exports contributed 25.02 per cent of the turnover of this industry in the financial year 2016–2017 (ACMA, 2017). The upward export trend is an indication of India emerging as a manufacturing hub of automobile components (ACMA, 2017). The positive relationship between export intensity and profitability indicates that the growth in exports is positively influencing profitability and the industry is increasingly getting integrated with the global supply chain. The negative relationship is found between export intensity and profitability in Kumaraswamy et al. (2012) by the extension of this could be plausibly be attributed to the limited integration of Indian auto-component industry with the global supply chain during the initial years of liberalisation.

As expected, size exhibited a positive and significant influence on profitability in all the models indicating that bigger firms are fat more profitable. This result corroborates the findings of earlier studies (Asimakopoulos et al., 2009; Kumaraswamy et al., 2012; Yazdanfar, 2013; Chari and Banalieva, 2015; Nunes and Serrasqueiro, 2015). Age is not found to be significantly related to profitability in any of the models. GDP growth displayed a positive and significant influence on profitability in all the models, which are in contrast with the results found in Kumaraswamy et al. (2012). This finding of a positive relationship between GDP growth and profitability appears to be exciting, and the difference in the results of an earlier study (Kumaraswamy et al. 2012) and this study can plausibly be explained by the difference in the time periods when these two studies were undertaken.

Another interesting finding is that the output of OEMs has a negative and significant influence on profitability in all the models. It appears that the intensified competition among the suppliers to capture new OEMs have resulted in an adverse impact on the Components Industry’s profitability. It can be observed from Table 2 that majority of the sample firms targeted multiple OEMs. In this context, it may be noted here that the buyers-suppliers relationship has undergone a sea change over time. The entry of MUL has transformed the established ‘arm’s length’ buyerssuppliers relationship into a relationship based on mutual interactions to improve quality, streamline production, prompt delivery, and collaborative problem-solving. The entry of new players into the market in the postliberalisation period intensified the competition which compelled the components manufacturers to diversify their customer base. To realise the scale economies, the components manufacturers targeted multiple OEMs and increasingly got integrated with the global supply chain by exporting components to other countries as well as increased aftermarket sales. It can be observed from Table 2 that majority of the sample firms targeted multiple OEMs.

The results, as reported in this study, have some important implications for different stakeholders like managers, regulators, policymakers, etc. The finding that past R&D intensity has a positive influence on current profitability is significant to managers so that the managers can allocate appropriate resources to fund such projects without many apprehensions. The finding that labour productivity growth and profitability are positively related implies that managers could further focus on various in-house skill development programs to enhance labour productivity. The finding of a positive influence of exports on profitability indicates that managers could further explore the external markets to boost up profitability as export markets are reportedly far more rewarding.

The finding of moderate profit persistence has an important implication for regulators. The regulators should facilitate healthier competition among firms. The moderate profitability persistence implies that the regulators have managed to instil a reasonable level of competition in the industry through carefully crafted interventions, thereby facilitating its growth. The finding of the positive influence of GDP growth on profitability implies that the pro-market reforms initiated by the policymakers have positively contributed to the growth of this industry.

5. Conclusion

This paper examines the profit persistence and the relevance of RBV in enhancing profitability by developing capabilities that are not easily imitable in Indian automotive components industry. The results of the estimated models confirm the existence of profit persistence though it is found to be moderate. The study found various independent variables such as past R&D intensity, labour productivity growth, firm size, export intensity, and GDP growth to have positive and significant bearings on the current profitability. Some variables such as A&M intensity, capital intensity, firm’s leverage, and output of OEMs have been found to have exercised negative influence. How are these findings relevant to the industry? All the results indicate the fact that being innovative in terms of R&D and productivity growth and exploring diversified markets make a significant difference to profitability.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

References

ACMA (Automotive Component Manufacturers Association) (2017). Future of Mobility: Annual Report 2016–2017. ACMA, Delhi.

Arellano

, and Bond

(1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297.

Asimakopoulos

, Samitas

, and Papadogonas

(2009). Firm-specific and economy-wide determinants of firm profitability: Greek evidence using panel data. Managerial Finance, 35(11), 930–939.

Athanasoglou

P. P.

, Brissimis

S. N.

, and Delis

M. D.

(2008). Bank-specific, industry-specific and macroeconomic determinants of bank profitability. Journal of International Financial Markets, Institutions and Money, 18(2), 121–136.

Bain

J. S.

(1951). Relation of profit rate to industry concentration: American manufacturing, 1936–1940. The Quarterly Journal of Economics, 65(3), 293–324.

Barney

(1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.

Beard

D. W.

, and Dess

G. G.

(1981). Corporate-level strategy, business-level strategy, and firm performance. Academy of Management Journal, 24(4), 663–688.

Bharadwaj

A. S.

, Bharadwaj

S. G.

, and Konsynski

B. R.

(1999). Information technology effects on firm performance as measured by Tobin’s q. Management Science, 45(7), 1008–1024.

Blundell

, and Bond

(1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.

10.

Branch

(1974). Research and development activity and profitability: A distributed lag analysis. Journal of Political Economy, 82(5), 999–1011.

11.

Capon

, Farley

J. U.

, and Hoenig

(1990). Determinants of financial performance: a meta-analysis. Management Science, 36(10), 1143–1159.

12.

Caves

R. E.

, and Porter

M. E.

(1977). From entry barriers to mobility barriers: Conjectural decisions and contrived deterrence to new competition. The Quarterly Journal of Economics, 241–261.

13.

Chari

M. D.

, and Banalieva

E. R.

(2015). How do pro-market reforms impact firm profitability? The case of India under reform. Journal of World Business, 50(2), 357–367.

14.

Chhibber

P. K.

, and Majumdar

S. K.

(1999). Foreign ownership and profitability: Property rights, control, and the performance of firms in Indian industry. The Journal of Law and Economics, 42(1), 209–238.

15.

Collins

N. R.

, and Preston

L. E.

(1969). Price-cost margins and industry structure. The Review of Economics and Statistics, 271–286.

16.

Cool

, and Schendel

(1988). Performance differences among strategic group members. Strategic Management Journal, 9(3), 207–223.

17.

Djalilov

, and Piesse

(2016). Determinants of bank profitability in transition countries: What matters most? Research in International Business and Finance, 38, 69–82.

18.

Douma

, George

, and Kabir

(2006). Foreign and domestic ownership, business groups, and firm performance: Evidence from a large emerging market. Strategic Management Journal, 27(7), 637–657.

19.

Feeny

, Harris

M. N.

, and Rogers

(2005). A dynamic panel analysis of the profitability of Australian tax entities. Empirical Economics, 30(1), 209–233.

20.

Fiegenbaum

, and Thomas

(1990). Strategic groups and performance: The US insurance industry, 1970–1984. Strategic Management Journal, 11(3), 197–215.

21.

Frederick

Harris

deB.

(1986). Market structure and price-cost performance under endogenous profit risk. The Journal of Industrial Economics, 35–59.

22.

Galbreath

, and Galvin

(2008). Firm factors, industry structure and performance variation: New empirical evidence to a classic debate. Journal of Business Research, 61(2), 109–117.

23.

Geroski

P. A.

, and Jacquemin

(1988). The persistence of profits: A European comparison. The Economic Journal, 98(391), 375–389.

24.

Glen

, Lee

, and Singh

(2001). Persistence of profitability and competition in emerging markets. Economics Letters, 72(2), 247–253.

25.

Goddard

J. A.

, and Wilson

J. O.

(1996). Persistence of profits for UK manufacturing and service sector firms. Service Industries Journal, 16(2), 105–117.

26.

Goddard

, Tavakoli

, and Wilson

J. O.

(2005). Determinants of profitability in European manufacturing and services: Evidence from a dynamic panel model. Applied Financial Economics, 15(18), 1269–1282.

27.

Grabowski

H. G.

(1968). The Determinants of Industrial Research and Development: a study of the chemical, drug, and petroleum industries. Journal of Political Economy, 76(2), 292–306.

28.

Grazzi

(2012). Export and firm performance: Evidence on productivity and profitability of Italian companies. Journal of Industry, Competition and Trade, 12(4), 413–444.

29.

Hatten

K. J.

, and Schendel

D. E.

(1977). Heterogeneity within an industry: Firm conduct in the US brewing industry, 1952–1971. The Journal of Industrial Economics, 97–113.

30.

Jaisinghani

and Tondon

(2016). R&D, Profit persistence impact on profitability of the Indian automobile sector (Presented at 12th Asia-Pacific Economic Association conference, Kolkata, 2016). APEA. http://www.apeaweb.org/confer/kkt16/papers/Tandon_Deepak.pdf

31.

Jaisinghani

, Tandon

, and Batra

D. K.

(2018). Capital expenditure and persistence of firm performance: An empirical study for the Indian automobiles industry. International Journal of Indian Culture and Business Management, 16(1), 39–56.

32.

Jefferson

G. H.

, Huamao

, Xiaojing

, and Xiaoyun

(2006). R&D performance in Chinese industry. Economics of Innovation and New Technology, 15(4–5), 345–366.

33.

Johnson

, and Thomas

(1987). The industry context of strategy, structure and performance: The UK brewing industry. Strategic Management Journal, 8(4), 343–361.

34.

Kambhampati

U. S.

, and Parikh

(2003). Disciplining firms: the impact of trade reforms on profit margins in Indian industry. Applied Economics, 35(4), 461–470.

35.

Khanna

, and Palepu

(1997). Why focused strategies may be wrong for emerging markets. Harvard Business Review, 75(4), 41–43.

36.

Khanna

, and Palepu

(2000). Is group affiliation profitable in emerging markets? An analysis of diversified Indian business groups. The Journal of Finance, 55(2), 867–891.

37.

Khanna

, and Rivkin

J. W.

(2001). Estimating the performance effects of business groups in emerging markets. Strategic Management Journal, 22(1), 45–74.

38.

Kumaraswamy

, Mudambi

, Saranga

, and Tripathy

(2012). Catch-up strategies in the Indian auto components industry: Domestic firms’ responses to market liberalisation. Journal of International Business Studies, 43(4), 368–395.

39.

Mahmood

I. P.

, and Mitchell

(2004). Two faces: Effects of business groups on innovation in emerging economies. Management Science, 50(10), 1348–1365.

40.

Majumdar

S. K.

(1997). The impact of size and age on firm-level performance: some evidence from India. Review of Industrial Organization, 12(2), 231–241.

41.

Mann

H. M.

(1966). Seller concentration, barriers to entry, and rates of return in thirty industries, 1950–1960. The Review of Economics and Statistics, 296–307.

42.

Mascarenhas

, and Aaker

D. A.

(1989). Mobility barriers and strategic groups. Strategic Management Journal, 10(5), 475–485.

43.

Mauri

A. J.

, and Michaels

M. P.

(1998). Firm and industry effects within strategic management: An empirical examination. Strategic Management Journal, 19(3), 211–219.

44.

McGahan

A. M.

, and Porter

M. E.

(1997). How much does industry matter, really? Strategic Management Journal, 18(S1), 15–30.

45.

Miller

R. A.

(1969). Market structure and industrial performance: Relation of profit rates to concentration, advertising intensity, and diversity. The Journal of Industrial Economics, 104–118.

46.

Mistry

D. S.

(2012). Determinants of profitability in Indian automotive industry. Tecnia Journal of Management Studies, 7(1), 20–23.

47.

Mueller

D. C.

(1977). The persistence of profits above the norm. Economica, 44(176), 369–380.

48.

Nunes

P. M.

, and Serrasqueiro

(2015). Profitability determinants of Portuguese knowledge-intensive business services: Empirical evidence using panel data models. Applied Economics Letters, 22(1), 51–56.

49.

Nunes

P. M.

, Viveiros

, and Serrasqueiro

(2012). Are the determinants of young SME profitability different? Empirical Evidence using dynamic estimators. Journal of Business Economics and Management, 13(3), 443–470.

50.

Okada

(2004). Skills development and interfirm learning linkages under globalisation: Lessons from the Indian automobile industry. World Development, 32(7), 1265–1288.

51.

Peteraf

M. A.

(1993). The cornerstones of competitive advantage: a resource-based view. Strategic Management Journal, 14(3), 179–191.

52.

Porter

M. E.

(1985). Competitive advantage: creating and sustaining superior performance. The Free Press.

53.

Roodman

(2009). How to do xtabond2: An introduction to difference and system GMM in Stata. The Stata Journal, 9(1), 86–136.

54.

Roquebert

J. A.

, Phillips

R. L.

, and Westfall

P. A.

(1996). Markets Vs. management: What ‘drives’ profitability? Strategic Management Journal, 17(8), 653–664.

55.

Rumelt

R. P.

(1991). How much does industry matter? Strategic Management Journal, 12(3), 167–185.

56.

Saranga

(2009). The Indian auto component industry–Estimation of operational efficiency and its determinants using DEA. European Journal of Operational Research, 196(2), 707–718.

57.

Scherer

F. M.

(1965). Corporate inventive output, profits, and growth. Journal of Political Economy, 73(3), 290–297.

58.

Schmalensee

(1985). Do markets differ much?. The American Economic Review, 75(3), 341–351.

59.

Schoeffler

, Buzzell

R. D.

, and Heany

D. F.

(1974). Impact of strategic planning on profit performance. Harvard Business Review, 52(2).

60.

Singh

R. K.

, Garg

S. K.

, and Deshmukh

S. G.

(2007). Strategy development for competitiveness: a study on Indian auto component sector. International Journal of Productivity and Performance Management, 56(4), 285–304.

61.

Tyagi

, and Nauriyal

D. K.

(2017). Firm level profitability determinants in Indian drugs and pharmaceutical industry. International Journal of Pharmaceutical and Healthcare Marketing, 11(3), 271–290.

62.

Venkatraman

, & Ramanujam

(1986). Measurement of business performance in strategy research: A comparison of approaches. Academy of management review, 11(4), 801–814.

63.

, Holmes

, Lim

, and Tran

(2014). Exports and profitability: A note from quantile regression approach. Applied Economics Letters, 21(6), 442–445.

64.

Wagner

(2012). Exports, imports and profitability: First evidence for manufacturing enterprises. Open Economies Review, 23(5), 747–765.

65.

Weiss

L. W.

(1963). Average concentration ratios and industrial performance. The Journal of Industrial Economics, 237–254.

66.

Wernerfelt

, and Montgomery

C. A.

(1988). Tobin’s q and the importance of focus in firm performance. The American Economic Review, 246–250.

67.

Yazdanfar

(2013). Profitability determinants among micro firms: evidence from Swedish data. International Journal of Managerial Finance, 9(2), 151–160.

Automotive Components Industry and Profitability Factors: Evidence from India

Abstract

Keywords

1. Introduction

2. Review of Literature

3. Methodology

Data and Sample

Table 1. Classification of the Indian Automotive Components Industry Total Firms Sample Firms Batteries 29 3 Tyres and tyre products 46 7 Other Ancillaries 890 68

Measures

Dependent Variable

Independent Variables

Estimation Method

Footnotes

Declaration of Conflicting Interests

Funding

References

Table 1.
Classification of the Indian Automotive Components Industry

Total Firms Sample Firms

Batteries 29 3

Tyres and tyre products 46 7

Other Ancillaries 890 68