Innovation is the corner stone for any economic success and hence the primary theme in economic research. The meaning of the word innovation has considerably altered, in the recent years; this word was originally linked to R& D, whereas currently it is linked to knowledge, used in the process of creating good ideas. Greer et al state that collaborative innovation with customers or users is increasingly important, for the development of new products and services.
Jiang et al presented a study on the electronic customer relationship management (e-CRM) process of collaborative electronic business (e-business), in the service industry. It found that the creation of e-business capability and information technology (IT) knowledge integration capability is based on the integration of IT resources. Moreover, this integration provides a mediation of collaborative e-business capability.
There is now a good amount of literature that supports the idea that innovation results are favoured by the presence of relationships, networks, alliances and different other forms of interaction, with external sources of knowledge (among others, Powell and Grodal 2005; Tether 2002).
At the same time, ‘open innovation’ has become one of the topics that is most debated by management scholars (Chesbrough 2003; Chesbrough, Vanhaverbeke, and West2006; Dahlander and Gann 2010; Huizingh 2011; Lichtenthaler 2011). The use of external relationships is increasingly interpreted, as the key factor in enhancing the innovation performance of modern enterprises. Several works confirm that network ties can be valuable tools for fostering innovation performance (e.g. Chen, Chen and Vanhaver-beke 2011; Freeman 1991; Love and Roper 2001; Nieto and Santamaria 2007; Rammer, Czarnitzki and Spielkamp 2009; Rogers 2004; Zeng, Xie and Tam 2010). Indeed, these knowledge links can offer firms easier access to new ideas and can enhance the transfer of knowledge, from the university and research units to business activities. Apart from this, increasing attention to the approaches to innovation, technological product development and market transformations have made competition everywhere increasingly stiff; and small and medium enterprises (SMEs) have come to depend increasingly on innovation, sometimes for their survival (Verhees and Meulenberg, 2004).
It is thus important that SMEs should achieve a competitive advantage, by increasingly improving their products and services, more especially because of globalisation.
Role of Small and Medium Enterprises (SMEs) in India
Today, Micro, Small and Medium Enterprises (MSMEs) and startups are viewed as major engines of job creation and inclusive growth, both in the developed and developing countries. In 2010- 11, MSMEs represented 45% of the manufactured output and 40% of exports*. India is also estimated to have 5,000 regional MSMEclusters**, comprising industrial, handicraft and handloom clusters, such as the gems cluster in Surat , the brassware cluster in Moradabad and the textile cluster at Tirupur. Innovation can play a pivotal role in driving growth in MSME clusters, by creating new products, services and business models.
However, innovation in MSME clusters in India suffers from lack of access to technology, R&D, financing skills, mentors and effective collaborative ecosystems, which in turn has an impact on their growth and productivity. In this context, the National Innovation Council (NInC) aims to create models, for transforming regional MSME clusters into innovation ecosystems, with collaborative partnerships among stakeholders.
(* MSME Annual Report 2010-11, Ministry of MSME, Government of India.)
SMEs have been playing a pivotal role in the overall economic growth of the country and have achieved steady progress, over the last couple of years. From the perspective of industrial development in India, and hence the overall growth of the economy, SMEs have to play a prominent role, given their labour intensiveness, which generates employment. The SME segment also plays a major role, in the developing countries, such as India, in an effort to alleviate poverty and propel sustainable growth. They also lead to equitable distribution of income, due to their nature of business. Moreover, SMEs, in countries, such as India, help in efficient allocation of resources, by implementing labour intensive production processes, given the abundant supply of labour in these countries, wherein capital is scarce.
The enactment of the Micro, Small and Medium Enterprises Development (MSMED) Act, 2006 was a landmark initiative taken by the Government of India, to enhance the SMEs’ competitive strength, address the issues and challenges and reap the benefits of the global market. SME policy initiatives, at the national and state level, are aimed at strengthening the role of SMEs, at the base as well as at the higher level.
The Tamil Nadu Government, by formulating an exclusive policy for the micro, small and medium enterprises sector, to encourage agro-based industries, is a recent example of the changes, taking place at the ground level. The policy offers a range of incentives and support for infrastructure development, subsidies for investment in industrially backward areas, capital investment and technology development, aiming to sustain over 10% growth of the MSME segment, in the food and agro sector. Some of the salient features of this policy include formation of multi-storied and flatted industrial estates, for micro industries, a liberal floor space index, in the plotted development of 1.5 to 1.75, for industrial sheds and 2.5 for multi-storied industrial units and 50% rebate on stamp duty and registration charges for micro and small enterprises in industrial estates and industrially backward areas. With globalisation, all forms of production of goods and services are getting increasingly fragmented, across countries and enterprises. With large players adopting different models of business that include involvement of their traditional partners, suppliers or distributors, at a different level, SMEs now are now experiencing a new model of functioning, in the value chain. The past few years has seen the role of the SME segment evolve, from a traditional manufacturer in the domestic market to that of an international partner.
The restructuring of production at the international level, through increased outsourcing, is creating a significant effect on small and medium entrepreneurs, in a positive as well as negative manner. Demand, in terms of new niche products and services, is providing more opportunities for SMEs that are in a better position to take advantage of their flexible nature of operations. However, at the same time, they have realised their drawbacks in terms of inadequate availability of managerial and financial resources, lack of working capital, personnel training and inability to innovate at a faster pace.
The combined effect of market liberalisation and deregulation has forced the SME segment, to change their business strategies, for their survival and growth. Some of the changes that SMEs are focusing on include acquiring quality certifications, increasing use of ICT, creating e-business models and diversification, to meet the increasing competition. Globalisation, economic liberalisation and the WTO regime would undoubtedly open up a unique opportunity, for the largest business community, i.e. SMEs, through effective involvement in international trade, by streamlining certain factors, such as access to markets, access to technology, access to skills, finance, development of necessary infrastructure, an SME-tax friendly environment and implementation of best practices, to name a few.
Production and Investment in SMEs
|
No of SME Units ( In Mn) |
|||
|
|
Registered |
Unregistered |
Total |
|
FY03 |
1.6 |
9.3 |
10.9 |
|
FY04 |
1.7 |
9.7 |
11.4 |
|
FY05 |
1.8 |
10.0 |
11.9 |
|
FY06 |
1.9 |
10.4 |
12.3 |
|
FY07 |
2.0 |
10.8 |
12.8 |
Total Production of MSMEs and Total Employment Generation
|
FY03 |
26.37 |
116 |
|
FY04 |
27.53 |
122 |
|
FY05 |
28.76 |
130 |
|
FY06 |
30.0 |
140 |
|
FY07P |
31.25 |
151 |
The total production of SMEs has shown a phenomenal growth, in FY07, as compared to the previous year. The production, at current prices, has experienced a growth rate of around 18%, as against 15.8% in the previous year, thereby raising its share in India’s GDP to15.5%, during the year. Economic activities, such as export market, growing domestic consumption, conducive policy measures, improving production methods, technology and development of SME clusters have fuelled production and hence their share in India’s GDP. SMEs have maintained an equal growth rate, vis-à-vis the overall industrial sector, during FY03-07, which grew at a CAGR of around 17%. The SME sector has also registered a consistently higher growth rate than the overall manufacturing sector. In fact, it plays a dual role, since the output produced by SMEs is not only about final consumption, but is also a source of capital goods, in the form of inputs to heavy industries. The table below indicates the growing significance of SMEs, in the Indian economy. Not only is the output of SMEs increasing, but the productivity in terms of per unit is also growing, at a higher rate, in the last four years. The relative advantage of SMEs is well recognised, by the resurgence of the manufacturing sector in India, during the last two fiscals and is poised for a higher growth in this fiscal, thus denoting the importance of SMEs and the need to sustain them, for a long time.
Share of SMEs Output in India’s GDP
|
Total No. of Units in mn |
Per Unit Production (` 000) |
SME Production % Share to GDP at the Current Prices |
Year |
|
6.8 |
116.1 |
15.3 |
FY 91 |
|
8.3 |
178.4 |
13.6 |
FY 96 |
|
9.7 |
240.5 |
13.1 |
FY 00 |
|
10.1 |
258.5 |
13.6 |
FY 01 |
|
10.5 |
268.3 |
13.5 |
FY 02 |
|
11.0 |
287.5 |
13.9 |
FY 03 |
|
11.4 |
319.8 |
14.4 |
FY 04 |
|
11.9 |
362.4 |
14.9 |
FY 05 |
|
12.3 |
403.4 |
15.2 |
FY 06 |
|
12.8 |
457.3 |
15.5 |
FY 07 |
The table below suggests that for every ten million rupees, invested by the SMEs, they have generated more than 4 times the employment opportunities in the context of the overall economy, as per the data available in FY06. For instance, every ten million rupees, invested by SMEs, in FY06, generated employment for around 151.4 persons, whereas for the overall economy the same amount of investment generated employment for 37.4 persons.
|
|
Investment to Production Ratio |
Employment per ` mn of Investment in SMES |
Investment to Output Ratio |
Employment per ` 10 mn Investment in the Economy |
|
FY02 |
0.57 |
157.0 |
0.26 |
76.5 |
|
FY03 |
0.54 |
155.5 |
0.26 |
70.8 |
|
FY04 |
0.49 |
154.4 |
0.27 |
58.9 |
|
FY05 |
0.44 |
152.3 |
0.31 |
44.0 |
|
FY06 |
0.40 |
151.4 |
0.34 |
37.4 |
|
FY07P |
0.35 |
150.7 |
0.36 |
NA |
SME exports growing in tandem with total exports
SMEs constitute an important segment of India’s industrial production, with a contribution to 33% of its exports. During FY03- 06, India’s total merchandise exports, in terms of US dollars, witnessed a CAGR growth of 25%, while during the same period, SME exports grew at a CAGR of 24%. The remarkable contribution of SMEs, in generating employment, in the country, has been instrumental in addressing issues, pertaining to poverty and inequality of income. As per the Third All India Census on Small Scale Industries - 2001-02, highly populated states, such as Madhya Pradesh, Uttar Pradesh, West Bengal, Maharashtra, Karnataka and Jharkhand, together contributed to around 55.4% of the total exporting units in India. In terms of distribution of value of exports, from the SME sector, states like Punjab, Haryana, Uttar Pradesh, Tamil Nadu and Maharashtra together contributed 64.75% of the total exports.
The composition of the export basket of SME’s in India, it has both traditional and non-traditional commodities. There are few commodity groups, which are exclusively exported by SMEs, such as sports goods, cashew, lac etc. In the commodity group of engineering goods, SMEs constitute around 40% of the total exports of this commodity group. Similarly, SMEs in basic chemicals and pharmaceuticals, finished leather and leather products and marine products account for around 44%, 69% and 50% of the export share, in their respective commodity groups. In view of the Government of India’s ambitious target of an average GDP growth rate of 9%, during the 11th Five Year Plan, SMEs have to play a vital role in achieving this target. It is imperative for the government, to address the major issues plaguing the sector and to take further inclusive growth oriented policy initiatives, to boost the sector. This includes measures, addressing concerns of credit, fiscal support, cluster-based development, infrastructure, technology and marketing, among others. As mentioned earlier, SMEs constitute 34% of India’s merchandise exports and in order to increase India’s export share in global trade, SMEs are expected to enlarge their scope manifold.
The Government of India as well as many state governments have created various incubation centres, product R& D centres, institutions such as IIP (the Indian Institute of Packaging), which engages in extensive research on types of packaging, required for modern days businesses and alterative low cost raw material that can be used for packaging, as also institutions such as NABARD, KVIC etc., which have their own programmes, to develop MSMEs. But the real question that arises is how much have these SMEs been able to take advantage of such government initiatives?
This paper investigates the role of external relationships, as key drivers of small business innovation. The results of an empirical analysis, based on data for approximately 50 small and medium- sized enterprises (SMEs) in Mumbai, indicate that innovation performance is higher in SMEs, which are proactive in strengthening their relationships with innovative suppliers, users and customers and with external agencies such as research laboratories (both private and government). Furthermore, the findings of this paper support the view that SMEs will have better product development results, for new products, if they improve their relationships with External Agents.
The main focus of this paper is on innovation performance in SMEs. Two principal research questions are addressed through empirical analysis. First, this study asks whether SMEs, which are proactive in strengthening their relationships with innovative suppliers and customers, are more likely to achieve positive results, in the innovation of products or services.
The second question is whether innovative SMEs are more likely to innovative than other SMEs, to take advantage of linkages with R&D laboratories. The motivation, behind the choice of focusing on these types of linkages, can be explained as follows: first, the basic idea of collaborative business is that suppliers (together with clients or customers) are the most sought-after innovation partners. Rinaldi et al (2010), in their article, focus on the importance and potential benefits of collaboration, in business enterprises, as game changers.
It is more likely for firms to source external knowledge, along the supply chain, because competences have probably to be complementary. Kitson et al (2004), in their article, explore the UK Government's response, in terms of funding university-business collaborations as also the breadth of services that are offered by universities. A case study examines the collaboration between ten universities and colleges, in the East of England, designed to help business- university collaboration. Three case studies follow, examining the broad range of work of their member institutions, in helping different growing businesses, to showcase the wide variety of different services, offered by the university sector. Large firms report more cooperation with higher education or government institutions than SMEs. Thus, it is important to understand, whether SMEs that are not engaged in partnerships, with universities or other scientific research units, are also associated with a lower number of innovative products.
Even basic things like packaging and the expertise provided by IIP (the Indian Institute of Packaging) are not used by SMEs; such SMEs thus find it increasingly difficult to export products to foreign markets. Almost all governments are trying to protect their domestic producers by trying to provide increasing support to their SMEs, to protect domestic jobs. The innovation index also shows that India is at bottom of index, let alone in the context of SMEs.
The empirical support used to answer these two questions is based on a large sample of approximately 50 SMEs located in India. To control the different dimensions of innovation, managers were asked to indicate the degree of novelty (in the range of products or in the contribution to turnover) that their SMEs have achieved over the past two years.
Studies focusing on external sources of knowledge as ‘innovation gateways’ for SMEs are relatively scarce.
The following section reviews the literature on open innovation, networking and external relationships, and illustrates the research hypotheses. The data and methodology section describes the data and the empirical methodology. The empirical results section provides the findings, whereas the discussion section contains the discussion of results. The final section formulates the concluding remarks.
Objectives
To find out whether
Data and Methodology
The data was collected through a survey of business owners and managers, who were located in various cities, across India. SMEs were identified and professionals interviewed, with the help of facilitators. The practices included in the survey were identified from the literature and from discussion with industry professionals.
All respondents were asked to use a 5 point Likert scale (1= lowest relevance of the option and 5= highest relevance of the option), to measure the position of the firm, with respect to different managerial practices. Almost all the respondents answered all the questions.
Dependent Variable: Innovation Performance.
Reliability
Scale: ALL VARIABLES
Case Processing Summary
|
|
N |
% |
|
|
Cases |
Valid |
20 |
100.0 |
|
Excluded |
0 |
0 |
|
|
Total |
20 |
100.0 |
|
Reliability Statistics
|
Cronbach’s Alpha |
Cronbach’s Alpha Based on Standardised Items |
No. of Items |
|
0.612 |
0.614 |
7 |
Inter-Item Correlation Matrix
|
|
VAR00002 |
VAR00003 |
VAR00004 |
VAR00005 |
VAR00006 |
VAR00010 |
VAR00008 |
|
VAR00002 |
1.000 |
.335 |
.413 |
-.030 |
-.031 |
.248 |
.465 |
|
VAR00003 |
.335 |
1.000 |
-.089 |
.058 |
.288 |
.110 |
.384 |
|
VAR00004 |
.413 |
-.089 |
1.000 |
.355 |
.130 |
.354 |
.088 |
|
VAR00005 |
-.030 |
.058 |
.355 |
1.000 |
.731 |
.056 |
-.200 |
|
VAR00006 |
-.031 |
.288 |
.130 |
.731 |
1.000 |
-.022 |
-.160 |
|
VAR00010 |
.248 |
.110 |
.354 |
.056 |
-.022 |
1.000 |
.404 |
|
VAR00008 |
.465 |
.384 |
.088 |
-.200 |
-.160 |
.404 |
1.000 |
Item-Total Statistics
|
|
Scale Mean, if Item Deleted |
Scale Variance, if Item Deleted |
Corrected Item-Total Correlation |
Squared Multiple Correlation |
Cronbach's Alpha, if Item Deleted |
|
VAR00002 |
17.8000 |
20.063 |
.397 |
.428 |
.553 |
|
VAR00003 |
18.9000 |
21.358 |
.291 |
.376 |
.586 |
|
VAR00004 |
18.7500 |
18.724 |
.391 |
.444 |
.550 |
|
VAR00005 |
19.3500 |
19.503 |
.324 |
.628 |
.576 |
|
VAR00006 |
19.8500 |
20.134 |
.302 |
.618 |
.583 |
|
VAR00010 |
18.6000 |
19.832 |
.326 |
.271 |
.575 |
|
VAR00008 |
17.5500 |
21.945 |
.254 |
.422 |
.596 |
Scale Statistics
|
Mean |
Variance |
Std. Deviation |
No. of Items |
|
21.8000 |
25.853 |
5.08 45 5 |
7 |
In this scale, the Reliability of scales, seven items i.e. variables 2, 3, 4, 5, 6, 10, 8, has been examined. What has been found is that the scale reliability is 0.614 (Cronbacha’s Alpha on Standardised Items), which is acceptable. If the item to the total correlation is examined, it can be observed that Variable 8 has the least value of .254, which can be considered for being dropped from the scale, if the item has to be reduced. The Alpha value of the scale, if this item is dropped is reduced to 0.596. But, in any case, it was decided that item should not be dropped, from the scale, because the researcher felt that it was an important variable.
Independent Variables: Key independent variables, in this study,
evaluate whether the firms have a strategy, for outsider - in the process of interacting with suppliers, customers and other research partners, such as Government Research Laboratories and Outside Research Laboratories, etc. The respondents were asked to offer their responses, to evaluate their customers and suppliers, in terms of their innovative collaborative experiences. Similarly, to evaluate their linkages, with Universities and other Scientific Institutes, managers were asked, if their relationships were active and ongoing. To test the empirical relevance of H1 (The role of knowledge relationships with customers and suppliers), four variables were taken into account:
As far as Objective 2 (The relationship with external research agents is considered, two questions were asked, i.e. questions 5 and 6. If the respondents provided a score of 1, it indicated no existing linkages, whereas a score of 5 meant close existing linkages with institutes.
Cluster Analysis was carried out in 2 stages:
In stage 1, Hierarchical Cluster Analysis was used, to identify how many clusters could be formed. Once the numbers of clusters that could be formed was identified, K-Means Cluster Analysis was carried out, to create clusters of companies, in stage 2.
Agglomeration Schedule (Hierarchical Cluster Analysis)
|
Stage |
Cluster Combined |
Coefficients |
Stage Cluster First Appears |
Next Stage |
||
|
Cluster 1 |
Cluster 2 |
Cluster 1 |
Cluster 2 |
|||
|
1 |
5 |
17 |
2.000 |
0 |
0 |
8 |
|
2 |
15 |
18 |
6.000 |
0 |
0 |
5 |
|
3 |
13 |
14 |
7.000 |
0 |
0 |
6 |
|
4 |
7 |
21 |
8.000 |
0 |
0 |
8 |
|
5 |
8 |
15 |
9.000 |
0 |
2 |
9 |
|
6 |
11 |
13 |
10.500 |
0 |
3 |
9 |
|
7 |
4 |
10 |
13.000 |
0 |
0 |
11 |
|
8 |
5 |
7 |
13.000 |
1 |
4 |
13 |
|
9 |
8 |
11 |
14.000 |
5 |
6 |
12 |
|
10 |
2 |
6 |
15.000 |
0 |
0 |
14 |
|
11 |
4 |
19 |
16.500 |
7 |
0 |
18 |
|
12 |
8 |
12 |
20.333 |
9 |
0 |
16 |
|
13 |
3 |
5 |
21.000 |
0 |
8 |
19 |
|
14 |
2 |
16 |
21.500 |
10 |
0 |
17 |
|
15 |
1 |
9 |
26.000 |
0 |
0 |
20 |
|
16 |
8 |
20 |
26.286 |
12 |
0 |
17 |
|
17 |
2 |
8 |
30.333 |
14 |
16 |
18 |
|
18 |
2 |
4 |
30.394 |
17 |
11 |
19 |
|
19 |
2 |
3 |
33.357 |
18 |
13 |
20 |
|
20 |
1 |
2 |
52.947 |
15 |
19 |
0 |
Analysis of Output
Cluster Analysis was used to identify, which companies are similar to each other. Cluster Analysis is a multivariate technique, which is used to club similar people or companies together, on the basis of some variables.
Hierarchical Cluster Analysis was carried out, to identify how many clusters could be formed. From the table, showing the Agglomeration Schedule, it can be identified that the difference between case 20 and case 19 shows maximum difference in their coefficients, i.e. ( 52.947-33.54= 22) , but this will indicate a One Cluster solution, since it is desired that groups of companies with similar characteristics should be created, for this at least a 2 cluster solution would be needed ,when examining the stage 17-16, which has a coefficient - 30.333-26.286. This results in a difference of 4.047, which can be considered as a sudden good jump; this is a 3 cluster solution, which should prove valuable for this research.
On carrying out further analysis in stage 2 i.e. K- Means Cluster, several outputs can be seen.
Output 1
Number of Cases in Each Cluster
|
|
1 |
2.000 |
|
|
12.000 |
|||
|
Cluster |
2 |
||
|
|
3 |
||
|
7.000 |
|||
|
Valid |
21.000 |
||
|
Missing |
0.000 |
||
From the above output, it can be seen that out of 21 cases 2 cases belong to cluster 1, while 12 cases belong to cluster 2 and 7 to cluster 3.
The table given below analyses all the three clusters
Initial Cluster Centres
|
|
Cluster |
||
|
1 |
2 |
3 |
|
|
VAR00001 |
2.00 |
4.00 |
4.00 |
|
VAR00002 |
2.00 |
1.00 |
5.00 |
|
VAR00003 |
2.00 |
2.00 |
5.00 |
|
VAR00004 |
3.00 |
1.00 |
5.00 |
|
VAR00005 |
4.00 |
1.00 |
5.00 |
|
VAR00006 |
4.00 |
1.00 |
5.00 |
|
VAR00007 |
2.00 |
5.00 |
5.00 |
|
VAR00008 |
1.00 |
5.00 |
5.00 |
|
VAR00009 |
1.00 |
3.00 |
4.00 |
|
VAR00010 |
1.00 |
4.00 |
5.00 |
Report on Initial Cluster Centres
Variable 10 in the questionnaire analysed if the company had increased its range of products, by at least 10 %, very 2 years.
From the above table, it is clear that Cluster 1 has the value of 1 on statement 10 and it has a low value on almost all statements, which pertain to maintaining strategic relations with key suppliers, whereas Clusters 2 and 3 have values of 4 and 5 respectively, which indicates that only companies belonging to Clusters 2 and 3 have increased their range of products by 20 %, every 2 years, while companies belonging to Cluster 3 have a maximum of 5 on this statement. Further, it can be seen that companies belonging to Cluster 3 have a value of 5 on the following statements: we maintain a strategic relationship with our suppliers, we maintain a strategic relationship with key machinery suppliers, we maintain a strategic relationship with key IT suppliers , we maintain a strategic relationship with key government agencies and laboratories for innovation suppliers, we maintain a strategic relationship with Independent Research Labs for innovations, we consider it important to register patents and trademarks, important to train and develop the employees and 4 on the statement that their turnover had increased, across geographical areas, with addition of these new products.
Conclusion
The companies in Cluster 3 have been the most innovative companies and have been able to launch new products much faster and achieve more profits, by increasing their geographic coverage, by using their strategic ties with Key Partners and their strategic relations with the Government and other independent Research Agencies.
Managerial Implications
Limitations:
Random sampling technique.
References
Authored by
Prof. Sameer A. Virani
India is home to diverse society with multiple regional languages; hence it is a challenge for retailers to have pan-India reach without making a huge investment. However the latest trends depict that consumers’ buying behavior is shifting towards adopting new methods of buying.
Reports from “Branding Brand” show there is huge sales from smartphones & tablets. In July 2014, 49.6% of all traffic was to a group of retail clients towards mobile devices, 34.9% from smartphones and 14.7% from tablets. Chris Masonco, founder and CEO, “Branding Brand” says that the data shows decisively that smartphone activity in retail is surging, with the highest percentage of consistently growth.
According to a report from “Avendus Capital”, Mumbai based financial advisory firm, 50% of internet users in India are ‘mobile only’, while over 60% of revenues come from Tier II / III cities for dominant players. These consumers are mostly shopping through their mobile phones, which is their only access to internet. Tracking this shift, the online players are changing focus from saturated urban hubs to Tier II / III cities. There is a growing trend in India with respect to offline retailers going online, with some significant brands like Bombay Store, Meena Bazaar, Future Retail, The Mobile Store, Shoppers Stop, to name a few. Hence e-tailing is not only beneficial for the retail giants but also for the small or new players. Small players have access to the gigantic e-commerce sites with fast and convenient operations, with minimum investment, few or no cliental data and minimal of warehousing facility.
Mobile phones today are the most crucial device for reaching the prospective clients, which allow internet access at very affordable cost. It is easier for them to blend in a language that they are comfortable in. Nevertheless, language continues to be the biggest hurdle for e-tailing in order to access the markets in the vicinity. Now the objective is to develop personalized mobile app to deliver content in local languages for establishing better connection with the consumers in smaller cities.
Hence keeping in mind the multiple regional languages, it is imperative in reaching the prospective buyers and retaining them. To be more competitive, e-commerce players have to ensure that consumers do not look somewhere elsewhere. However to take e-tailing to the next level there are some applications like LinguaNext, Langify, Magento – Connect, etc. which deliver content in multiple regional languages.
Authored by:
Prof. Sana Khan
©Mumbai Educational Trust, All rights reserved | Last Modified Date: Wednesday, April 24th, 2024