DIGITAL MARKETING AND SMES: AN IDENTIFICATION OF RESEARCH GAP VIA ARCHIVES OF PAST RESEARCH
Bibliographic record
Abstract
As per the report of Boston Consulting (BCG) and internet mobile association of India (IAMAI) 2015, the internet population increased 25 times in the last 12 years. The reinvention of marketing requires a re-examination of existing techniques and practices to assure their appropriateness for changing global digital environment. The need to carry out research on usage of digital marketing in the Small and medium enterprises is utmost importance. Objective: The main objective of this research is to develop a clear understanding about existing research related to SMEs and digital marketing. This paper aims to empirically explore all the different research points related to studies of digital marketing published between 2005 and 2016 and explore different methodologies adopted by researchers in the field of SMEs. Approach: For analyzing, review of the literature concerning the usage of digital marketing in SMEs, the author used systematic analysis and classifies the published data of marketing journals, economic, business and management journals and IT journals along with online accessible newspapers and reports. In this paper, author proposes a model of DOI (Diffusion of Innovation), Everett Rogers’ five dimensional model, for understanding the studies on Indian SMEs’ decisions to use digital marketing. Results: The result indicated lack of structured research studies in order to use digital marketing in small businesses in India. The study covers many related areas such as: Electronic commerce, electronic platforms, Mobile marketing, E-marketing and many other research areas. There are clear research gap in field of digital marketing, to fill the same, there is need to conduct research to investigate the opportunities created by digital marketing for Indian SMEs. The use of different methods in reviewing the similar phenomenon should lead to greater validity and reliability than single method. Authors’ recommends that triangulation approach will help in answering future researcher’s questions and filling the research gap in this field. Implications: This paper may give clearer view towards the published work in the field of digital marketing. Beneficiaries of this research may be industrialists, policy maker, practitioners, researchers and academicians. Originality/Value: The outcomes of the study illustrate the research gap between digital marketing and its usage in SMEs. It may also facilitate better approach towards accumulated knowledge in the field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".