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Record W2582663546 · doi:10.1108/jcc-08-2016-0009

Product innovation, cost-cutting and firm economic performance in the post-crisis context: Canadian micro evidence

2016· article· en· W2582663546 on OpenAlexaffabout
Zhan Su, Jianmin Tang

Bibliographic record

VenueJournal of CENTRUM Cathedra (JCC) The Business and Economics Research Journal · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsInnovation, Science and Economic Development CanadaUniversité Laval
Fundersnot available
KeywordsProfitability indexIndustrial organizationContext (archaeology)BusinessSample (material)MarketingProduct innovationProduct (mathematics)Propensity score matchingEconomicsFinance

Abstract

fetched live from OpenAlex

Purpose It has been suggested that to be successful in the current global economy with increased competition and ever changing markets, especially in the post-crisis context, firms need to focus more on innovation in exploring new ideas and designing new products to develop new markets than on cost-cutting strategies to maintain cost leadership in old markets. However, because of the lack of micro data, this conjecture has not been systematically evaluated. This paper aims to fill this important void by studying the economic performance associated with these two different business strategies using Canadian micro data. Design/methodology/approach The main data for our analysis are from the Survey of Innovation and Business Strategy (2009 and 2012) which is a sample-based survey of Canadian government. The authors used in this research regression models for the econometric analysis of the underlying factors for undertaking certain business strategies and how business strategies link to economic performance. They also used propensity score matching to ensure the group of firms with innovation strategy being comparable to that with cost-cutting. Findings The research shows that firms focusing on product innovation are indeed more productive than firms focusing on cost-cutting, although there is no evidence that these two different strategies make a difference in profitability. The first indication from the research has been that certain characteristics of Canadian firms are very useful predictors for firms to undertake product innovation. They are, among other things, the age of the firms, the single-establishment structure of the business and being multinationals. Research limitations/implications This empirical research opens up many interesting avenues for future research. Some other variables could be integrated into the models to increase the rate of explained variance. Moreover, because this research is based only on the case of Canadian firms and for a relatively short period of four years after the 2008 crisis, an extension to other context and to a longer period of time should be interesting. Practical implications The research has confirmed that Canadian firms adopting long-term business strategies based on product innovation are more productive. Social implications The results truly concur with the vision of the Government of Canada, like some other developed countries, on the importance of innovation and its policies in encouraging business innovation in driving the growth of the Canadian economy and improving the standard of living of country. Originality/value Mainly because of the lack of micro data, the existing researches have not provided solid evidence on why firms are choosing different business strategies when they are operating in the same business conditions and how the financial crisis has affected the undertaking of business strategies. They have not established a clear linkage between economic performance and different business strategies, although there has been some anecdotal evidence about their association. This study aims to bridge the knowledge gaps with theoretical and practical contributions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.081
GPT teacher head0.286
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2016
Admission routes2
Has abstractyes

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