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Business groups’ internal labour markets and SME labour productivity

ABSTRACT: Labour market regulation constrains small and medium-sized enterprises’ (SMEs) ability to minimize redundant labour. However, it is often neglected that many SMEs might circumvent these constraints by accessing a business group’s internal labour market (ILM). In this study, we analyse whether SMEs with ILM access—i.e., with an increasing number of sister group companies in the same subnational region-industry—enjoy a labour productivity premium and whether this potential productivity premium depends on the bargaining power of labour. Because intra-group reallocation of redundant personnel often involves substantial changes in employment conditions, we argue that the value of ILM access might be reduced when the bargaining power of workers is high as they can oppose the reallocation or demand significant compensation. Using a panel of 119,801 European SMEs during 2011–2019 (639,675 firm-year observations), we find that SMEs with ILM access show relatively higher labour productivity. Further, our findings suggest that this productivity premium is higher in those contexts associated with lower labour bargaining power.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11187-023-00780-4. Intra-group reallocation of unused labour can enhance SME labour productivity, but its feasibility and value-adding potential depend on labour bargaining power. This article explores differences in SME labour productivity attributed to internal labour market access, that is, the flexibility to reallocate unproductive labour to other sister companies in the group network. We also analyse if any potential productivity premium associated with internal labour market access depends on the bargaining power of labour. Our statistical analysis shows a labour productivity premium associated with internal labour market access. Furthermore, it is especially significant in contexts associated with low employee bargaining power. For instance, when collective bargaining coverage is low (very low) we predict a labour productivity premium of 3.3% (4.5%) for all SMEs embedded in a network of 10 companies in the same region-industry. Thus, our study contributes to sharpening our understanding of SME productivity and the value they receive from group affiliation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11187-023-00780-4.

SUBMITTER: Doucet P

PROVIDER: S-EPMC10189715 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Project description:BackgroundDigital health technology (DHT) has the potential to revolutionize the health care industry by reducing costs and improving the quality of care in a sector that faces significant challenges. However, the health care industry is complex, involving numerous stakeholders, and subject to extensive regulation. Within the European Union, medical device regulations impose stringent requirements on various ventures. Concurrently, new reimbursement pathways are also being developed for DHTs. In this dynamic context, establishing a sustainable and innovative business model around DHTs is fundamental for their successful commercialization. However, there is a notable lack of structured understanding regarding the overarching business models within the digital health sector.ObjectiveThis study aims to address this gap and identify key elements and configurations of business models for DHTs in the European Union, thereby establishing a structured understanding of the archetypal business models in use.MethodsThe study was conducted in 2 phases. First, a business model taxonomy for DHTs was developed based on a systematic literature review, the analysis of 169 European real-world business models, and qualitative evaluation through 13 expert interviews. Subsequently, a 2-step clustering analysis was conducted on the 169 DHT business models to identify distinct business model archetypes.ResultsThe developed taxonomy of DHT business models revealed 11 central dimensions organized into 4 meta-dimensions. Each dimension comprises 2 to 9 characteristics capturing relevant aspects of DHT business models. In addition, 6 archetypes of DHT business models were identified: administration and communication supporter (A1), insurer-to-consumer digital therapeutics and care (A2), diagnostic and treatment enabler (A3), professional monitoring platforms (A4), clinical research and solution accelerators (A5), and direct-to-consumer wellness and lifestyle (A6).ConclusionsThe findings highlight the critical elements constituting business models in the DHT domain, emphasizing the substantial impact of medical device regulations and revenue models, which often involve reimbursement from stakeholders such as health insurers. Three drivers contributing to DHT business model innovation were identified: direct targeting of patients and private individuals, use of artificial intelligence as an enabler, and development of DHT-specific reimbursement pathways. The study also uncovered surprising business model patterns, including shifts between regulated medical devices and unregulated research applications, as well as wellness and lifestyle solutions. This research enriches the understanding of business models in digital health, offering valuable insights for researchers and digital health entrepreneurs.

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Business groups’ internal labour markets and SME labour productivity

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Supplementary Information

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