The role of information technology and workplace organization in firm productivity: evidence from Spanish firms

ABSTRACT

This paper explores the complementarity effect of information technology (IT) and workplace organization (WO) on firms’ productivity, but it also provides evidence of the differential effect of two of the main IT systems: Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM). To do this, I use a sample of more than 1,600 large Spanish firms for the period 2006-2009. I find evidence supporting the hypothesis that IT and WO are complementary. I also find that the use of CRM seems to interact better with WO to enhance productivity. Finally, results are consistent when analyzing manufacturing and services firms separately, although evidence for complementarity is strong for services.

KEYWORDS:

• Information technology
• workplace organization
• complementarity
• productivity

Acknowledgements

I thank the editor and anonymous referees for useful comments. I am grateful to the INE for access to the data. A previous version of this paper was awarded with the UAM-Accenture Chair 2016 award in Economics and Management of Innovation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Firms often adopt a number of strategies simultaneously, and empirical literature from many fields (including industrial organization, strategic management, and others) has long been interested in the analysis of complementarity between practices or decisions (Brynjolfsson and Milgrom 2013 and Ennen and Richter 2010 present comprehensive reviews of this broad literature).

2 For example, Bloom et al. (2014) argue that this aggregation is inappropriate because the impact of IT and CT on the organization of firms will be quite different depending on the type of technology. These authors find that the use of IT is associated with more autonomy and a wider span of control, whereas CT decreases autonomy for workers and plant managers.

3 A detailed description of these variables is given in Section 3.

4 Industry breakdown is defined in Table A1 in Appendix A.

5 I include the four mutually exclusive dummy variables in the regression, but do not include a constant term.

6 It is, unfortunately, not possible to completely solve potential omitted variable issues since some time-varying unobservables which jointly affect IT and WO adoption and productivity may still exist.

7 Specifically, I use data for the year 2006 to define the variable on IT and for the year 2005 to define the variable on WO, while productivity is defined over the period 2006-2009. Section 3 details data available and all variables employed.

8 The Community Innovation Survey (CIS) is a survey executed by national statistical offices throughout the European Union to investigate innovation activities of firms. The CIS is carried out in Spain by the Instituto Nacional de Estadística (INE) under the name Encuesta sobre Innovación en las Empresas. The CIS follows the recommendations of the OSLO Manual on performing innovation surveys (see OECD 2005).

9 This survey is executed by national statistical offices. In Spain, it is carried out by the Instituto Nacional de Estadística (INE) under the name Encuesta sobre el uso de Tecnologías de la Información y las Comunicaciones y del Comercio Electrónico en las Empresas.

10 Only 21 small-medium firms are available for all the years from 2006 to 2009.

11 As pointed out by Oz (2005, 793), ‘IT input figures used in any productivity study must include: computing hardware, telecommunications hardware and software, purchased software, software development, consulting services, and personnel training.’

12 The industry breakdown provided by the INE is: Food products, beverages and tobacco products; Textiles and clothing; Leather and footwear; Wood and products of wood and cork; Paper, publishing, printing and reproduction; Coke, refined petroleum products; Chemicals and chemical products; Rubber and plastic products; Other non-metallic mineral products; Metal products; Machinery and equipment; Electrical machinery, apparatus and electronic components; Transport equipment; Other manufacturing products; Wholesale, retail trade and repair of motor vehicles and motorcycles; Hotels and restaurants; Transport and communications; Financial intermediation; Real estate activities and professional, scientific and technical activities; Other services activities.

13 Following the Eurostat classification, I group firms by industry into six categories: high-tech manufacturing firms; medium-high tech manufacturing firms; medium-low tech manufacturing firms; low-tech manufacturing firms; knowledge-intensive services; and non-knowledge-intensive services (see Table A1 in Appendix A).

14 m₁ and m₂ Arellano and Bond (1991) test statistics for first-and second-order serial correlation.

15 It is worth mentioning that the period analyzed includes both years of economic expansion (2006 and 2007) and recession (2008 and 2009). As a robustness check, I find that OLS results for the periods 2006–2007 and 2008–2009 are quite similar (these results are available upon request). It therefore seems that, at least for the period analyzed, the complementarity effect between IT and WO exists during years of both economic expansion and recession. I thank an anonymous referee for raising this point.

Additional information

Funding

I acknowledge financial support from the Ministerio de Economía y Competitividad (Spain) [grant no PID2020-112984GB-C21].