Setting the model

Leading organizations use a variety of measures to evaluate their performance. A common indicator of organization performance is productivity. In basic terms, productivity relates to the output of goods and services divided by its input. The measurement of productivity can be employed by two approaches: the multi factor productivity (MFP) and single factor productivity. MFP is the ratio of total outputs to total inputs, whereas single factor productivity measures the ratio of outputs to a single category of inputs (Stone et al. 2020). How may one consider combining the technological dissemination and innovation? In an attempt to answer this, the OECD multi-factor productivity (MFP) has been applied in this study. According to OECD (2021), MFP reflects the overall efficiency with which labor and capital inputs are used together in the production process. Changes in MFP reflect the effects of changes in management practices, brand names, organizational change, general knowledge, network effects, spillovers from production factors, adjustment costs, economies of scale, the effects of imperfect competition and measurement errors. Growth in MFP is measured as a residual, i.e. that part of GDP growth that cannot be explained by changes in labor and capital inputs. In simple terms therefore, if labor and capital inputs remained unchanged between two periods, any changes in output would reflect changes in MFP.Footnote 4 Econometrically, we have followed the studies of Bourlès et al. (2013), and Gal et al. (2019), where (among other studies) MFP is considered to follow an error correction model of the form:

$$Delta MFP_{f, d, c, t } = a_{1} ,Delta MFP_{Frontier d, t } + a_{2} ,Distance_{f, d, c, t – 1 } + beta ,DA_{{ d, c, overline{t}}} + gamma ,Z_{f, d, c, t } + delta_{c,t} + delta_{i} + varepsilon_{c,t} ,$$


where ({Delta MFP}_{f, d, c, t }) denotes the shift in the logarithm of MFP of shipping firm f, which operates in division d and country c, in year t. MFP growth of firm f is presumed to hinge on MFP growth of the efficiency frontier (({Delta MFP}_{Frontier d, t})), which is described as the average MFP among the 5% most productive firms in division d and year t across the countries in the sample, and on the lagged distance to the frontier (({Distance}_{f, d, c, t-1}) = ({MFP}_{Frontier d, t-1})({MFP}_{f,d, t-1})).

Frontier shipping firms have been excluded from the sample to avoid endogeneity concerns. According to previous studies (i.e. Berlingieri et al. 2020; McGowan et al. 2017), one should presume α1 to be positive but below 1, suggesting that innovation at the frontier advantages shipping firms but only partially, whereas α2 to be positive, suggesting that shipping firms below the frontier value from the theory of convergence, meaning that they might possibly replicate the other firms’ methods. Nevertheless, the degree the frontier shifts might mean efficiency convergence or divergence among shipping firms.

Coefficient β, which depicts the effect of industry-level digital adoption on firm-level efficiency growth is of high importance. ({DA}_{ s, c, overline{t} }) embodies the portion of firms in division d and country c, which use a specific digital technology averaged over the period 2015–2020. The impact of distinct digital technologies has been measured in distinct identical regressions. Moreover, following Andrews et al. (2018), technologies combined effect has been measured employing a composite indicator of adoption, being built as the principal component of the five variables, signifying the implementation of different digital technologies.

A question to answer here would be about the profile of shipping firms and divisions which mainly benefit after the adoption of digitalization and what might the prospective interconnection with other factors be. To answer this, the digital adoption variable has been interrelated (1) with a categorical variable breaking water transport, and warehousing and support activities for transportation, and (2) with a variable encapsulating the average routine intensity of duties in each shipping industry, following Zhang and Tang (2021). Furthermore, in an effort to evaluate which shipping firms benefit more from the dissemination of digital technologies, the digital adoption variable has been interrelated with two categorical variables dividing the sample into size and efficiency classes, following Gal et al. (2019).

As digital adoption is detected only for one or two years in the period of interest, the regression depends on the average of the digital adoption variable over the available years (({DA}_{ d, c, overline{t} })), meaning that adoption does not fluctuate over time. From the one hand, this might put identification in danger, from the other hand prospective endogeneity issues could be alleviated, without omitting the observation of lagged benefits of digital adoption. As far as the vector of control variables ({Z}_{f, d, c, t}) is concerned, it captures firm size, age, division, and country-year fixed effects. Nevertheless, such an empirical framework has the advantage of considering plausible firm heterogeneities and crucial drivers of efficiency (firm-specific); this leads us to a more robust framework than an industry-level one (Wang 2018).

However, there are a few drawbacks to be unfolded. Initially, endogeneity (even being small or negligible) might endure. This is plausible to happen when factors being unobserved influence simultaneously adoption levels in a division and efficiency growth rates of the firms in each division; this means that the structured model has failed to explain this by division and country-year fixed effects and by the supplementary control variables. Second, the top 5% efficiency frontier being used for shipping firms in each division might cause understatement of the impact of innovative technologies, that some firms might be pioneers in adopting them. Third, based on the convergence theory previously discussed, some shipping firms might manage to fill in the gap by adopting digital technologies that more advanced firmsFootnote 5 have previously adopted; this could be explained via the efficiency gap variable instead of the digital adoption (Zhang and Tang 2021).

Blended firm and industry-level information

This study has merged a series of industry-level sources as far as routine intensity, digital adoption and occupational shortages are concerned, with firm-level data about efficiency growth. Information about digital adoption has been taken from the Eurostat “ICT usage in enterprises (isoc_e)” including country and industry components. Data given in this domain are collected on a yearly basis by the National Statistical Institutes or Ministries and are based on the annual Eurostat model questionnaires on ICT usage and e-commerce in enterprises. Furthermore, it supports measuring the implementation of one of the six priorities for the period 2019–2024 of European Commission—A Europe fit for the digital age. The coverage includes: ICT systems and their usage in enterprises, use of the Internet and other electronic networks by enterprises, e-commerce, e-business processes and organizational aspects, ICT competence in the enterprise and the need for ICT skills, barriers to the use of ICT, the Internet and other electronic networks, e-commerce and e-business processes, ICT security and trust, access to and use of the Internet and other network technologies for connecting objects and devices (Internet of Things), access to and use of technologies providing the ability to connect to the Internet or other networks from anywhere at any time (ubiquitous connectivity), use of Big data analysis, use of 3D printing, use of robotics, and use of Artificial Intelligence (Eurostat 2021).

The question arising here is about the subset of indicators to use. After carefully reading the literature, as well as the necessary documentation stemming from the European Commission (i.e. European Commission 2020), this study ended up following indicators similar to Andrews et al. (2018), and Gal et al. (2019), namely (1) Sophisticated Cloud Computing, (2) Broadband Internet Connection, (3) Enterprise Resource Planning, (4) Customer Relationship Management, and (5) Ecommerce Website.Footnote 6 Table 1 denotes the description of the above variables (digital solutions). The aforementioned variables come in annual data series, stemming from Eurostat comprehensive database (Digital economy and society statistics—households and individuals) and their coverage is from 2015 to 2020.

Table 1 Description of variables

Efficiency and other firm-level variables come from Bureau van Dijk, Orbis database (a Moody’s Analytics company), based on the information structure steps depicted in Gal (2013), Gopinath et al. (2017), and Andrews et al. (2018). A crucial issue to deal with was the data cleansing procedure to convert the financial data to a database appropriate for economic analysis. To ensure such, this study initially proceeded with comparability indications of nominal variables across countries and over timeframe, then received additional indicators, and of course kept solely shipping company accounts with valid and applicable information for this paper objective. The study acquired efficiency as a residual from measuring value-added based production tasks, for each detailed division independently, applying the control function methodology relying on transitional inputs to alleviate the endogeneity of input selections (Wang 2018). The sample has been limited to shipping firms having an average of at least 10 employees to meet the reference group of the division-level digital adoption indicator.

As far as the control variables at the industry level are concerned, the study of Pak and Schwellnus (2019) has been followed, who provide information of the routine content of occupations, based on the degree of independence and freedom in scheduling and establishing the duties to be accomplished on the occupation as a proxy for non-routine content. Moreover, the indicators measuring skill shortage and surplus rely on the OECD Skills for Jobs database (OECD 2018a) and are constructed on the basis of signals extracted from five sub-indices: wage growth, employment growth, hours worked growth, unemployment rate, and under-qualification growth. The indicators encompass seven sets of skills, of which we use the following ones: (1) complex problem-solving skills, including complex problem solving, (2) technical skills, including operations analysis, technology design, equipment selection, installation programming, operation monitoring, operation and control, equipment maintenance, troubleshooting repairing, quality control analysis, (3) systems skills, including judgment and decision making, systems analysis, systems evaluation, and (4) resource management skills, including time management, management of financial resources, management of material resource, management of personnel resources. As the OECD Skills for Jobs database includes seven big sets of skills, we focused on skills that are expected to be most corresponding to a shipping firm digital adoption. Our joint dataset extends over the EU 27 countries and 2 available divisions over 2015–2020 (Table 2).

Table 2 Descriptive statistics

The digital technologies description has been displayed in Table 1. Additionally, in regard to the shipping firm-level variables, ‘frontier growth’ is denoted as the average growth of the top 5% shipping firms in each division-year cell, ‘gap to frontier’ means the shipping firms’ lagged distance to the frontier, ‘age’ is the shipping firms’ age, ‘employees’ defines the shipping firms’ number of employees (log), and capex presents the capital expenditures (log).

The efficiency gains from digital adoption seem greater in industries that are intensive in routine tasks. This validates that streamlining or automating routine assignments is one of the channels through which digital adoption enhances efficiency. Though this could put up questions for policy in terms of job losses, digital adoption is also predisposed to creating new jobs due to their complementarity with skilled labor (OECD, 2019), this being an issue for further research.

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