Measuring the contribution of logistics service delivery performance outcomes and deep-sea container liner connectivity on port efficiency

Research in Transportation Business & Management xxx (xxxx) xxx–xxx

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Research in Transportation Business & Management journal homepage: www.elsevier.com/locate/rtbm

Measuring the contribution of logistics service delivery performance outcomes and deep-sea container liner connectivity on port efficiency ⁎

Halvor Schøyena, , Clemet T. Bjorbæka, Kenn Steger-Jensena,b, Noureddine Bouhmalaa, Umar Burkia, Tor Erik Jensena, Øivind Berga a b

University College of Southeast Norway, P.O. Box 4, NO-3199 Borre, Norway Aalborg University, P.O. Box 159, DK-9220 Aalborg, Denmark

A R T I C LE I N FO

A B S T R A C T

Keywords: Port efficiency Container ports DEA Logistics performance Freight connectivity

One objective for countries in the European common market is to optimize the performance of their multimodal logistics chains. The attainment of this goal requires the continuous development of container ports' performance, better customer satisfaction and - at the same time - to deter the occurrence of waste and bottleneck. Many regions in Europe are shifting from a single-port to a multi-port gateway situation; their ports frequently have overlapping hinterlands and are therefore increasingly facing competition and rivalry between each other. This paper examines container ports located in six countries: Denmark, Finland, Iceland, Norway, Sweden and the UK. It focuses on sensitivities to the inclusion of country-specific measurements on logistics service delivery performance outcomes on port efficiency. Port efficiency is measured with Data Envelopment Analysis (DEA). The results suggest that: (1) efficiency measurements for Danish, Finnish, Swedish and British ports are heavily influenced by whether logistics service delivery outcomes are included or not; (2) Icelandic and Norwegian ports appear to be not sensitive to whether logistics service delivery outcomes are included or not; (3) on average, the container ports located in countries that are directly called by deep-sea transcontinental container liners are over-performers and under-performers with regard to technical efficiency and scale efficiency, respectively. We further apply a second-stage regression analysis to explain the impact of country-specific contextual factors on DEA-based efficiency scores.

1. Introduction One objective for countries in the European common market is to optimize the performance of their multimodal logistics chains. By the year 2030, 30% of road freight over 300 km in Europe should shift to other transportation modes, such as waterborne transport; by 2050 this percentage ought to increase to > 50%, in order to achieve a more competitive, greener and resource-efficient transport system (European Commission, 2011). The attainment of this goal requires the continuous development of container ports' performance in supply chains, to provide customer satisfaction and - at the same time - to deter the occurrence of any waste, defect and bottleneck. Many gateway regions in Europe are shifting from a single-port to a multi-port gateway situation; their ports frequently have overlapping hinterlands (Ducruet, 2009; Langen & Nijdam, 2009) and are therefore increasingly facing competition, interchange and rivalry between each other (Notteboom, 2010; Schøyen, Hjelmervik, Wang, & Osen, 2017). Connectivity effects

(Calatayud, Palacin, Mangan, Jackson, & Ruiz-Rua, 2016) and aggregated service quality effects (Brooks & Schellinck, 2013) at gateway ports mean that a historically “natural” gateway for a certain hinterland region is not necessarily the port closest to that hinterland region (Notteboom, 2010). This paper benchmarks the performance of comparable northern European gateway ports, located in six Northern European countries: Denmark, Finland, Iceland, Norway, Sweden and the UK. Considered in this study are small and medium-sized container ports that are mainly acting as gateways in global supply chains (Fransoo & Lee, 2013) and as nodes in short-sea shipping. Logistics practices, both within European countries and across other continents, are far from uniform; they are different in respect of configuration of transport and networks (Rodrigue & Notteboom, 2010). Therefore, it is of interest for managers and policy makers to ascertain what the ports' potential for service delivery improvements are, compared to other ports in neighbouring countries and/or regions. The rationale for this study is the observation

⁎

Corresponding author. E-mail addresses: [email protected] (H. Schøyen), [email protected] (C.T. Bjorbæk), [email protected] (K. Steger-Jensen), [email protected] (N. Bouhmala), [email protected] (U. Burki), [email protected] (T.E. Jensen), [email protected] (Ø. Berg). https://doi.org/10.1016/j.rtbm.2018.03.002 Received 13 February 2017; Received in revised form 20 June 2017; Accepted 11 March 2018 2210-5395/ © 2018 Elsevier Ltd. All rights reserved.

Please cite this article as: Schøyen, H., Research in Transportation Business & Management (2018), https://doi.org/10.1016/j.rtbm.2018.03.002

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benchmarking is clear: this study is the first to study the impact of country-specific logistics service delivery indicators on container port benchmarking with DEA. Moreover, to our knowledge this paper is the first to scrutinize the relationship between port efficiency and freight connectivity of marine transport of containerized merchandise in the literature on container port production and economic efficiency: it does so by distinguishing between countries that are directly called by deepsea transcontinental container liners and those that are not. The next section is a literature review of the previous publications on benchmarking of container ports in multi-port gateway regions. The subsequent sections outline the methodology used in the analysis, the data used and the results obtained. The concluding remarks are presented in the final section.

that researchers, when conducting port performance benchmarking, have recognized the need to include both port-internal production input resources (port managers' controllable factors) and logistics service delivery outcomes in the production function. In port economic research, identifying significant uncontrollable factors for a set of ports, charting the interplay between controllable and uncontrollable factors and collecting the pertaining data is a difficult, costly and unresolved task (Bichou, 2013). Consequently, the literature on port efficiency generally provides rather unstable and inconsistent results. This situation is across researchers and in relation to dynamic market and operating conditions (Bichou, 2013). This study, when benchmarking a country's principal container ports, takes advantage of measurements of country-specific logistics service delivery outcomes, as developed and measured by The World Bank Group to produce their Logistics Performance Index (LPI) (Arvis et al., 2014; Arvis, Mustra, Ojala, Shepherd, & Saslavsky, 2010, 2012). Logistics service delivery performance outcomes relate to three indicators of timeliness, price and reliability. Timeliness is “the frequency with which shipments reach consignees within scheduled or expected delivery times”, price is “the ease of arranging competitively priced shipments” and reliability is “the ability to track and trace consignments” (Arvis et al. 2014, p. 7). Data on these logistics service delivery performance outcomes are collected from logistics professionals among international freight forwarders, shippers and express carriers. Ocean carriers' performance with respect to timeliness and schedule reliability varies substantially among shipping lines and routes (Saldanha, Tyworth, Swan, & Russell, 2009). Therefore, this paper also explores port efficiency in countries directly called be deep-sea transcontinental container liners versus those with no direct calls. Three research questions serve our purpose:

2. Literature review Since the very first port benchmarking DEA hypothetical study by Roll and Hayuth (1993), several papers have studied the efficiency measurements of container ports, and have provided a literature review on the use of DEA to measure their performances. Our choice of using DEA is grounded in the aim of making the results comparable to some of the latest studies of container port efficiency. Both globally and in Europe, the changing characteristics of the container port industry, along with the competition among container ports (Notteboom, 2012; Notteboom & Rodrigue, 2008), have triggered a vast literature of container port benchmarking, in the way of quantifiable efficiency and productivity assessment studies (e.g. DEA). The intention of these assessments has been to identify causes of inefficiency and ways of improving the ports' container operations (see, e.g. Tongzon, 2001; Barros & Athanassiou, 2004; Tongzon & Heng, 2005; Wang, Cullinane, & Song, 2005; Barros, 2006; Cullinane & Wang, 2006; Kaisar, Pathomsiri, & Haghani, 2006; Wang & Cullinane, 2006; Trujillo & Tovar, 2007; González & Trujillo, 2009; Wu, Yan, & Liu, 2010; Wu & Goh, 2010; Bichou, 2013; Schøyen & Odeck, 2017). The most thorough review and critical analysis of the major studies that have employed the DEA is found in Woo et al. (2012). Cullinane, Wang, Song, and Ji (2006) argue that high-quality container ports attract more users, thus ensuring a connection between the container throughput and the effectiveness of the production of a port. Correspondingly, they propose to include only the container throughput as output variable in port performance assessments. Most of the published papers on port performance measurement follow this approach. Tongzon (2001), on the other hand, considers both the quantity of cargo handled and the quality of port services to be appropriate indicators of production outcomes. For Tongzon (2001), the latter is operationalized by the ship-working rate, i.e. number of containers moved per working hour per ship. Such data are difficult to obtain. The recent study by Suárez-Alemán, Trujillo, and Cullinane (2014) shows that the inclusion of the time that ships spend in port, in the production efficiency measurements by DEA, would modify the results derived from a more traditional port efficiency approach. De Oliveira and Cariou's (2015) study of 200 container ports in 2007 and 2010 assesses the impact of inter-port competition on container port efficiency: they find that port efficiency decreases with competition intensity at the regional level (range 400–800 km). The effect of competition on efficiency is not significant when the latter is measured at a global level (> 800 km) or at a local level (< 300 km). Verhoeven and Vanoutrive (2012) aim at providing an overview of European port governance and conclude that there exist different typologies of port governance in Europe, which can be linked to geography and port size; port authorities can be classified in five regional groups. Denmark, Finland, Iceland, Norway and Sweden all belong to the Hanse group. In contrast, the UK (and Ireland) was sorted into a separate group, the Anglo-Saxon (Verhoeven & Vanoutrive, 2012). Since port performance is multi-faceted (Woo, Pettit, & Beresford, 2011), evaluation of port performance ought to take into account the influence of contextual

1. What is the efficiency of small and medium-sized northern European container ports? 2. How do logistics service delivery performance outcomes – as observed by logistics professionals - impact on the measurement of port efficiency? 3. Are there differences in the efficiency between ports in countries directly called by deep-sea transcontinental container liners versus those with no direct calls? To answer research question 1, the efficiency of 261 Danish, Finnish, Icelandic, Norwegian, Swedish and UK ports is measured against a frontier composed of the best performing among these. To answer research question 2, we need to appraise the efficiency sensitivities to these ports' country-specific logistics service delivery performance outcomes. To answer research question 3, we divide the ports in the two groups (direct deep-sea calls/no direct deep-sea calls) and compare their efficiency scores. The method applied for this study is the Data Envelopment Analysis (DEA), which is often used to measure the performance of ports (Woo, Pettit, & Beresford, 2012; Woo, Pettit, Kwak, & Beresford, 2011). The contribution of this paper to the literature on port performance

1 Schøyen and Odeck (2017) collected data on port internal production resources and container traffic, and measured the efficiency and productivity of container ports in Denmark, Finland, Iceland, Norway, Sweden and the UK. They point out that there may be external, uncontrollable factors that were not included in their analysis and that may impact efficiency and productivity. The data on port internal production input resources and container traffic for 20 of the 26 ports considered in this paper are obtained from Schøyen and Odeck (2017). Data for additional six ports' have been collected and included in the study reported in this paper, applying the same data collection and verification procedure as described in Schøyen and Odeck (2017). Such primary data collection from each port and terminal management is costly to perform. This paper extends Schøyen and Odeck (2017) by investigating the sensitivity to the inclusion of countryspecific logistics service delivery indices in the efficiency measurements, and by investigating the impact of direct connectivity of deep-sea shipping transcontinental shipping lines.

2

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SEk = 1 means fully scale efficient. SEk < 1 means scale inefficiency. To determine whether scale inefficiency is due to increasing or decreasing returns to scale, the sum of the weights under the CCR formulation must be examined:

variables (Bergantino, Musso, & Porcelli, 2013). The general conclusion that can be drawn from our literature review is that the DEA is the most popular, as well as the most recent approach in applications across container port studies. While the inclusion of the time dimension, along with other logistics-related aspects and contextual variables, can affect DEA efficiency results, inter-port competition intensity could force ports to over-invest in container handling facilities and therefore record a lower DEA efficiency score.

n

SW =

j=1

Benchmarking is basically the procedure of identifying the highest standards of quality for products and services, and pointing to the necessary steps to achieve those standards, frequently called best practices. DEA defines the best virtual producer (hereafter port) corresponding to each real port, where the virtual port does not necessarily exist but is imputed from the linear combination of inputs and outputs of one or more fully efficient ports. If the corresponding virtual port performs better than the real port - by producing either more output with the same level of inputs, or the same level of output with less inputs - then the real port is inefficient. Panel data was collected and scrutinized in this paper. The procedure of determining the efficiency indexes for the real port can be formulated as a linear-programming problem. The input-oriented constant returns to scale (CCR) formulation (Charnes, Cooper, & Rhodes, 1978) is expressed as: n

s. t ∑ ωj χj − φ1 χ0 = −s−j j=1 n

∑ ωj yj − y0 = s+j j=1

ωj ≥ 0,

j = 1, …, n

DEA‐CCR

ωj = 1

j = 1, …, n

DEA‐BBC

⎫ ⎪ (a) ⎪ ⎪ ⎪ (b) ⎬ ⎪ (c) ⎪ ⎪ (d) ⎪ ⎭

4. Data Important prerequisites when measuring the efficiency of ports are: (1) The data include clearly defined production units (container ports), (2) For each port there are outputs and inputs indicating the services produced in their operations, the resources used as well as the most critical ones, and (3) The ports utilize the same types of inputs to produce the same types of output services. This study selects 26 ports located in six countries, from five different multi-port gateway regions in the North Sea/Baltic area. These regions are: (1) Kattegat/Öresund, (2) Skaw/Oslo Fjord, (3) East Sweden, (4) South Finland and (5) South UK (Notteboom, 2010). Additionally, four gateways ports were included in the sample; one located in Scotland (UK), two on the Norwegian West coast and one in Iceland. Four ports are Danish (including the Malmö part of CMP), four are Finnish, one Icelandic, eight Norwegian, five Swedish and four British, see Fig. 1. Table 1 provides a listing of countries and ports, their average container traffic and average LPIs for the period studied, as well as presence or absence of direct deep-sea country calls. The sampled ports are mainly gateway ports (as opposed to sea-tosea transhipment hubs) and constitute a common container transport system for the North Sea, the Norwegian Sea, Skaw, Kattegat/Öresund and the Baltic Sea (Schøyen & Odeck, 2017). The sampled ports can be characterized as peripheral and smaller in comparison to many other container ports in continental Europe (Wiegmans & Dekker, 2016). Although the sampled ports and their regions can be considered as remote and peripheral compared to the container ports on the European mainland, the sampled countries' average LPIs (Table 1) suggest that these regions are well connected with the global container-shipping network. In line with almost all previous research on container port production, this study includes the necessary physical facilities as inputs to container terminal production. The selected input variables comprise quay length, terminal areas, number of container handling trucks (reach stackers and top lift trucks), number of straddle carriers and number of yard gantry cranes (including rail-mounted gantry (RMG) cranes and rubber-tyred gantry (RTG) cranes). The various ports show difference in the investments and operation of different types of container handling equipment. Following Cullinane and Wang's (2006) procedure, we combine the three variables of container handling trucks, straddle carriers and yard gantry cranes into a single combined variable, here called “number of yard machines”. The selected three input variables can be considered as proxies for capital (Fig. 2). Based on the literature review, container units (TEU) over each port per year are the most frequent output variable, and were consequently identified as one appropriate output measure, both for port production efficiency and effectiveness (Cullinane et al., 2006). Next, this study uses aggregated country-specific data on: (1) the ease of arranging competitively priced shipments “price”, rated from “very difficult” (1) to “very easy” (5) in LPI survey questions (Arvis et al., 2014, p. 7), (2) the ability to track and trace consignments (“tracking & tracing” rated from “very low” (1) to “very high” (5) in survey question) (Arvis et al., 2014, p. 7), and (3) the frequency with which shipments reach consignees within scheduled or expected delivery times “timeliness”, “the

(1)

For a more detailed explanation on model (1), see for example (Schøyen & Odeck, 2017). In addition, we wished to explore the assumption of variable returns to scale (VRS); the CCR formulation can be modified to include variable returns to scale (VRS) (Banker, Charnes, & Cooper, 1984). The VRS formulation by Banker et al. (1984) (hereafter denoted BCC) was therefore calculated in this study by modifying Model (1). For a more detailed explanation on this modification and how the linear programs are run, see for example (Schøyen & Odeck, 2017). The BCC formulation allows us to calculate scale efficiencies, see Eq. (2). The best-performing container ports show an optimal (φ-value equal to 1) combination of inputs or outputs. Less well-performing ports will obtain an φ-value between 0 and 1. Model (1) determines inputoriented efficiency. The reciprocal of Model (1) may be applied if output-oriented efficiency indices are to be determined. In our study, as will be more thoroughly explained in the next section, we have to process both ratio and ordinal output data. All input data are ratio scaled. This turns the above-formulated DEA linear-programming Model (1) into a non-linear imprecise DEA (IDEA). There exist techniques and procedures (Zhu, 2009) for transforming such an IDEA model into a regular DEA linear-programming model. Such transformation can be brought about by applying appropriate transformations of the data before they are to be processed by the model. In lieu of the somewhat modest, exploratory objective of this paper, it was decided to employ the data as they were measured and reported. Thus, the results obtained here should be interpreted with care. The technical efficiency indexes per container port (k) and time period can be used to obtain a measure of scale efficiency, which is given by:

SEk =

φCCRk φBCCk

(3)

where SW = 1 means the optimal scale. SW < 1 corresponds to increasing returns to scale and SW > 1 corresponds to decreasing returns to scale (Schøyen & Odeck, 2017).

3. Methodology

Min φ1

∑ ωj

(2) 3

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Fig. 1. Map showing countries and ports. The basis for the map was retrieved from: www.worldatlasbook.com.

Table 1 Countries and ports characteristics. Listing of countries, container ports studied and characteristics categorization Port no.

Country

Container port

Average container traffic 2010–2014. TEU per year Schøyen and Odeck (2017)

Container port size. Schøyen and Odeck (2017)

Average LPI ranking 2010–2012–2014 (n = 155–160). Arvis et al. (2010, 2012, 2014)

Direct calls: Country is directly called by deep-sea transcontinental container liners

1

Denmark

Copenhagen/ Malmö Aarhusa Aalborg Fredericia Helsinki Turku Rauma HaminaKotka Reykjavik Oslo Borg Moss Larvik Risavika Drammen Ålesund Kristiansand Gothenburga Stockholm Helsingborg Södertelje Norrköping Southamptona Immingham Grangemouth Bristol

152,000

Small

13.0

Yes

606,667 60,040 70,667 400,895 8587 226,512 572,899 225,009 205,753 42,324 58,421 61,547 27,333 23,868 61,483 44,979 790,987 37,775 173,667 31,215 40,283 1,605,257 148,333 250,000 82,369

Medium Small Small Medium Small Medium Medium Medium Medium Small Small Small Small Small Small Small Medium Small Small Small Small Medium Small Medium Small

13.0

No

37.0 13.0

No No

7.3

Yes

7.3

Yes

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 a

Finland

Iceland Norway

Sweden

United Kingdom

Port with direct-calls of deep-sea transcontinental container liners (Tasto, 2010). 4

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Price

LPI

Tracking & tracing

Timeliness

Fig. 2. Logistics Performance Index (LPI) and logistics service performance outcomes “Price”, “Tracking & tracing” and “Timeliness”, by country. 5-point Likert scale. Year 2014. Source: Arvis et al. (2014).

frequency with which shipments reach consignees within scheduled or expected delivery times”, rated from “hardly ever” (1) to “nearly always” (5) in LPI survey questions (Arvis et al., 2014, p. 7). These are three additional service outcomes (Woo, Pettit, & Beresford, 2011) and are therefore used as production outputs to assess the differences in port performance among the 26 ports in the six countries considered. These outcomes were qualitatively measured on a 5-point Likert scale, while data collection was managed by the World Bank Group in online surveys carried out among professionals in the fields of international freight forwarders and express carriers (Arvis et al., 2010, 2012, 2014). A general rule in applying DEA is to include a minimum set of ports in the evaluation set equal to the number of inputs multiplied by the number of outputs (Boussofiane, Dyson, & Thanassoulis, 1991). Accordingly, in this study, with a total of three inputs and four outputs variables, an adequate minimum set is 12 ports; since our sample comprises 26 ports, we fully meet this criterion. The data set covers three distinct periods (2010, 2012 and 2014), with twenty-six observations for each period and 546 data points. The summary statistics of the variables used are reported in Table 2. Table 2 shows that there is a great variation in the magnitudes of input variables and container traffic volumes among the ports. Table 3 shows the correlation coefficients between input and output variables. For the three outputs “container throughput”, “price” and “tracking & tracing” the coefficients are positive, which shows that when input increased, also output did increase for all these three outputs. Although “tracking & tracing” hardly correlates with the three input factors, we

Table 3 Correlation between inputs and outputs across years 2010–2014. Inputs

Terminal area (m2) Quay length (m) No. of yard machines (units)

Outputs Container throughput

Price

Tracking & tracing

Timeliness

TEUs per year per port

5-Likert scale

5-Likert scale

5-Likert scale

0.81

0.19

0.06

−0.17

0.58 0.93

0.20 0.20

0.01 0.12

−0.29 −0.10

decided to include it in the benchmarking analysis. Interestingly, Table 3 shows that “timeliness” correlates negatively with each of the three input factors, hence the “isotonicity” requirement in DEA (Lin & Tseng, 2007) is breached: that is, the output must not decrease while the input increases. This means that, in our sample, large ports are associated with countries with less “timeliness” in their overall logistics performance. This is not intuitive and cannot be explained. It was decided to employ “timeliness” as an output in the analysis, and to deal with its impact on the results in a sensitivity analysis, as will be explained in Section 5. Concluding on the selection

Table 2 Descriptive statistics. Descriptive statistics for the sample Inputs

Year 2010

2

Terminal area (m ) Quay length (m) No. of yard machines (units) Outputs Container throughput (TEU) Price (5-likert scale) Tracking and tracing (5-likert scale) Timeliness (5-likert scale)

Year 2012

Year 2014

Avg.

Max

Min

SD

Avg.

Max

Min

SD

Avg.

Max

Min

SD

237,995 842 18.6

998,717 2306 94.0

10,000 140 2.0

276,479 639 22.6

239,995 880 18.5

1,000,717 2792 94.0

15,000 140 2.0

276,087 679 21.9

240,365 899 18.7

1,000,717 2792 89.0

15,000 140 2.0

275,171 693 21.5

221,152 3.51 4.06 4.27

1,539,404 3.83 4.22 4.38

13,500 3.10 3.14 3.27

343,197 0.20 0.21 0.23

228,790 3.56 3.88 4.14

1,445,574 3.85 4.14 4.26

10,068 3.01 3.39 3.62

324,393 0.19 0.21 0.13

242,927 3.56 3.63 4.22

1,830,792 3.76 4.08 4.39

1884 3.15 3.31 3.51

375,593 0.15 0.30 0.25

5

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Table 4 The efficiency scores of container ports. Variable return to scale (VRS)

Port no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Country Denmark

Finland

Iceland Norway

Sweden

United Kingdom

Container port Copenhagen/ Malmö Aarhus Aalborg Fredericia Helsinki Turku Rauma HaminaKotka Reykjavik Oslo Borg Moss Larvik Risavika Drammen Ålesund Kristiansand Gothenburg Stockholm Helsingborg Södertelje Norrköping Southampton Immingham Grangemouth Bristol Average Max Min SD No. of efficient ports % of ports efficient

Scale efficiency (SE)

Scale of operation-sum of weights (SW)

Return to scale (RTS)

2010 0.54

2012 0.53

2014 0.59

Avg. 0.55

SD 0.03

2010 0.65

2012 0.63

2014 0.56

Avg. 0.61

SD 0.05

2010 1.02

2012 1.07

2014 1.06

Avg. 1.05

SD 0.02

Avg. Decreasing

1.00 0.75 1.00 0.53 0.34 0.78 0.43 0.63 0.74 0.60 0.94 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.97 1.00 1.00 1.00 1.00 0.86 1.00 0.34 0.21 15

1.00 0.75 1.00 1.00 1.00 1.00 1.00 0.67 0.78 0.53 0.86 0.88 0.71 1.00 0.84 1.00 1.00 1.00 1.00 0.89 0.93 1.00 1.00 1.00 0.92 0.90 1.00 0.53 0.15 14

1.00 0.75 1.00 0.46 0.41 0.77 0.44 0.81 0.64 0.56 0.83 0.70 0.67 1.00 0.90 1.00 1.00 1.00 1.00 1.00 0.89 1.00 1.00 1.00 1.00 0.82 1.00 0.41 0.20 12

1.00 0.75 1.00 0.66 0.58 0.85 0.62 0.70 0.72 0.56 0.88 0.86 0.79 1.00 0.91 1.00 1.00 1.00 1.00 0.96 0.93 1.00 1.00 1.00 0.97 0.86 1.00 0.55 0.16 10

0.00 0.00 0.00 0.29 0.36 0.13 0.33 0.10 0.07 0.04 0.05 0.15 0.18 0.00 0.08 0.00 0.00 0.00 0.00 0.07 0.04 0.00 0.00 0.00 0.04 0.08 0.00 0.10 0.11

1.00 0.49 0.99 1.00 0.86 0.96 0.96 0.99 0.98 0.97 0.96 1.00 1.00 1.00 0.97 1.00 0.76 0.67 0.49 0.65 0.52 1.00 0.76 0.84 0.85 0.86 1.00 0.49 0.18 6

0.84 0.49 0.83 0.56 0.32 0.77 0.52 0.98 0.96 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.79 0.65 0.57 0.59 0.50 1.00 1.00 1.00 0.86 0.80 1.00 0.32 0.22 10

0.73 0.45 0.75 0.99 0.71 0.98 1.00 1.00 0.97 0.99 0.99 0.99 1.00 1.00 0.99 1.00 0.74 0.71 0.54 0.51 0.52 1.00 0.93 0.81 0.89 0.84 1.00 0.45 0.19 6

0.85 0.48 0.86 0.85 0.63 0.90 0.82 0.99 0.97 0.99 0.98 1.00 1.00 1.00 0.98 1.00 0.77 0.68 0.54 0.58 0.51 1.00 0.90 0.88 0.87 0.83 1.00 0.48 0.17 5

0.14 0.02 0.12 0.25 0.28 0.12 0.26 0.01 0.01 0.02 0.02 0.01 0.00 0.00 0.02 0.00 0.02 0.03 0.04 0.07 0.01 0.00 0.12 0.10 0.02 0.07 0.00 0.09 0.08

1.00 1.03 1.03 1.00 1.02 1.01 1.01 0.92 1.31 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.10 1.14 1.14 1.14 1.14 1.00 1.09 1.08 1.09 1.05 1.31 0.92 0.08

2.88 1.10 1.10 1.06 1.13 1.29 2.89 1.07 1.45 1.00 0.99 1.00 1.00 1.00 0.99 1.00 3.20 1.04 1.03 1.04 1.04 1.00 1.00 1.00 1.07 1.29 3.20 0.99 0.64

1.05 1.07 1.07 1.02 1.03 1.02 1.01 0.94 1.13 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.07 1.14 1.12 1.14 1.14 1.00 1.15 1.15 1.16 1.06 1.16 0.94 0.06

1.64 1.06 1.07 1.03 1.06 1.11 1.64 0.98 1.30 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.79 1.11 1.09 1.11 1.11 1.00 1.08 1.08 1.11 1.13 1.79 0.98 0.22

1.07 0.03 0.03 0.03 0.06 0.16 1.08 0.08 0.16 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.22 0.06 0.06 0.06 0.06 0.00 0.08 0.07 0.05 0.17 1.22 0.00 0.36

Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Constant Constant Constant Constant Constant Constant Constant Constant Constant Decreasing Decreasing Decreasing Decreasing Decreasing Constant Decreasing Decreasing Decreasing

58%

54%

46%

38%

23%

38%

23%

19%

port across countries, and refers to scale efficiency (SE). Constant returns to scale (CRS) are not reported in Table 4 but can easily be obtained by multiplying the VRS efficiency indexes with the scale efficiency indexes (SE). The bottom-left part of Table 4 shows the averages of the mean VRS efficiencies across ports by each of the three periods reported. These averages are in the range 0.82–0.90, indicating that an average port in the sample could have reduced its inputs by 10–18% and still have produced the same level of output as the best performing ports. Next, the VRS scores of the individual ports from one year to another will be discussed. In this respect, three important observations emerge:

of variables, we assume that a port's main objective is to minimize the use of inputs and to maximize the outputs. 5. Results and discussion We evaluate ports from the input-oriented approach (confer Eq. (1)) because we purport to study how their managers and principals are capable of operating their restricted resources in an efficient way. As we intend to consider the scale of an operation's impacts on efficiency (SE), both CRS and VRS efficiency scores must be calculated, confer Eq. (2). In what follows we will therefore present the results of both VRS and SE when analysing efficiency. When investigating SE, we measure the Scale of operation-sum of weight (SW) in order to ascertain whether the individual port is operating under increasing, constant or decreasing returns to scale (RTS).

(1) Ten ports (Aarhus, Aalborg, Drammen, Kristiansand, Gothenburg, Stockholm, Helsingborg, Southampton, Immingham and Grangemouth) have an efficiency score of 1.00 each year, i.e. these are positioned on the frontier across all the three periods of observation. Two of these ports are Danish, two are Norwegian, three are Swedish and three are British. The data set contain three ports (Aarhus, Gothenburg and Southampton) that had direct calls of deep-sea transcontinental container liners included in their scheduled traffic (Table 1); notably, all these three ports are on the frontier each year. (2) The grand standard deviation is about 8%, which can be characterized as low; there is on average little variation in efficiency scores obtained by the individual ports across the periods of observation. This indicates that the relative position of the ports with respect to the frontier is stable through the period studied.

5.1. Efficiency Table 4 reports the efficiency scores by the year of observation, by using the variable returns to scale (VRS) and scale efficiency (SE) models of DEA. The CCR model identifies the overall inefficiency and the BCC model differentiates between technical efficiency and scale efficiency, confer Section 3. Hence, overall efficiency for one port depends on two factors: The first one is operational performance rooted in decisions by (i) the port and the container terminal management, (ii) the local (e.g. communal/county) authorities, and (iii) national authorities' policies related to transport logistics, for both domestic and international trade; this factor accounts for technical efficiency (VRS). The second factor is the relative size of operations for each individual

Concerning the variation in performance of individual ports, for

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5.2. How does logistics service delivery performance outcomes impact on the measurement of port performance?

some of them there are plausible explanations. One example is HaminaKotka: The two Finnish ports of Hamina and Kotka merged into one company in 2011, with the new name Port of HaminaKotka Ltd. From 2012 to 2014, HaminaKotka upscaled their container handling equipment by investing into six additional pieces of yard machines. The other critical port assets were reported to be kept constant during this period. A 23% increase in the container traffic was registered in the period from 2010 to 2012, while it declined by 9% from 2012 to 2014, which can be characterized as a major fluctuation. Hence, the port managers and the port users were experiencing some turmoil and were seemingly struggling to adapt the merged port capabilities and production in the market situations at that point in time. Another example from Finland is the port of Turku. The container volumes over Turku were dropping steadily, because that port was basically a ro-ro port without sufficient lo-lo container volume to support regular liner traffic; as there are no regular container lines operating to and from Turku, the container volumes of Turku are transported on the side of other traffic. During the period studied, the Finnish paper export industry has increased the containerization of exports of forest products and concentrated their flows via the ports of Rauma and HaminaKotka, which have managed to maintain their volumes. The container port industry in Southern Finland is truly dynamic and has gone through a period of transformation towards increased concentration of ports. Moreover, Finnish shipping may be susceptible to ice on the water in the winter seasons, which may influence Finnish ports operational performance, as will be further investigated in Section 6. In Norway, for the Risavika Container Terminal (RCT) there were relatively high investments in yard machines, yard area and quay length. The expansion of container handling facilities consequently increased Risavika's operational costs for 2010–2014. Combined with an increase in container throughputs from 2010 to 2014, which were relatively lower than the investments, these developments led to a reduction in Risavika's technical efficiency score from 1.00 in 2010 to 0.71 in 2012, and further to 0.67 in 2014. Let us now turn to consider the scale efficiency scores. The mean scale efficiency across all ports and period studied is approximately 0.83. Thus, on average, there is a potential for improvements in scale efficiency of about 17%. We have assumed that a scale-efficient port has an average scale efficiency score above a threshold value of 0.95. There are ten ports in the sample yielding a scale efficiency score above this threshold level: Reykjavik, all the eight Norwegian ports and Southampton. In this respect, Norwegian and British ports beat those in Denmark, Finland, Iceland and Sweden. All the considered Swedish ports, as well as Turku, Aalborg and Copenhagen Malmö (CMP) are all well below the average efficiency of 0.83. Thus, for Sweden, and in contrast to variable returns-to-scale scores, Swedish ports seem to be less scale-efficient than the sample average. Compared to the ports in other countries, the Finnish ones as a whole present higher variation in both VRS efficiency and SE scores over the period studied. The right column in Table 4 shows the scale of operations measure (SW). We observe that there are as many as 16 ports operating with decreasing returns to scale, while ten ports operate with constant returns to scale (are scale-efficient) and, notably, zero ports operate with increasing returns to scale. From the right-most column it can be further observed that whereas all the Danish, Finnish and Swedish ports as well as Reykjavik operate with decreasing returns to scale, all Norwegian ports in the sample operate with constant returns to scale. It should be kept in mind that the observations do not point to a certain “prime size” or optimal scale of operations. In our study, as will be explained in the next sections, it is particularly interesting to further investigate the sensitivities to changes in outputs impacts on the DEA model measurements of port performance.

To answer this question we have conducted a sensitivity analysis on the three dimensions of logistics service delivery outcomes: “price”, “tracking & tracing” and “timeliness”, see Table 5. Score values which changed by 0.1 or more are marked in bold. Table 5 shows that, when “price” was deleted, the grand average VRS score was reduced from 0.86 to 0.85, i.e. by one point, while no change was apparent when “tracking & tracing” was deleted. Hence, deleting either one of these two variables insignificantly impacts the average VRS efficiency scores in our study. Conversely, the average VRS score is reduced by three points when “timeliness” is deleted, therefore the time dimension is a possible influential factor, in line with Suárez-Alemán et al. (2014). Next, we simultaneously deleted all these three outcome variables and studied the impact on VRS scores, SE scores and Returns to Scale (RTS). By doing so, we explicitly intended to look only at the efficiency of the internal port operations (Woo et al., 2011), not at their logistical performance in supply chains. The right-hand section of Table 5 shows that deleting all the three variables reduces the average VRS efficiency score from 0.86 to 0.71, which is significant, and that VRS efficiency scores for 14 of the ports are heavily influenced. In this context, there are some ports and countries that need to be highlighted. Across all the five Swedish ports, the VRS scores fall greatly: between 19 points (Gothenburg) and 47 points (Helsingborg); the Danish and Finnish ports yield similar values. In contrast, the Icelandic port and the Norwegian ones, as well as Southampton, show no or little sensitivity when the three outputs on logistics outcomes are deleted. There are great variances in respect of how sensitive the scale is across the individual ports. The ranking of ports is influenced. Notably, the last column to the right shows that the returns-to-scale flag (decreasing, constant or increasing) has changed for all ports but six (Aarhus, HaminaKotka, Reykjavik, Oslo, Kristiansand and Southampton). For all the 20 ports with a change in returns to scale, the change is towards increased port size. 5.3. Do ports in countries directly called by deep-sea liners perform differently from those with no direct calls? As a point of departure before addressing this question, we refer to an overview of the considered countries, their ports and characteristics, as shown in Table 1. Three countries had direct calls: Denmark, Sweden and the UK. Three countries had no direct calls: Iceland, Norway and Finland. The three direct-called deep-sea ports in this study serve both as gateways and as hubs in global hub-and-spoke network configurations; the spoke mode for the flows is then distributed across the rail/ sea/road modes (Wilmsmeier & Notteboom, 2011). The 23 ports without direct calls of deep-sea liners rely primarily on feeder services2 to continental European ports (Tasto, 2010; Wiegmans & Dekker, 2016). Typically, these feeder services simultaneously serve both shortsea shipping markets and deep-sea transhipments markets on the same keel and leg (Paixão & Marlow, 2002). Based on the measurements presented in Table 4, Table 6 shows that the average VRS score for the three deep-sea called countries' ports was found to be high, at 0.94. The three countries with no direct calls had a lower average of 0.78. The average SE score for the three deep-sea called countries' ports was measured at 0.73, while for the three countries with no direct calls it was found to be higher, on average at 0.93. To reach a definitive conclusion we tested if there were any statistically significant differences in the VRS, SE and CRS scores between the ports in direct-called 2 Feeder service is a short sea shipping service which connects at least two ports in order for the containers to be consolidated or redistributed to or from a deep-sea service in one of these ports (UNECE, 2001). A port may function as a hub in one container liner service and as a gateway port in another, due to intricate multi-layered network structures in container shipping (UNECE, 2009, Rodrigue, Comtois, & Slack, 2006).

7

Denmark

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

8

United Kingdom

Sweden

Iceland Norway

Finland

Country

Port no.

Copenhagen/Malmö Aarhus Aalborg Fredericia Helsinki Turku Rauma HaminaKotka Reykjavik Oslo Borg Moss Larvik Risavika Drammen Ålesund Kristiansand Gothenburg Stockholm Helsingborg Södertelje Norrköping Southampton Immingham Grangemouth Bristol Average

Container port

Average values for years 2010, 2012 and 2014

Table 5 Sensitivity analysis.

0.55 1.00 0.75 1.00 0.66 0.58 0.85 0.62 0.70 0.72 0.56 0.88 0.86 0.79 1.00 0.91 1.00 1.00 1.00 1.00 0.96 0.93 1.00 1.00 1.00 0.97 0.86

VRS Table 4

0.55 1.00 0.75 1.00 0.66 0.52 0.85 0.62 0.70 0.72 0.56 0.88 0.86 0.79 1.00 0.91 1.00 0.92 1.00 0.89 0.96 0.92 1.00 1.00 1.00 0.97 0.85 One bold

Price

0.55 1.00 0.75 1.00 0.66 0.58 0.84 0.62 0.70 0.72 0.56 0.86 0.86 0.79 1.00 0.90 1.00 1.00 1.00 1.00 0.96 0.93 1.00 1.00 0.99 0.97 0.86 Zero bold

Tracking & tracing

VRS efficiency value after three outputs deleted 0.34 0.91 0.36 0.85 0.52 0.29 0.78 0.49 0.70 0.72 0.56 0.86 0.86 0.79 1.00 0.90 1.00 0.81 0.62 0.53 0.52 0.44 1.00 0.89 0.88 0.84 0.71 14 bold

Timeliness

0.37 0.92 0.55 1.00 0.66 0.58 0.85 0.62 0.70 0.72 0.56 0.87 0.86 0.79 1.00 0.91 1.00 0.98 1.00 0.86 0.92 0.85 1.00 0.97 1.00 0.97 0.83 Three bold

VRS efficiency value after one output deleted

0.61 0.85 0.48 0.86 0.85 0.63 0.90 0.82 0.99 0.97 0.99 0.98 1.00 1.00 1.00 0.98 1.00 0.77 0.68 0.54 0.58 0.51 1.00 0.90 0.88 0.87 0.83

SE Table 4

0.95 0.94 0.87 0.87 0.99 0.19 0.93 0.94 0.96 0.98 0.81 0.80 0.90 0.62 0.74 0.82 1.00 0.95 0.60 0.96 0.49 0.63 1.00 0.92 0.99 0.90 0.84 14 bold

SE efficiency value after three outputs deleted

From Decreasing to Constant No change From Decreasing to Increasing From Decreasing to Increasing From Decreasing to Constant From Decreasing to Increasing From Decreasing to Increasing No change No change No change From Constant to Increasing From Constant to Increasing From Constant to Increasing From Constant to Increasing From Constant to Increasing From Constant to Increasing No change From Decreasing to Constant From Decreasing to Increasing From Decreasing to Constant From Decreasing to Increasing From Decreasing to Increasing No change From Decreasing to Increasing From Decreasing to Constant From Decreasing to Increasing

RTS change

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Table 6 Non-parametrical statistical tests of the difference in efficiency scores 2010–2014 between ports in countries directly called by deep-sea transcontinental container liners versus no direct calls. Technical efficiency (VRS)

Scale efficiency (SE)

Constant return to scale (CRS)

Direct country calls

No direct country calls

Direct country calls

No direct country calls

Direct country calls

No direct country calls

Number of ports Number of observation periods Number of observations Median Average SD

13 3 3·13 = 39 1.000 0.936 0.132

13 3 3·13 = 39 0.813 0.781 0.208

13 3 3·13 = 39 0.742 0.732 0.180

13 3 3·13 = 39 0.990 0.932 0.151

13 3 3·13 = 39 0.742 0.696 0.223

13 3 3·13 = 39 0.749 0.723 0.218

Test results Mann-Whitney U Test Kolmogorov-Smirnov Test

Reject the null hypothesis (0.000) Reject the null hypothesis (0.000)

Reject the null hypothesis (0.000) Reject the null hypothesis (0.000)

Retain the null hypothesis (0.613) Retain the null hypothesis (0.906)

Table 7. In the Eastern and Northern part of the Baltic Sea the water is frequently frozen, and the concerned Finnish ports are much more affected by icy winters than other Norwegian, Danish and Swedish ports in the sample (Swedish Ice Service, 2017). We want to control for the impact of ice on the water during wintertime may result in ship navigation restrictions, delays and also possibly reduced port traffic and container throughput over the Finnish ports, i.e. whether there is in fact a seasonal effect. We investigate the annual VRS-scores, as depicted in Table 4. The dummy turns out to be statistically significant (β = −0.133, t = −2.453) at p < 0.05 level. The sign on the dummy variable for ice suggests that harsh ice conditions impede port efficiency. Next, we turn to governance structure. When we control for the impact of port governance typology, the dummy turns out to be statistically significant (β = 0.109, t = 2.167) at p < 0.05 level. The sign on the dummy variable for port authority typology suggests that the Anglo-Saxon group fosters port efficiency. Further, we regress the DEA-based efficiency scores on each of the country-specific indicators for “Customs”, “Logistics quality and competence” and “Infrastructure” (Arvis et al., 2010, 2012, 2014). “Customs” is the efficiency of customs clearance and border management (Arvis et al., 2012). “Customs” is negatively related with port efficiency, and is highly statistically significant (β = −0.381, t = −2.724) at p < 0.01 level. This result is not intuitive and we cannot find plausible explanations. “Logistics quality and competence” has a high statistical significance (β = 0.426, t = 2.692) at p < 0.01 level. The sign on the coefficient (Table 7) suggests that a high level of “Logistics quality and competence” promotes port efficiency. “Infrastructure” relates to “hard” and “soft” structures (Arvis et al., 2012), which inevitably may impact on ports' connectivity to hinterlands and forelands. Although “Infrastructure” appears to promote port efficiency (β = 0.151, t = 1.216) this parameter has a very high p-value (p = 0.228), revealing that there is no significant impact from “Infrastructure” on port efficiency.

deep-sea countries as a group, and the other ports as a second group. The statistical tests were conducted in SPSS Statistics 23, and the results are presented in Table 6. The null hypothesis in the statistical tests in Table 6 is that the distribution of the mean indices values across the two groups is similar. The test statistics, with p-value equal to zero across the two methods, indicate that there are significant differences between the two groups as far as VRS and SE are concerned. As regards CRS, the test statistics, with a very high p-value across the two methods, reveal that there are no significant differences between the two groups. The two tests of differences between ports in direct-called countries versus other ports show that whereas the null hypothesis can be rejected for VRS and SE, it cannot be discarded for CRS. The general deduction here is that the average VRS score of the individual ports in direct-called countries is well above average among all ports studied. On the other hand, there are indications that the ports in not-direct-called countries are better at scaling their operations (SE). One plausible explanation for such a circumstance is that for one port to scale-up and facilitate and be attractive for deep-sea direct-calls, the combined efforts –over decades- by ports management as well as by local and national authorities' policies leads to a situation where in that country container ports' are less scale efficient, compared to countries with no direct calls. One example is Gothenburg, a combined river and coastal port, where the commercial potential in large investments in container handling equipment, fairways and dredging, depends in full on the deep-sea ship calls by one operator alone, i.e. Maersk Line's AsiaEurope service (Hatteland, 2010). A country's or a region's coastal and inland topography and demography may also impact on whether some of its ports have direct-calls or not. A large and dense population generally creates a demand for ports of more direct calls. 5.4. Country-specific effects and differences in governance structure influence on port efficiency Until now we have investigated the sensitivities to port efficiency scores of country-specific logistics service delivery logistics outcomes. We now turn our investigation to other contextual variables, see Table 7 Control for country-specific effects and differences in governance structure. Country-specific contextual variables

Ice on the water (1 = ice season, 0 = otherwise) Port authority typology (1 = Anglo-Saxon, 0 = Hanse) Customs (5-point Likert scale) Logistics quality and competence (5-point Likert scale) Infrastructure (5-point Likert scale)

Test results Unstandardized β-coefficient

Sandardized β-coefficient

t-Value

−0.133 0.109 −0.381 0.426 0.151

−2.453 2.167 −2.724 2.692 1.216

−2.453** 2.167** −2.724* 2.692* 1.216

Note: * and ** indicate variables are at the 1% and 5% level of significance. 9

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6. Management implications and contributions to scholarly knowledge

identified, but the scale-inefficiency measurements are highly sensitive to the inclusion of variables on service performance outcomes. Further research is needed to investigate the reason why some ports exhibited more sensitiveness to the inclusion of logistics service delivery outcomes than others. (3) There is statistical evidence that the average operational performance of the individual ports in direct-called countries is well above average, while the average in scaling their operations efficiently is below average, when compared to the others. If port concentration within a region leads to a sufficient port capacity and size for one of the ports, this can be attractive for direct calls of deep-sea shipping lines, example provided in Section 5 is the port of Gothenburg. De-concentration within a port system occurs when some of the existing cargo is shifted from large ports to smaller or new ports, as it seems to be the case for the ports in the Oslo Fjord region across the period studied. Moreover, there is statistical significance that harsh ice conditions and typologies of port governance influence port efficiency scores. A high level of a country's “Logistics quality and competence” promotes port efficiency.

This paper used aggregated country-specific data on logistics service delivery performance outcomes to evaluate the efficiency sensitivities to these countries' container ports. When indicators for country-specific logistics service delivery performance outcomes are included, this analysis suggests that port size is kept constant or is decreased, cf. Table 4. Conversely, when logistics service delivery performance outcomes are excluded from the analysis, port size is kept constant or is increased, cf. Table 5. The general conclusion that can be drawn is that the inclusion of indicators for logistics outcomes can modify the results derived from a more traditional approach on container port production and economic efficiency based only on container throughput as an output variable. This has both a managerial and a policy implication relative to methods for assessing effects of port governance reforms: our results indicate that benchmarking ports' pure technical efficiency is not sufficient, as contextual variables need be taken into account, which includes container ports' performance and integration in supply chains. The main outcome of this study is that of viewing that, once contextual factors (e.g. logistics service delivery performance outcomes, ice on water, governance structure etc.) have been taken into account, small and medium-sized container port efficiency in multi-port gateway regions, can be more thoroughly investigated. The novelty of our contribution is that this is the first study to take into examination port efficiency with respect to longitudinal studies on logistics service delivery performance outcomes, as provided by the World Bank Group. In order to improve the identification and measurement of the individual port's service delivery outcomes, it will be ultimately necessary to collect systematic disaggregate data on port service logistics quality, as perceived by the users. This has both a managerial and a policy implication, namely the need to launch a data collection process in container ports and container businesses with the following objectives: on the one hand, identifying port users' decision makers – which frequently is not straightforward3 - and on the other seeking to meet their demand for the various quality attributes when looking for a logistics pathways for import and/or export relative to a region or country.

Some cautiousness, however, must be shown when interpreting the results. The material comprised only few ports and few observations from each country and region; hence, the derived scores may not tell the full story of the container ports of each individual country. Despite these shortcomings, this study has contributed to the existing literature by revealing the efficiency performances of some North European small and medium-sized gateway container ports. Acknowledgements The authors are sincerely thankful to comments from two anonymous referees and Alice Tonzig for her proofreading of the English language. Any errors are our responsibility. Financial support for this work was provided by European Union Interreg NØKS II: Närsjöfart i Öresund-Kattegat-Skagerak, www.interreg-oks.eu. References

7. Conclusion

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This paper applies DEA models in order to benchmark container gateway ports in six Northern European countries. The data set included proxies on port production resources and on container throughput, as well as country-specific measurements on logistics service delivery performance outcomes, as perceived by logistics managers among international freight forwarders. We have addressed three questions: (1) What is the efficiency of small and medium-sized northern European container ports? (2) How does logistics service delivery performance outcomes -as observed by logistics professionalsimpact on the measurement of port efficiency? (3) Are there differences in port efficiency between ports in countries directly called by deep-sea transcontinental container liners versus those with no direct calls? Our conclusions are as follows: (1) The average potentials for technical efficiency and scale-efficiency improvement across ports and for improvement during the period studied are 14% and 17%, respectively. (2) Efficiency measurements for Danish, Finnish, Swedish and British ports are sensitive to whether operational logistics service delivery outcomes are included or not. Icelandic and Norwegian ports are not sensitive to the same issue. No ideal operational size was

3 One example of a circumstance making it difficult to identify port users' decision makers in Norway: “Three of four cargoes shipped to Norway is booked by the foreign supplier”. Source: Shortsea shipping Norway, 2014. www.shortseashipping.no.

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