Exploring and Validating Container Operational Risk Scale in Container Shipping: The Case of Ethiopian Shipping and Logistics Service Enterprise

Abstract

The risk associated with container shipping has been a major concern in recent decades. This study presents three major risk frameworks to systematically and inclusively explore and validate container operational risk scales based on risk factors derived from the extant literature. The three risk frameworks identified are risks related to information flow, risks related to physical flow, and risks related to payment flow. Each risk factor is grouped into sub-factors (dimensions), three factors for information flow, two factors for physical flow, and two factors for payment flow. The study uses Ethiopia as a case study and employed both qualitative and quantitative research methods. An interview survey was conducted to explore additional risk factors and validate the identified risk factors in container shipping, and a questionnaire survey was then accompanied to collect the relevant data. A pairwise comparison chart (PCC) was employed to rank the risk dimensions. The results showed that the container operational risk model is satisfactory by employing exploratory and confirmatory factor analysis. Furthermore, the PCC result indicates that risk of loss or damage of goods/assets, payment delay, and decrease in or total loss of payment were ranked first, second, and third, respectively, and consequently the most significant dimensions of the risk factors. This study provides a reliable and valid scale for measuring container operational risk in container shipping companies. It also unlocks future works for using the identified risk factors as guidelines for researchers and experts to design and develop container operational risk dimensions.

1. Introduction

In the global economy, container shipping has become the foundation of maritime conveyance and logistics systems [1,2]. As they gain prominence in diverse areas, container shipping companies have to deal with uncertainties and interruptions. As recognized in the literature, “risk” has continuously been debated as a major impelling factor in maritime transportation [3,4]. Risks associated with shipment management are classified as one of the leading possible accident risks in container docks, as stated by some port safety authorities such as Health and Safety Executive UK [5] and Hong Kong Marine Department [6]. In the case of Ethiopia, the Ethiopian Shipping and Logistics Service Enterprise (ESLSE) is an international shipping industry known for its volatility and high risks associated with its container shipping system [7]. Many studies in risk management have gained attention in logistics risk in general and container operational risk in particular [8,9,10,11,12,13,14]. However, they have not come to a common consensus on container operational risk dimensions [11]. The extant literature shows that the lack of management commitment of the shipping company to container handling is a typical dimension for container operational risk [15,16,17]. Drewry [18] indicated that the risk factors related to container logistics operations dimensions could be categorized into seven themes: booking and invoicing errors, documentation, errors in customs regulatory compliance and security compliance, theft and cargo loss or damage, strikes and transport congestions, piracy, and terrorist attack. In their study, Fu et al. [19] found that piracy has been a significant threat to container liners. It was also found by [20,21] that the risk related to container operational risk such as ‘‘delay in information transmission by parties involved’’ and ‘‘delay in the processing of document by government authorities (e.g., customs)’’ had a significant adverse effect on Taiwan’s shipping industry. This present study concentrates on risks in container shipping operations but endeavors to contribute to the research in this field by exploring additional risk factors. To further enrich the contribution, the paper validates and ranks the dimensions of the identified risk factors that could serve as a platform for researchers interested in this field.

To successfully achieve container safety risk management, the shipping companies are responsible for understanding how to explore the container operational risk dimensions for risk management purposes and for knowing the dimensions of container operational risks for port operation. To better understand how best to explore the container operational risk dimensions for risk management, the first step is to understand the experts’ and port employees’ perspectives and perceptions of the container operational risk dimensions. Additionally, to help container shipping companies to differentiate among the risk factors, the risk factors will be ranked to reveal which risks factors would have a more serious impact than the others and which ones would be the most significant among all other risks factors. Experts’ and employees’ perspectives and perceptions of container operational risk factors could provide the information needed for container shipping companies and maritime managers to make better decisions regarding the risk factors for successful container operational risk management. Consequently, this study contributes to the extant literature by achieving the following main objectives:

• To explore and validate the risk factors for container operational risk scales based on experts’ and employees’ perspectives at the ESLSE;
• To rank the risk factors to reveal the ones with more serious impact than the others at the ESLSE.

2. Literature Review

Based on the relevant research and the features of container operational risk, this paper reviews the literature on container risk factors as academia pays significant attention to risks in maritime transport and container shipping [12,22].

Despite a relatively short development history, a steadily thriving trend of the containerized shipping industry can be observed over the past few decades. A significant amount of 1.63 billion tons of containerized freight volume accounted for 15 percent of the international seaborne trade in 2015 [23]. In the global world, container shipping has become the backbone of maritime transportation and logistics networks [1,2]. As they gain momentum and involvements in different grounds, container shipping companies have to deal with challenges of instabilities and disruptions.

No generally accepted definition exists for the term “risk” [24,25]. Traditionally, risk is understood as potential economic losses or chances. In recent literature, there is a broader perspective. Risk is understood as an effect that prevents organizations from achieving their predefined targets [26]. The literature on container shipping and supply chain risk management as a whole has recently expanded, such as in the form of review papers (e.g., the recent ones by [24,27] and empirical research [28,29,30]. In the literature on supply chain risk management (see, for example, [31,32,33,34]), the authors point to the fact that it is almost taken for granted that companies implement such measures to prevent any unforeseen disruptions in the supply chain [35]. We refer to the definition of supply chain risk by Pfohl et al. [24]: ‘‘Supply chain risks involve risks that can be attributed to disturbance of flow within the goods, information, and financial network, as well as the social and institutional networks. They might negatively affect the goal achievement of single companies and the whole supply chain, respectively, concerning end customer value, costs, time, or quality’’, implementing related measures for identifying, managing, and mitigating then leads to supply chain risk management.

As it originated in the maritime discipline, “risk” has always been considered as a major influencing factor in maritime transportation [36,37].The attention of academia towards risk management has been reflected in numerous studies in the shipping and supply chain sector [12,22,38]. Unexpected disruptive events directly and indirectly negatively impact a company in multiple respects [10,11,13], such as unpunctuality of the liner schedule and damages or total loss of a shipment. These events could lower the transportation service quality or even cause severe disruptions in a supply chain. The continuity and agility of the shipping network and interrelated systems is heavily affected in a pervasive manner [39]. Furthermore, the existence and possible consequences of risks require a managerial mechanism, which in turn requires adequate resources of a company to be distributed [40]. Given the significant role of container shipping in transport and the irreplaceable position of the transportation process in logistics planning, risk management can be regarded as an essential sector in container shipping and supply chain management.

Risks in the container shipping industry exist in different areas such as business, market, supply, or demand. Owing to differences in factors involved and their mechanisms, this paper concentrates on operational risks. Operational risks here can be understood as the risks originating from activities in daily operations or businesses of the company [41]. An adequate risk management plan is essential to reduce and control operational risks. However, to facilitate risk prevention/mitigation plans, identifying and analyzing related hazardous events (HEs) are inevitable.

Additionally, resources allocated for the risk management of a company are limited to a time frame. Container shipping corporations are not exceptions. An effectively quantitative risk analysis model will provide insights into the risk situation of container shipping companies and motivate industrial stakeholders to take actions confidently as a decision support system [42].

Nevertheless, container shipping is a complicated and somewhat fragmented system that comprises physical movements, the associated information, and the responsibilities of multiple involved parties. Therefore, a decision support system, which could prioritize the identified operational risks based on a multi-dimensional base, is crucial regarding a container shipping company’s financial performance and service quality. The port provides information, costs [43], and facilities required by consumers to use container loading and unloading services and consider the accident rate and material damage [42,44].

Despite the importance of prioritizing operational risks, only a few studies attempted to comprehensively evaluate them or contribute analytical methodologies to determine their relative priorities [13,40,45]. One obstacle in container shipping risk assessment/prioritization is the scale and complexity of the system. It involves multiple parties (such as transporters, haulers, shippers, consignees, forwarders, and banks) whose responsibilities and processes (such as trucking, loading/unloading, shipping, payment, and consolidating) vary with different operations, which are hard to investigate exhaustively. Given the extraordinary relationship between container shipping and logistics operations, this paper continues to use the logistics perspective to identify operational HEs in container shipping as proposed in the study by Chang et al. [13,40]. By investigating the logistics network’s information flow, physical flow, and payment flow, potential HEs in container shipping operations can be identified and categorized inclusively. Even though it is undisputed that risk management along the entire container supply chain is essential for shipping companies, none of the review papers mentioned addresses the aspects of validating the risk factors for container operational risk scales.

In order to be comprehensive, this study considers risk factors associated with the three logistic flows in shipping operations, i.e., the information flow, the physical flow, and the payment flow. These concepts in the given context can be defined as follows: information flow refers to the gathering and conveying of information between parties involved in the container shipping process; physical flow refers to the flow of the container shipment from the shipping company to the customer; payment flow refers to the flow of monetary transaction of the container shipment from the customer to the shipping agent [46]. Given these definitions with detail and a comprehensive literature review, we found that the risk factors in container shipping operations could be grouped into three types of risk factors with seven sub-factors (dimensions), as shown in the frameworks explained below.

3. The Three Proposed Research Frameworks

This paper presents three proposed frameworks based on potential container risk factors to develop the dimensions of the risk. These frameworks are concentrated on different components and risk factors identified in the literature, namely information risk, physical risk, and payment risk. Inclusive explanations for the three frameworks are presented below.

3.1. Information Risk Framework

Information flow is very vital in handling risks associated with container shipping companies. The flow is initiated by the trade contracts between shipping agents and customers. The flow of information through different communication systems, e.g., email, telephone, and social media apps, in the supply chain will increase the time of transmitting the message due to the increase in the complexity of the process [47]. Consequently, it will delay information that will put the entire process under multiple risks [48]. The extant literature [48] noted communication inadequacies among various systems such as disruptions or breaks to delay transmitting the information. The time-lags between the sender and receiver are also identified as another source of delay in sending information [9,12,20]. Similarly, the processing of documents being delayed because of different accomplishments and tasks of parties in the container shipping process also exposes the entire system to the risk of delays, for instance, the apprehension of documents by government departments such as customs clearance and issuing of documents [10,20,25]. In the same line of investigation, information insecurity in information transmission is recognized as one of the major risk factors closely related to container transportation systems [49,50,51].

The sufficiency and integrity of information are also identified as an issue in the literature. For instance, merely general information of combined shipments in a container is made available in many cases. The omission of additional detailed but relevant information about individual shipments may bring risk to the container shipping process [8,9,10]. Reference [10] discusses other cases of incomplete information in cargo declaration, like issuing incomplete documents to accompany the shipment in case of any eventuality where the protection of the cargo is compromised with accidents and inadequacies. Inadequacy of information security could lead to exposure of the information transmission. The correctness and reliability of the information might be affected by tampered data [13]. Consequently, this could expose a container shipping company to the risk of information technology (IT) interference or illegal access to its information or database.

Moreover, technical risks involving information, such as deficiencies in the IT system, could interrupt the continuous flow of information and adversely affect other flows in the container shipping process. Crash or breakdown in IT infrastructure could be a possible factor [51,52]. Similarly, information technical risk could also be caused by inappropriate human operation on IT systems [53] and improper human operation on software applications [53]. Additionally, [54] noted that 95 percent of all IT insecurity issues are caused by human error. Inadequacies in the operation of IT systems will undoubtedly put the entire process of information transmission at risk.

The risk factors related to the flow of information are divided into three sub-factors (dimensions): information delay, information inaccuracy, and information technical risk [17]. Each of these sub-factors is also subdivided into multiple factors, as presented in Figure 1. Hence, information risk factors are incorporated in this study’s framework, which may be used to develop and validate container operational risk dimensions.

3.2. Physical Risk Framework

Unlike the case of information flow associated with a lot of complexity, the container movements seem to be more direct and straightforward in physical flow. It is important to note that the transportation of containers has to face so many risks. The risks are caused by different influencing factors, including shipping concentration, port condition, and human errors [10,18]. Some scholars in the extant literature noted that a port strike is one example of a human error [10,18,44,55]. Port congestion triggered by random times to wait before docking or before starting loading and clearing is another identified factor [12,18,44,55].

Moreover, Husdal and Bråthen [10] in their study pointed out many risks that could lead to interruption of outgoing and incoming physical flow. According to [56], inappropriate empty container transportation and the shortage of empty containers can potentially risk volume management. In addition, other factors identified in the literature that can trigger risk are stolen cargo from containers that are not sealed [10,18], loss or destruction caused by shipping dangerous goods [10,14,57], and damage to ship, or quay due to inadequate quay operations [10,35]. Additionally, terrorists or pirate attack is identified as a well-noted risk factor in container shipment [18,44,58].

On the whole, the risk factors related to the physical flow are divided into two sub-factors, namely transportation delay and loss or damage of goods [17]. Each of these sub-factors is also subdivided into multiple factors as presented in Figure 2. Hence, physical risk factors are incorporated into this study’s framework, which may be used to develop and validate container operational risk dimensions.

3.3. Payment Risk Framework

Unlike risks associated with information and physical flows of the shipping process in container shipment, risk factors associated with payment flow directly affect the financial performance of shipping agencies. In the extant literature, scholars recognize many risk factors related to monetary transactions. Payment delays by shippers, caused mainly by the clients’ errors and unrealized contracts with partners are major risk factors identified by previous studies [13,14,44]. Additionally, [44] in their study noted that changes in the currency exchange rate while making the payment or during payment could cause financial losses in international transactions. It is also pointed out in a study by [59] that financial losses may be incurred by shipping companies when the currency used in trading is weak. Another risk factor identified is that involved in the cost of operation, which is greatly affected by the rise in oil prices [55,59,60]. In the same line of investigation, if one party in the shipping system is faced with bankruptcy, it may also cause payment-related risks to other parties within the system [10,44,59].

Moreover, signing the shipping contract with the wrong partners, such as those with bad credit, may decrease or result in total loss of payments [13]. Breach of contract by shippers, reducing the volume of the container, and the willful abandonment of containers at the port of destination also increase the likelihood of financial loss or payment risk [13].

In summary, the risk factors related to the payment flow are divided into two sub-factors, such as payment delay and decrease or total loss of payment [17]. Each of these sub-factors is also subdivided into multiple factors, as presented in Figure 3. Hence, payment risk factors are incorporated into this study’s framework, which may be used to develop and validate container operational risk dimensions.

Although many studies exist on container shipping supply chain risks, they have not reached a common consensus on container operational risk dimensions. To the knowledge of the authors, none of the existing studies in this field have validated the container operational risk dimensions. This present study proposes adding a novel contribution to the existing body of knowledge by systematically and inclusively exploring, validating, and ranking the main risk factors facing container shipping companies in the Ethiopian context, which could serve as a platform for other developing countries that are similar to Ethiopia.

4. Materials and Methods

This paper uses the Ethiopian Shipping and Logistics Service Enterprise (ESLSE) as a case study. As noted in one of its assessment reports [7], ESLSE is an international shipping industry known for its volatility. Therefore, it necessitates complete strategies to mitigate the risks associated with its container shipping system and enable the companies to gain a competitive advantage. Within Ethiopia, ESLSE operates with multipurpose cargo carrier vessels, including dry port facilities with the capacity of 100,000 containers annually, and deals with 98% of the country’s import/export commodities [7]. Moreover, ESLSE has 35 shipping agencies in different ports and countries, including the Far East, mainly on Chinese ports (11), Middle Eastern and Indian ports (7), African ports (6), and European ports (11) [7]. Another reason why ESLSE is chosen as a representative of the shipping industry in Ethiopia is because ESLSE is a state-owned enterprise and the only shipping company handling container shipment in Ethiopia with different branches across the country [7]. We believe that a case study of this Ethiopian-based shipping company can provide a valuable insight that could be generally applied to the shipping industry in general as it is the sole provider of container shipping operation and has representative agencies in different countries.

To achieve the objectives in this study, a three-stage approach has been followed. Firstly, to assemble an inclusive list of potential risk factors in container shipping operations, a questionnaire instrument was derived from previous literature and different areas of supply chain risks in container shipping research. Secondly, interviews were conducted with experts in the shipping industry and university faculty members to validate the risk factors identified in the literature and to explore supplementary ones. Finally, a questionnaire survey was designed to list all the risk factors to collect the data for further analysis.

4.1. Interview

We conducted interviews to confirm the container operational risk factors identified in the extant literature and to discover supplementary ones that have not been mentioned in the literature. Twelve experts from six branches of the ESLSE container shipping company and six faculty members from three universities in Ethiopia were interviewed between 3 December 2020 and 30 January 2021. The three universities include Addis Ababa University, Jimma University and Bahir Dar University. The six ESLSE branches include Modjo in Ethiopia, Kality in Ethiopia, Djibouti branch in Djibouti, Silver Express Pvt.Ltd in Shanghai, China, National Shipping service Ltd. in the United Arab Emirate (UAE) in Dubai, and Cory Brothers agency in the United Kingdom (UK). The researchers choose these six branches for two reasons: they present a mix of the different representatives of the ESLSE from the Far East, Middle East, Africa, and Europe, and they are the largest in terms of capacity and size [7].

To achieve meaningful and adequate information from the experts’ interviews in the ESLSE branches, the interviewees comprised six container risk assessment managers and six senior container operations managers. All the twelve experts have extensive working experience in container shipping. Similarly, the university members interviewed included three from the Maritime Transportation department and three from the School of Management and Logistics. Among the six faculty members, two were deans, three were heads of departments, and one was a senior lecturer. The six faculty members have diverse research interests in container shipping and logistics, supply chain management, risk management, and maritime affairs.

Table 1. Demographics of the interviewees in the content validity and the reliability analyses.

Variables		Validity Analysis
	University Faculty Members (n = 6)	ESLSE Experts (n = 12)	Reliability Analysis (n = 18)
Gender
Male	4 (66.67)	7 (58.33)	11 (61.11)
Female	2 (33.33)	5 (41.67)	7 (38.89)
Age (y)	50.6 (8.3) *	50.7 (10.2) *	50.7 (9.3) *
<30	-	-	-
30–39	1 (16.67)	2 (16.67)	3 (16.67)
40–49	2 (33.33)	3 (25.00)	5 (27.78)
50–59	3 (50.00)	6 (50.00)	9 (50.00)
≥60	-	1 (8.33)	1 (5.56)
Working experience (y)	18.7 (9.4) *	19.3 (6.07) *	19.0 (7.52) *
<1	-	-	-
1–5	-	-	-
6–10	-	2 (16.67)	2 (11.11)
11–15	1 (16.67)	2 (16.67)	3 (16.67)
16–20	2 (33.33)	3 (25.00)	5 (27.78)
>20	3 (50.00)	5 (41.67)	8 (44.44)

* Mean and standard deviation in years provided for age and working experience of the participants.

summarizes the designation of the interviewees. We believed that responses from these twelve experts and six faculty members interviewed would provide adequate information to validate the identified risk factors and explore additional risk factors.

The content and the face validity of the scale were examined by the university faculty members and the ESLSE experts. The criterion for measuring the content validity by the faculty members and experts included three categories: (1) essential; (2) useful, but not essential; and (3) not necessary [61]. Further, we asked the interviewees to write their comments about the ambiguity and the clarity of the items to evaluate the face validity.

We employed descriptive statistics to describe the individual characteristics of the interviewees and to examine the content validity of the scale. Content validity ratio (CVR) was calculated for each item of the questionnaires, which were filled out by the experts [CVR = (n_e − N/2)/(N/2)]. The mean of item CVRs was computed to calculate the content validity index (CVI) [61].

The pair-wise ranking was performed using a pairwise comparison chart (PCC) to help rank the risk dimensions as experienced by the experts based on their impact on container shipment. In this way, the study also reveals which risks have a more serious impact than others and consequently which ones are the most significant among all other risks.

4.2. Questionnaire Survey

After validating the absence of errors in the scales and the terms used and exploring additional risk factors from the interviewees, a final questionnaire was designed. The respondents’ demographic information was included in the final questionnaire with thirty-seven items for seven factors proposed in the three frameworks, as shown in Appendix A. The questionnaire items were rated on a 5-point Likert scales ranging from strongly disagree to strongly agree. Data were collected from 384 respondents from 6 ESLSE operations (Modjo, Kality, Djibouti branch, ESLSE agency in Shanghai, China, ESLSE agency in Dubai, and ESLSE agency in the UK) via the online questionnaire. After responses with incomplete information and questionable responses were removed, 347 valid samples were obtained that were then used to do the final analysis. SPSS software version 25.0 and AMOS version 23.0 were used to analyze the collected data.

5. Data Analysis and Results

5.1. Interview Results

In the interview exercise, all the identified risk factors from the extant literature presented in the research framework were confirmed. Two new risk factors were recommended and added to the existing ones, which sum up 37 risk factors.

One suggested risk factor during the interviews is “Exchange rate fluctuation during payment process”. It was recommended as a risk factor as it results in an increase in cost, which has the tendency to delay the payment process. Similarly, “Unexpected rise in operational cost” was also recommended as a risk factor.

Table A1. Risk factors in container shipping supply chain.

Risk Factors	Items	Measures	Literature Sources
Information risk
Information delay	ID1	1. Using different communication channels in the supply chain increases the time of information transmission. (e.g., telephone, Email, EDI)	[13,23,24,33]
	ID2	2. Supply chain partners not transmitting essential information on time	[9,20,21]
	ID3	3. Processing documents being detained by the government departments (e.g., customs)	[10,20]
	ID4	4. Shipping company not transmitting essential information on time	[9]
Information inaccuracy	II1	1. Lack of information security within the information flow	[13,14]
	II2	2. Information asymmetry/incompleteness	[9,25]
	II3	3. Lack of information standardisation and compatibility	[33]
	II4	4. Shippers requesting extra service information	[13]
	II5	5. Shippers hiding cargo information (non-declare)	[13,25]
Information technical risk	ITR1	1. IT infrastructure breakdown or crash	[28,29,33]
	ITR2	2. Unsuitable human operation on IT infrastructure	[30,31]
	ITR3	3. Unsuitable human operation on application software	[30]
Physical Risk
Transportation delay	TD1	1. Port strike	[10,32,33]
	TD2	2. Port congestion (unexpected waiting times before berthing or before starting loading/discharging)	[18,32,33]
	TD3	3. Port/terminal productivity below expectations (loading/discharging)	[32,33]
	TD4	Unstable weather	[10,32]
	TD5	Unsuitable empty mile transportation	[18,34]
	TD6	Container shortage (e.g., Shippers use containers as storage, container revamp, unexpected demand)	[13]
	TD7	Lack of flexibility of fleet size and designed schedules	[28,34]
	TD8	Cargos are detained by custom	[13]
	TD9	Oil price rise	[10,32]
Loss/damage of goods/assets	LDG1	Damage to containers or cargo due to terminal operators’ improper loading/unloading operations	[10]
	LDG2	Cargo stolen from unsealed containers	[10,18]
	LDG3	Transportation of dangerous goods	[10,13,25]
	LDG4	Damage to ship or quay due to improper berth operations	[10,35]
	LDG5	Damage to frozen cargo or reefer containers due to power failure	[13]
	LDG6	Attack from pirates or terrorists	[18,19]
Payment risk
Payment delay	PD1	Currency exchange in payment process	[33]
	PD2	Financial crisis in the loan countries	[33]
	PD3	Payment delay from partners or shippers	[33,37]
	PD4	Unrealized contract with partners	[13,33,38]
Decrease or total loss of payment	DPL1	Unexpected rise in operational cost	Interview
	DLP2	Shippers’ bankruptcy	[10,33,37]
	DLP3	Exchange rate fluctuation during payment process	Interview
	DLP4	Shippers breaking contract and reducing container volume	[13]
	DLP5	Having partners with bad credit	[33]
	DLP6	Containers are abandoned at the port of destination	[13]

in the Appendix A summarizes all the risk factors identified in this research, two of which were identified through interviews (i.e., DPL1 and DLP3, highlighted in bold). The code of each item is listed in the second column in Table A1. The interviewees further confirmed the validity of the scale being identified.

Eighteen interviewees participated in the content and the face validity analyses of the container shipping scale. As shown in Table 1, the majority of the university faculty members (66.67%) and ESLSE experts (58.33%) were male. The age pattern revealed that most respondents of the two groups of the participants were aged 50–59 years. Most of the ESLSE experts had more than 20 years of working experience, and most of the university faculty members (50%) had more than 20 years of working experience. The majority (61.11%) of the interviewees who participated in the reliability analysis were male. Most of these interviewees were aged 50–59 years, and 44.44% of them had >20 years of working experience.

The analysis of the content validity of the scales, which were rated by the university faculty members and ESLSE experts, showed that all the 37 items had an excellent content validity. The acceptable level of CVR for the 18 interviewees is >0.38 [61]. Consequently, the 37 items were retained.

5.2. Questionnaire Results

This paper conducted the questionnaire data analysis through descriptive analysis, reliability and validity analysis, and exploratory factor analysis (EFA) to explore and validate the risk factors based on experts’ and employees’ perceptions.

However, before the EFA, we established the demographic characteristics of the survey participants. As shown in Table 1, the largest category of the participants at ESLSE who participated in the questionnaire survey was those between 36 and 40 years of age (32.28%), followed by those in the 31 to 35 years of the age range (20.75%). There were only seven respondents who were 20 years or below in age. Regarding the genders of the participants, male participants were higher in frequency than female participants at percentages of 61.28 and 38.62, respectively. Most participants had 11–15 years of working experience (34.29%), followed by those with 6–10 years of working experience (32.85%). The demographic information of the survey respondents is summarized in

Table 2. Demographic characteristics of the participant.

Items	Options	Frequency	Percentage (%)
Employee at ESLSE	Yes	347	100.00
	No	0	0.00
Gender	Male	213	61.38
	Female	134	38.62
Age	≤20	7	2.02
	21–25	38	10.95
	26–30	67	19.31
	31–35	72	20.75
	36–40	112	32.28
	>40	51	14.70
Education	Bachelor	158	45.53
	Masters	63	18.16
	PhD	13	3.75
	Others	113	32.56
Experience	1–5	73	21.04
	6–10	114	32.85
	11–15	119	34.29
	>15	41	11.82

.

5.3. Descriptive Analysis

Descriptive statistics analysis was done for all items for their mean, standard deviation, skewness, and kurtosis to test the normality of the data. According to Hair et al. [62], normality refers to the “degree to which the distribution of the sample data corresponds to a normal distribution”. The data can be assessed for normality statistically by obtaining skewness and kurtosis. Skewness is the measure of the symmetry of the data distribution [63], while kurtosis measures the peak or flatness of the distribution [62]. The distribution is normal when the values of skewness and kurtosis range between −1 and +1 [64]. The results show that the values of mean ranged from 4.04 to 4.11 on a five-point scale, which indicates that most of the respondents had an agreement with the items of risk factors associated with container shipping, as displayed in Figure 4

Furthermore, we presented the results of the descriptive statistics in

Table 3. Descriptive Analysis.

Factors	Items	Mean	Std.	Skewness	Kurtosis
Information delay (ID)	ID1	4.11	0.898	−0.964	0.408
	ID2	4.08	0.706	−0.791	0.070
	ID3	3.93	0.536	−0.789	0.066
	ID4	4.05	0.871	−0.795	0.104
Information inaccuracy (II)	II1	3.97	0.868	−0.949	0.459
	II2	3.99	0.786	−0.925	0.462
	II3	4.07	0.849	−0.852	0.031
	II4	4.12	0.852	−0.912	0.387
	II5	4.20	0.815	−0.853	0.114
Information technical risk (ITR)	ITR1	3.94	0.803	−0.850	0.124
	ITR2	4.13	0.875	−0.853	0.125
	ITR3	4.19	0.897	−0.851	0.254
Transportation delay(TD)	TD1	3.90	0.786	−0.814	0.136
	TD2	3.94	0.849	−0.963	0.516
	TD3	4.15	0.773	−0.917	0.272
	TD4	4.08	0.780	−0.912	0.287
	TD5	4.25	0.693	−0.922	0.259
	TD6	4.03	0.572	−0.958	0.525
	TD7	4.18	0.705	−0.903	0.465
	TD8	3.95	0.692	−0.862	0.540
	TD9	4.01	0.669	−0.917	0.691
Loss ordamage of goods/assets (LDG)	LDG1	3.93	0.735	−0.843	0.462
	LDG2	3.97	0.199	−0.954	0.481
	LDG3	4.17	0.082	−0.936	0.411
	LDG4	3.87	0.168	−0.921	0.397
	LDG5	4.41	0.694	−0.951	0.439
	LDG6	4.34	0.548	−0.972	0.480
Payment delay (PD)	PD1	4.14	0.920	−0.962	0.634
	PD2	4.16	0.837	−0.989	0.674
	PD3	4.09	0.749	−0.983	0.578
	PD4	4.07	0.538	−0.921	0.401
Decrease or total loss of payment (DLP)	DPL1	4.25	0.357	−0.993	0.600
	DLP2	3.97	0.648	−0.795	0.104
	DLP3	4.01	0.488	−0.798	0.114
	DLP4	4.09	0.849	−0.912	0.287
	DLP5	4.11	0.748	−0.983	0.578
	DLP6	4.14	0.392	−0.843	0.462

for each item. The results showed that the standard deviations range between 0.082 and 0.971, which implied that the values were acceptable. The normality distribution of the data was adequate because the values ranged between −1 and +1 according to the assumption of skewness and kurtosis.

5.4. Exploratory Factor Analysis

To evaluate the dimensions of the three models, an EFA was employed to ascertain an initial set of dimensions through varimax rotation. Seven dimensions were achieved, explaining 71.26% of the variance. The values of the Cronbach alpha for all dimensions were greater than 0.80, satisfying the threshold value of 0.70 recommended by [65], thus establishing internal consistency reliability of the scales. The computed Kaiser–Meyer–Olkin value of 0.937 established that the sample for the analysis was adequate. Moreover, Bartlett’s test for sphericity with a significance level (χ² = 4157.178, p < 0.01) verified the homogeneity of the variances [62].

Table 4. Exploratory Factor Analysis Rotated Component Matrix.

Measurement Items	Information Delay (α = 0.935)	Information Inaccuracy (α = 0.921)	Information Technical Risk (α = 0.910)	Transportation Delay (α = 0.879)	Loss or Damage of Goods/Assets (α = 0.854)	Payment Delay (α = 0.916)	Decrease or Total Loss of Payment (α = 0.930)
ID3	0.857
ID1	0.849
ID4	0.826
ID2	0.803
II2		0.831
II1		0.821
II4		0.818
II3		0.809
II5		0.794
ITR3			0.854
ITR1			0.847
ITR2			0.839
TD7				0.842
TD3				0.835
TD2				0.819
TD9				0.724
TD5				0.717
TD6				0.701
TD1				0.699
TD8				0.696
TD4				0.692
LDG3					0.844
LDG2					0.826
LDG5					0.794
LDG6					0.716
LDG1					0.708
LDG4					0.698
PD3						0.796
PD2						0.789
PD3						0.766
PD4						0.765
DPL1							0.837
DLP2							0.826
DLP6							0.804
DLP4							0.781
DLP5							0.738
DLP3							0.667

provides the results of the rotated component matrix of the EFA for the seven dimensions of the container shipping risk factors, along with their corresponding coefficient alpha scores.

5.5. Measurement Model

As illustrated in Figure 5, the risk factor measurements were considered as latent constructs in confirmatory factor analysis (CFA). The result of CFA confirmed that the model that EFA initially established is acceptable. The chi-square minimum discrepancy (CMIN) divided by its degrees of freedom (df) or CMIN/df is less than the suggested 3.0 value, and the overall chi-square statistic for the measurement model was significant (χ² = 315.070, df = 176, CMIN/df = 1.790, p < 0.001).

We followed the process outlined by [66] to complete the factor analysis, all individual items in each construct load at a statistically significant level (p < 0.001), with the standardized loadings for all items spanning from 0.794 to 0.923, as presented in

Table 5. Standardized Factor Loadings of Measurement Model.

Factors	Items	Standardized Loadings (>0.7)	p-Value	Items Removed
Information delay (ID)	ID3	0.874	0.001	No item
	ID1	0.855	0.001
	ID4	0.865	0.001
	ID2	0.872	0.001
Information inaccuracy (II)	II2	0.879	0.001	No item
	II1	0.841	0.001
	II4	0.818	0.001
	II3	0.859	0.001
	II5	0.794	0.001
Information technical risk (ITR)	ITR3	0.870	0.001	No item
	ITR1	0.865	0.001
	ITR2	0.862	0.001
Transportation delay (TD)	TD7	0.884	0.001	No item
	TD3	0.795	0.001
	TD2	0.867	0.001
	TD9	0.857	0.001
	TD5	0.855	0.001
	TD6	0.856	0.001
	TD1	0.878	0.001
	TD8	0.903	0.001
	TD4	0.865	0.001
Loss or damage of goods/assets (LDG)	LDG3	0.899	0.001	No item
	LDG2	0.897	0.001
	LDG5	0.879	0.001
	LDG6	0.874	0.001
	LDG1	0.871	0.001
	LDG4	0.846	0.001
Payment delay (PD)	PD3	0.861	0.001	No item
	PD2	0.857	0.001
	PD3	0.851	0.001
	PD4	0.853	0.001
Decrease or total loss of payment (DLP)	DPL1	0.920	0.001	No item
	DLP2	0.799	0.001
	DLP6	0.823	0.001
	DLP4	0.922	0.001
	DLP5	0.914	0.001
	DLP3	0.851	0.001

. The standardized loadings met both the minimum (0.50) and preferred (0.70) guideline suggested by [67] for all 37 items. The AVE value was 0.745, and each construct’s AVE exceeded 0.703, reaching the benchmark of 0.50 for convergent validity recommended by [68].Table 5 shows the standardized factor loadings of the measurement model.

Table 6. Scale Reliability and Validity Statistics for Measurement Model.

Construct	α	AVE	MSV	ASV
Information delay	0.935	0.751	0.454	0.279
Information inaccuracy	0.921	0.703	0.524	0.315
Information technical risk	0.910	0.749	0.351	0.208
Transportation delay	0.879	0.744	0.417	0.251
Loss or damage of goods/assets	0.854	0.771	0.531	0.382
Payment delay	0.916	0.732	0.419	0.266
Decrease or total loss of payment	0.930	0.762	0.282	0.249

Note. χ² = 315.070; df = 176; GFI = 0.953; AGFI = 0.937; IFI = 0.951 CFI = 0.986; NFI = 0.968; RMSEA = 0.047. AVE = average variance extracted; MSV = maximum shared variance; ASV = average shared variance.

shows the results for discriminant validity, where construct values for MSV, ASV, and AVE were compared to confirm MSV < AVE and ASV < AVE for all constructs. The discriminant validity of the constructs was also established by comparing the square root of the AVE with their paired correlations as shown in the diagonal of the matrix in

Table 7. Factor correlation matrix with square root of the AVE on the diagonal.

	ID	II	ITR	TD	LDG	PD	DLP
ID	0.867
II	0.284 **	0.839
ITR	0.453 **	0.503 **	0.866
TD	0.590 **	0.563 **	0.417 **	0.863
LDG	0.248 **	0.194 *	0.299 **	0.476 **	0.878
PD	0.378 **	0.138 *	0.539 **	0.526 **	0.425 **	0.856
DLP	0.415 **	0.189 *	0.485 **	0.576 **	0.521 **	0.468 **	0.873

Note. ** p < 0.01; * p < 0.05.

.

Moreover, other goodness of fit measures show that the model is satisfactory and hence acceptable. The GFI (0.953), AGFI (0.937), IFI (0.951), CFI (0.986), and NFI (0.968) were all greater than the 0.90 threshold value recommended by [69]. Furthermore, the RMSEA (0.047) computed value is far below the 0.08 threshold value recommended by [62]. Lastly, the calculated CFI value of 0.985 is above the recommended threshold value of 0.95 by [70]. (See bottom of Table 6.)

In order to rank the risks, pair-wise ranking was performed using the pairwise comparison chart (PCC) to help rank the risk dimensions as experienced by the experts based on their impact on container shipment (

Table 8. Pairwise comparison chart (PCC).

	ID	II	ITR	TD	LDG	PD	DLP	Score	Rank
ID	…..	7	5	10	4	7	6	39	7th
II	11	…..	8	11	5	5	7	47	5th
ITR	13	10	…..	7	6	6	7	49	4th
TD	8	7	11	…..	6	7	6	45	6th
LDG	14	13	12	12	…..	10	11	72	1st
PD	11	13	12	11	8	…..	9	64	2nd
DLP	12	11	11	12	7	9	…..	62	3rd

). In this way, the study also reveals which risks have a more serious impact than others and consequently which ones are the most significant among all other risks.

We rank the risk dimensions based on the perceptions and perspectives of the interviewees using the five-step procedure of the pairwise comparison chart (PCC) as follows: In the first step, we listed down the risk dimensions along the top row of the table and along the left hand side of the table. In the second step, we put dashes diagonally downwards in the chart. In the third step, we moved to the whole chart comparing two risk dimensions at a time to determine which one is more or less important based on the experts’ perspectives and perceptions. We recorded 1 in the row for the risk dimension that was more important and 0 in the row for the risk dimension that was less important. In the fourth step, we added across each row to determine the total. Finally, in the fifth step, we ranked the risk dimensions and reflect on the results.

From the results in Table 8, the ranking shows that risk of loss or damage of goods/assets ranks number one among the seven dimensions of the risk factors with a total score of 72. The second-ranked risk is the risk of payment delay with a score of 64 followed by a decrease in or total loss of payment with a score of 62 that ranks third. The fourth, fifth sixth, and seventh are information technical risk, information inaccuracy, transportation delay, and information delay with scores of 49, 47, 45, and 39 respectively.

6. Discussions and Conclusions

The main objectives of this study were the exploration, validation, and ranking of the container shipping risk factor scale. Inclusive literature was reviewed in identifying the risk factors, and an exploratory factor analysis was employed to validate the identified risk factors. After assembling all the container operational risk factor scales, a qualitative evaluation exercise was first done by a group of experts and university faculty members to evaluate the content validity of the scales as suggested by Seo et al. [71]. After that, we applied EFA and CFA to assess the construct validity of the scales. Moreover, the internal consistency reliability of the scales via the Cronbach alpha was also adequate as the results showed values above 0.80, meeting the threshold of 0.70 [65]. Hence, the scales were discovered to be a valid and reliable instrument to measure the container operational risk dimensions.

The EFA was done to explore the dimensions of the container operational risk factors in the three frameworks. The risk factor dimensions were categorized as information delay, information inaccuracy, information technical risk, transportation delay, loss or damage of goods/assets, payment delay, and decrease or total loss of payment. These results are consistent with the findings of the previous studies that stated the information delay, information inaccuracy, information technical risk [9,44,51], transportation delay, loss or damage of goods/assets [10,18,44,55], payment delay [13,44], and decrease or total loss of payment [13,44,55] as container operational risk dimensions. Furthermore, CFA’s findings support the application of the seven-dimension model of the three frameworks for measuring the container operational risk factors. The assessment of the major fit indices revealed that the dimensional structure of the container operational risk scale was satisfactory. The outcome of the Chi-square test for the examination of the CFA model showed a statistically significant result. The Chi-square test is one indicator of good model fit; however, it is more sensitive to minor misspecifications in the structure of the model [72]. Previous studies used other indices to verify the model fit when the Chi-square result was significant [72,73,74]. Tharaldsen et al. [75] also employed other fit indices, but they did not report the Chi-square result. We therefore used GFI, AGFI, CFI, NFI, goodness of fit, and RMSEA to evaluate the CFA model fit. Furthermore, the risk dimensions were also ranked via the PCC approach; the PCC result indicates that risk of loss or damage of goods/assets, payment delay, and decrease in or total loss of payment were ranked first, second, and third respectively, and consequently the most significant dimensions of the risk factors.

The qualitative evaluation of the container operational risk scales by a group of experts is a common approach to assess the content validity of the scales [71]. The application of a quantitative method for conducting such analysis facilitates the decision-making process regarding retention or rejection of the items of the scale. The authors employed experts and a Likert-type scale for rating the items (risk factors) in the validation process. These were conducted to consider the recommendations given by Wynd et al. [76] for overcoming the limitations of only relying on qualitative validation.

In summary, the results of this study showed that the validity and the reliability of the explored scale were satisfactory. The scale was developed in response to a need for a container operational risk dimension scale in the shipping industry in Ethiopia. It can be used to investigate the perception of experts and container shipping employees about risk factors associated with container shipping operations.

Although this paper uses the Ethiopian Shipping and Logistics Service Enterprise (ESLSE) as a case study, the findings of the risk factors can be extended to other international container shipping companies for two reasons. The first reason is that the interviewees include the experts of six ESLSE operations (Modjo, Kality, Djibouti branch, ESLSE agency in China, ESLSE agency in the UK, and ESLSE agency in Dubai) and university faculty members with diverse research interests in this field. Based on their viewpoint, the risk factors in container shipping operations could be generalized to international container shipping companies. The second reason is that although the respondents of the survey that this paper focused on are working in an Ethiopian-based company, this company is also regarded as an international company as the container shipping company has branches in many other countries around the globe. After establishing that fact, it should be noted that while applying the research findings in this paper to other container shipping operations, some cultural differences and market structures would need to be considered. This should possibly be considered when risk factors are analyzed in other contexts. Future research recommends re-investigating the scale’s reliability and validity with a more extensive and more diverse sample of experts and container shipping employees in different shipping companies. Such investigation will be necessary for the validity and reliability of the container operational risk dimensions’ structure across different companies. Research in the future might also consider assessing the discriminant validity of the scale by conducting a correlation analysis between the container operational risk dimensions and other causal work-related or institutional factors to establish relationships.