Short Sea Shipping as a Sustainable Modal Alternative: Qualitative and Quantitative Perspectives

Abstract

This study investigated the possibility of using short sea shipping (SSS) as a sustainable freight modal alternative by analyzing data collected from shippers in the New York State Capital Region. To this end, qualitative and quantitative approaches were jointly used. The qualitative analysis focused on exploring in-depth interviews with the decision makers regarding the drivers and the barriers to firms shifting to SSS. The quantitative efforts included estimating discrete choice (ordinal logit) models to assess the influence of four key governing aspects (leadership buy-in, emergency logistics, public policy, and sustainability) on the probabilities of shifting to SSS supported by the findings from the qualitative part. This paper also includes a comprehensive description of various variables, factors influencing the current mode choice, shippers’ perceptions, and willingness to use SSS. The results showed that firms with higher truck shares had fewer chances to switch to SSS unless in emergencies. Unfortunately, sustainability was the least valued by most of the participants in choosing SSS. Hence, lower costs and better service were essential. The ordinal logit models provide a potential tool for policymakers and freight planners to estimate the probability of firms choosing SSS over the current mode.

1. Background and Introduction

Sustainability is defined as meeting the current generation’s needs without compromising the ability to meet the needs of future generations [1]. Historically, corporations have predominantly operated within an infinite supply of resources driven by the notion that the companies should focus merely on minimizing the costs related to social, economic, and environmental activities. Contemporary culture now identifies resource limitations, scarcity of energy, and other hidden external costs to society (e.g., climate change, global warming, traffic congestion, noise) as critical issues that must be considered when addressing sustainability [2]. The transportation sector, especially freight transportation, is a major contributor to these externalities. The sustainability of freight movement by truck haulage has long been questioned due to its adverse impacts on the environment and society [3,4]. Road haulage is often characterized as causing environmental and societal problems regarding negative externalities, including highway congestion and longer wait times, air pollution, climate change, traffic accidents, noise, infrastructure damage, and high energy consumption [3]. Research showed that the external costs related to infrastructure, air quality, congestion, and accidents by truck transportation are nine times greater than those of water transport and six times greater than those of rail per ton-mile [5]. A growing driver shortage has recently presented an added concern that has dramatically impacted timely and cost-efficient freight delivery [6]. Global supply chain disruptions associated with the COVID-19 pandemic and the global warming crisis have exacerbated these concerns.

To overcome those road-related negative externalities, an instrumental measure suggested by researchers and the European Commission is a modal shift to less-polluting modes, such as rail or, better, by waterways. One of the least studied modal shifts is to a waterborne transport known as short sea shipping (SSS). SSS refers to moving goods via water over relatively short distances and does not involve ocean crossings [7,8]. SSS has been proposed as an alternative to truck haulage when water transport is feasible [9]. In the European Union (EU), SSS has demonstrated numerous advantages compared to the road haulage model; notably, improved highway congestion and cost reduction have been touted as the most significant benefits of this modal shift [10]. The United States Department of Transportation and the European Commission aggressively support SSS as an acceptable alternative, environmentally friendly mode of transportation [11,12]. Industry activists also proposed an SSS service in New York’s Hudson River Marine Highway corridor as part of a local effort to coordinate and improve infrastructure capacity and reduce the negative environmental impacts associated with truck haulage [9]. This research focused on SSS as an alternative mode.

The global shipping crisis was in its infancy when this research was conceived in mid-2020. Higher local household goods’ consumption had moderately disrupted regional supply chains at the time. Chaotic and unpredictable disruptions have affected the global supply chain, impacting regional operations with no end. Fuel costs have continued to rise, and the cost of shipping containers from China has skyrocketed to tenfold normal rates. As a result, containers crowd regional ports, and cargo ships are backlogged. Ports struggle to unload vessels, and a lack of truck drivers leaves freight unmoved. Although supply chain risk can mitigate catastrophic disruptions through proactive supplier diversification and capacity redundancy [13], researchers and authorities across the globe are advocating for more cost-efficient and environmentally friendly solutions.

Most research articles on SSS have focused on descriptive analysis, with limited empirical evidence on the drivers and barriers to adoption [4]. The literature proved that stakeholder theory provides the most robust support for supply chain management practices involving sustainability [14]. Using this theory, we can explain how stakeholders can be the primary drivers for the upstream adoption of sustainable practices [15]. This study conducted both qualitative and quantitative analyses of the adoption of SSS. It performed qualitative research focusing on exploring in-depth interviews (IDIs) with decision makers regarding the drivers and barriers firms face when switching to SSS. The quantitative efforts included estimating discrete choice (ordinal logit) models to assess the influence of various factors on the probabilities of shifting to SSS, which was supported by the findings from the qualitative part.

Four objectives guided this study: (1) to determine shippers’ satisfaction level and gain insight into their concerns regarding their current mode of shipping; (2) to determine shippers’ perceptions of SSS, including perceived barriers to a mode shift and improvements needed to consider a mode shift; (3) to determine the level of improved transportation costs and quality of service needed to persuade shippers to shift their current mode of shipping; and (4) the estimation of discrete choice (ordinal logit) models to assess the influence of various factors on the probabilities of shifting to SSS supported by the findings from the qualitative part.

The rest of this paper is organized as follows: Section 2 provides a brief review of the literature related to mode choice and sustainable freight modal shifts to SSS; Section 3 presents this study’s area, data (qualitative and quantitative), and descriptive statistics; Section 4 provides an overview of the methodology used; Section 5 offers the quantitative results supported by the findings from the qualitative analysis of IDIs; and, finally, Section 6 provides concluding remarks.

2. Literature Review

An extensive literature review was conducted to better understand the factors and dimensions relevant to a mode shift to SSS. The literature on SSS has predominantly focused on the choice of ports, unimodal or intermodal transport, and carrier selection [7,16,17]. Perceived limitations to switching from truck haulage to SSS include demand elasticities for service, business climate, economic performance, service quality, and environmental performance [4,7,18,19,20,21]. Despite these perceived limitations, SSS has taken a considerable hold in other developed nations in the European Union (EU). In the EU, SSS has demonstrated numerous advantages compared to road haulage; notably, improved highway congestion and cost reduction have been touted as the most significant benefits of this mode shift. Most of the literature on SSS has focused on EU countries, which can be attributed to their well-connected waterways and their supportive policies designed to mitigate the negative externalities associated with trucking [22]. For instance, Raza and Svanberg [4] published a systematic review on a mode shift from truck haulage to SSS. Out of the 58 papers Raza et al. included in the review, only 5 focused on SSS in the United States [12,23,24,25,26]. Each of these five papers focused on SSS as a form of intermodal transport, characterized by a separate contract for each leg of the shipment journey, multiple handoffs, and, therefore, greater risk.

Perakis and Denisis [12] conducted a literature search and a strengths, weaknesses, opportunities, and threats analysis to address the major issues and benefits of SSS operations in the United States. The authors highlighted several drivers and barriers to SSS competitiveness and ultimately recommended a multimodal transportation system with an SSS component. Multimodal transport typically involves a single contract for the entire shipment journey, which means fewer handoffs and lower risk—ultimately, Raza, Svanberg [4] concluded that economic, environmental, and service quality represent the three dimensions that should be explored when considering a mode shift from truck haulage to SSS. The freight cost is generally regarded as the top economic supply chain efficiency indicator. Freight costs are defined as the charges paid for freight transportation, with acceptable premiums often paid for speed and reliability [5]. Two dimensions (i.e., economic and quality of service) align with those in earlier studies. Holguín-Veras, Kalahasthi [27] conducted a national-level qualitative and quantitative study on freight modal shifts. They concluded that the top two factors affecting mode choice were cost and quality of service. Notably, several participants in the IDIs mentioned they would switch from trucks to waterways if abandoned river terminals were opened, which improved delivery times and terminal operations.

Other relevant works regarding sustainable modal shifts and the modeling of preference or choice of freight mode are those of [21,28,29,30,31]. Wilson, Bisson [28] examined the factors affecting the choice of shifting from truck to rail using linear probability models, where reliability was found to be significant compared to transportation costs. Wilson, Wilson [29] estimated truck and rail market shares for grain transport with respect to costs and fuel prices. Comi and Polimeni [21] developed a discrete choice model to evaluate the probability of using a given transport mode (i.e., SSS, railway, or road) in the Mediterranean basin. The authors analyzed different scenarios that could arise from introducing SSS services, where the focus on safety, pollution, and efficiency was analyzed. Across all cases, the authors noted the competitive advantages of using SSS for maritime freight flows, shedding light on SSS as a competitive, sustainable alternative to road, rail, and combined road and rail modes. Larranaga, Arellana [30] identified logistics managers’ preferences for freight transport service attributes in terms of mode split. Different transport policies that could encourage multimodality and more sustainable uses of available transport infrastructure were discussed to promote alternative modes and strengthen competitiveness. Kim, Nicholson [31] used latent class modeling to study the different decision-making processes of firms, freight shippers, and agents in New Zealand. The author’s experiments focused on two main groups: long-hauling and low-volume shipment and long-hauling and large volume. The long-hauling and low-volume split is based on the perception toward sea and rail and the positive utilities by reducing transport time and improving service reliability. The recent freight mode choice literature in the U.S focuses on estimating discrete choice models using the Commodity Flow Survey (CFS) datasets, i.e., either the public-use microdata [32,33] or the confidential microdata [27]. However, none of these studies considered SSS exclusively as a modal alternative, primarily due to a lack of data and research.

Table 1. Literature review summary.

No.	Reference	Factors in Mode	Mode Choice	GHG Emissions	SSS in Europe	SSS in the US	Port as Mode	Barriers to SSS	Drivers to SSS
[4]	Z. Raza, M. Svanberg, and B. Wiegmans, (2020)				√			√	√
[5]	A. Izadi, M. Nabipour, and O. Titidezh (2020)	√
[7]	M. R. Brooks, J. R. Hodgson, and J. Frost (2006)						√	√
[10]	G. Fancello, P. Serra, and S. Mancini (2019)						√
[12]	A. N. Perakis and A. Denisis (2008)					√			√
[17]	A. K. Y. Ng (2009)						√
[19]	C. J. Kruse, D. H. Bierling, and N. J. Vajdos (2004)							√
[20]	L. García-Menéndez, et. al (2004)							√
[21]	A. Comi and A. Polimeni (2020)		√					√
[23]	H. B. Bendall and M. R. Brooks (2011)					√
[24]	J. J. Corbett, et. al (2012)					√
[25]	R. Nealer, H. S. Matthews, and C. Hendrickson (2012)					√
[26]	S. M. Puckett, et. al (2011)					√
[27]	J. Holguín-Veras, et. Al (2021)	√	√
[28]	F. Wilson, B. Bisson, and K. Kobia (1987)		√
[29]	W. W. Wilson, W. W. Wilson, and W. W. Koo (1988)		√
[30]	A. M. Larranaga, J. Arellana, and L. A. Senna (2017)		√
[31]	H.-C. Kim, A. Nicholson, and D. Kusumastut (2017)		√
[32]	N. Keya, S. Anowar, and N. Eluru (2019)		√
[33]	M. Stinson, et al. (2017)		√
[34]	A. M. Arof (2018)							√
[34]	A. Arof, R. M. Hanafiah, and I. Ooi (2016)		√
[35]	A. Christodoulou and J. Woxenius (2019)
[36]	S. Theofanis, M. Boile, and W. Laventhal (2009)
[37]	J. Holguín-Veras, et. al (2016)
[38]	Xu, L., Zou, Z., Chen, J., & Fu, S. (2024)			√
[39]	Xu, L., Zou, Z., Liu, L., & Xiao, G. (2024).			√
[40]	Xiao, G., Yang, D., Xu, L., Li, J., & Jiang, Z. (2024)
This paper		√	√			√		√	√

shows a summary of the literature with the key attributes from each work, as well as those addressed in this paper.

Research also indicates that decision making regarding a mode shift should account for regional corridor-specific attributes [34,35]. For example, unionized port labor has traditionally resisted the adoption of new operating practices. Consequently, the International Longshoreman’s Association represents a potential barrier to establishing an SSS service. The movement of mixed domestic SSS with international transshipments outside of normal locations may or may not fall under the association’s jurisdiction. Operating negotiators should understand organized labor’s reaction to and influence on the ability to shift modes and locations [7,36]. However, shippers and receivers most often make the ultimate decision for mode choice because they select the shipment size and establish a delivery’s urgency level [37]. The Government of China has placed regulations reducing sulfur emissions related to vessels in ports on the Yangtze River that could be applied in a Hudson River scenario [38], where similar studies have focused on the use of environmentally friendly ships to reduce control sulfur emissions, and government policies driving their adoption will further add to the sustainable benefits of marine transportation [39]. Current research has also pointed to the use of artificial intelligence (AI) technology in shipping data processing and analysis to increase efficiency and economic benefits in shipping [40]. Yet, little empirical research has been conducted with shippers and receivers to understand better the perceived barriers and opportunities associated with a mode shift to SSS. Hence, this research addresses this significant gap in the freight literature by examining the mode choice with an emphasis on SSS as a sustainable alternative.

3. Study Area and Data Description

This study followed a mixed-methods design, where qualitative data are complemented with quantitative data. Quantitative data were collected first using a researcher-developed online survey. After conducting the survey, in-depth interviews (IDI) were conducted with twelve survey respondents to gain additional insight into shippers’ perspectives regarding their current mode of shipping and the factors influencing their views on a shift to SSS. A comprehensive description of the study area and qualitative and quantitative datasets is provided below.

3.1. Study Area

The participants in both quantitative and qualitative surveys had their establishments located in the New York State Capital Region. They preferred to utilize the Hudson River Corridor, which runs from the New York City metropolitan area to the Albany area (see Figure 1). Hence, a brief history of the study area was included. However, the findings from this research apply to similar firms and economies in the U.S., and they are not confined to this study area. Despite water transport’s benefits, minimal freight movement occurs along the Hudson River Marine Highway between New York City and Albany. A regional study undertaken by the authors for the U.S. Maritime Administration’s (MARAD) Marine Highway motivated this research. MARAD has 26 designated routes [41]. The Capital Region is a booming regional economic development area of 1.1 million people and home to global leaders in the technology and manufacturing industries. Leading global manufacturing hubs reside throughout the region, most within one hour or less of deep-water port access via the Hudson River. Logistics cluster opportunities, to maximize SSS, exist in the Capital Region, where 28 major distribution centers are located at the crossroads of major interstate highways (i.e., the I-87 and I-90). Furthermore, two international ports connect to New York’s northernmost 12-month port operations, with rail connections to CSX Corporation, Norfolk Southern, and the Canadian Pacific Railway.

The New York Capital District’s proximity to the Hudson River presents a unique opportunity to leverage logistics clusters to bring value-added service, job opportunities, mobility, and overall regional growth and competitiveness [42,43]. The local Albany market also has a significant cluster of large retail distribution centers that service the East Coast Seaboard and New York metropolitan area within a 48hr. order-to-delivery window [44]. However, a prior attempt to establish an SSS service in this area failed. The barge, which operated from 2003 to 2006, represented an attempt to execute a mode shift from truck to container on barge (COB), which proved challenging to establish because a minimum volume of cargo is needed to make COB financially viable [27]. Other reasons cited for the barge’s failure included infrequent trips, longer transit times, and a lack of specific resources and strategies to optimize the lane [27,44,45]. The perceived imbalance of the freight corridor’s directional traffic flow, international component, handling charges, and distance could be more efficiently optimized with the expanded development of clustered logistics and collocated customers directly on the water [42,45,46].

Figure 1. Marine highway route designation M-87. Source: [47].

Figure 1. Marine highway route designation M-87. Source: [47].

Lessons can also be learned from the James River Barge Service, established in 2008 at the Port of Virginia. As a result of a partnership with a national agricultural company, empty containers are often filled with agricultural products that are ultimately shipped overseas, which increases cost efficiency by avoiding empty backhauls [48]. Port and city investments in harbor cranes, channel dredge operations, refrigerated cargo containers, and undeveloped land have contributed to the James River Barge Service’s success. The case study on the failed Albany Express Barge produced four lessons. First, economic models must include added costs associated with labor, lifts, and harbor maintenance taxes to ensure the service can compete with trucking. Second, developers must identify and market to product shippers that are not time-sensitive and seek to understand shippers’ service demands and needs. Third, communication among all stakeholders is critical to secure buy-in. Fourth, a future service should include a public–private partnership, like the partnership in developing the James River Barge Service.

3.2. Sample Selection

The population data comprising all shippers (more than 200) in the New York State Capital Region were collected from the Council of Supply Chain Management Professionals; port data were collected from those who had the potential to use SSS. A random sample of 155 was selected from the shipper data to send out the survey for quantitative data. After the completion of quantitative data collection, the final sample size with usable responses was 55. Hence, the quantitative data constituted a valid random sample of nearly 25% of the total population of shippers in the study area. The twelve shippers selected for the qualitative interviews were based on the key findings from the preliminary analysis of the quantitative data. The interviewees who participated in the IDIs had hands-on experience with SSS compared to the remaining 43 shippers in the quantitative survey. The motive behind collecting the qualitative data was to obtain a further understanding of factors affecting the current mode choice and shift to SSS. The qualitative data were representative of all shippers who used SSS along the Albany Hudson Corridor. The sample was also stratified across two criteria. (i) Industry sector: the sample must cover all industry sectors with potential for SSS. Hence, the sample comprised manufacturing, transportation, and warehousing firms. (ii) The establishment size: the respondents belonged to small, medium, and large firms. Moreover, the respondents were carefully chosen for each shipper, and the decision maker of the logistics operations was interviewed. Therefore, the dataset was reliable and representative of the population. Appendix A provides the validation of sample size and preliminary analysis.

3.3. Qualitative Data

The qualitative data contained IDIs with 12 respondents (See

Table 2. In-depth interview participant overview.

Sector	Firm	NAICS (6-Digit)	Description	Truck	Rail	Inland Water-Ways	Ocean	Air
Manufacturing	S1	321920	Wood container pallets & skids	√
	S2	325199	All other basic organic chemical	√	√			√
		325211	Plastics material and resin
	S3	331529	Other nonferrous metal foundries				√	√
	S4	333611	Turbine and turbine generator	√	√	√
	S5			√	√		√	√
	S6	335999	All other miscellaneous electrical equipment and component manufacturing	√				√
Transport & Ware-Housing	S7	488310	Port and harbor operations	√
	S8	488320	Marine cargo handling	√
	S9	488390	Support for water transportation	√	√
	S10	488510	Freight forwarding & customs broker	√	√
	S11			√			√	√
	S12			√			√	√

Note: NAICS = North American Industry Classification System. S = shipper.

). Each interview lasted approximately 60 min and included eight open-ended questions. Interview questions were designed to elicit detailed responses regarding shippers’ perceptions of their current mode of shipping, what factors they valued when considering a mode shift, and conditions they deemed necessary to consider a shift to SSS in the Hudson River corridor. Interviewees were assured that their identity and affiliation would be kept confidential to ensure candid responses. Interviewees were shippers in the New York Capital Region, who represented a wide range of industry sectors, including small-to-large domestic and international firms. Collectively, these firms employed approximately 6000 people, and their impact on New York state’s economy equaled an estimated USD 20 billion. The firms’ total annual revenue totaled approximately USD 6 billion. Six respondents (S1–S6) belonged to the manufacturing sector, and the others (S7–S12) were in the transportation and warehousing sector. Respondent S2 manufactured both organic chemicals and plastics. These firms were chosen for the interviews, as the freight modal share of these firms had a strong potential to be shifted to SSS. For instance, the respondents currently used a wide range of modes for shipping goods, including bulk cargo for longer distances. Nine shippers used multiple modes, and just three (S1, S7, S8) used one mode, i.e., truck. Other modes included rail (S2, S4, S5, S9, and S10), ocean (S3, S5, S11, and S12), and air (S2, S3, S5, S6, S11, and S12). Hence, the participants actively shared their views on the current mode choice, respective benefits, challenges, and recommendations to promote SSS as an alternative sustainable option. A brief overview of the interviewees’ profiles is provided in Appendix B.

3.4. Quantitative Data

The quantitative data were collected from online surveys from a random sample of 55 respondents, with a participation rate of 40%. The questionnaire comprised establishment details, modal characteristics, factors influencing the current mode choice, and other perspectives on SSS as an alternative mode. The final survey contained 20 items: 17 multiple-choice questions, 1 question with a 5-point Likert scale, 1 rank-order question that asked respondents to rearrange and rank multiple-choice options to a specific order, and 1 rank-order question where respondents ranked the three most important and three least essential factors related to a transition to an SSS service.

Table 3. Description of variables.

No	Variable	Description	Obs.	%
1	Predominant mode (Pred. Mode)	Truck	22	45.80%
		Vessel	11	22.90%
		Air	5	10.40%
		Intermodal	5	10.40%
		Train	2	4.20%
		Barge	1	2.10%
		Small Package	1	2.10%
		No response	1	2.10%
2	Domestic/International (Freight Type)	Domestic	12	23.08%
		International	8	15.38%
		Domestic and International	32	61.54%
3	Import Frequency (Import Freq.)	0	6	12.50%
		1–4	13	27.10%
		5–9	6	12.50%
		10–14	5	10.40%
		15–19	4	8.30%
		20–50	6	12.50%
		>50	8	16.70%
4	Export Frequency (Export Freq.)	0	12	25.00%
		1–4	13	27.08%
		5–9	3	6.25%
		10–14	5	10.42%
		15–19	1	2.08%
		20–50	5	10.42%
		>50	9	18.75%
5	Value of Freight (Value)	High (Electronics, Retail, Perishables)	25	50.00%
		Medium (Standard Freight)	22	44.00%
		Low (Bulk, Commodity)	3	6.00%
6	Industry Sector (Industry)	Accommodation & Food (72)	3	6.00%
		Agriculture, Forestry (11)	1	2.00%
		Construction (23)	4	8.00%
		Mining, Quarrying & Oil, Gas (21)	1	2.00%
		Manufacturing (31–33)	13	26.00%
		Retail (44)	1	2.00%
		Transportation and Warehousing (48–49)	17	34.00%
		Utilities/Energy (22)	5	10.00%
		Others	5	10.00%
7	Is current mode of shipping economical?	Yes	27	71.00%
		No	11	29.00%
8	% reduction in the transportation cost required for sustainable modal shift	Same cost; Better service	7	14.60%
		1% to 4%	5	10.40%
		5% to 9%	10	20.80%
		10% to 14%	13	27.10%
		15% to 19%	0	0.00%
		>20%	2	4.20%
		“Not up to me”	10	20.80%
		No response	1	2.10%

describes the eight predominant independent variables used in the quantitative analysis, and their respective short forms are in the parentheses “().” The first variable in Table 3 is the predominant shipping mode (Pred. Mode). Nearly half (46%) of the participants indicated “trucking”, with “vessel” representing the following highest means of transport at 23%. “Air” and “intermodal” accounted for 10% each. The second variable is the freight type (Freight Type), which shows whether the firm ships goods “domestically (24%)”, “internationally (15%)”, or “both (62%)”. The third and fourth variables show the import (Import. Freq) and export frequencies (Export Freq.), respectively, in the number of deliveries per week. The dataset covered firms with a wide range of imports, exports, or both. The fifth variable indicates the value of the cargo shipped, where half of the participants had “high value”, with 44%, showing their cargo value as “medium”. The value of goods is defined by the nature of the goods being shipped. High-value electronics, energy production, high-dollar technology-related equipment, a medium consisting of general cargo, and low consisting of bulk low dollar commodity products. The sixth variable is the industry sector based on the 2-digit NAICS (in parentheses). The seventh and eighth variables prove that nearly 71 thought their current mode was economical. At the same time, a modal shift would require a 5–9% reduction in transport costs for 21% of the respondents and 10–14% percent for 27% of the respondents.

Figure 2 shows the percentage of responses on “the key aspect the SSS mode should improve to become a viable alternative mode”. The major requirements to promote SSS were “lower costs”, from 26% of the respondents, and “proven-concept pilot program”, from another 25% of the respondents. With the proven-concept pilot program, the shippers would commit to using SSS only after a successfully demonstrated pilot program had been completed. The shippers do not want to commit to SSS with associated risk, only to have it discontinued as in the previous Albany Express Barge Program. They want to use a service that has demonstrated actual value to the users. Shippers clearly want to see demonstrated real value prior to signing on for SSS. A total of 6% of the respondents recommended reforming the 1920 Jones Act, which restricts maritime companies to using only domestically built vessels and policies that hamper the economic competence of SSS [49]. Another major factor was the lack of frequent routes for SSS. Lack of infrastructure, vessels, and shorter cycles (total travel time of a round trip) were the least important factors affecting the shift to SSS.

Table 4. Satisfaction levels with current mode.

No.	Attributes of Current Mode	Extremely Dissatisfied		Dissatisfied		Neither Satisfied or Dissatisfied		Satisfied		Extremely Satisfied
		Obs.	%	Obs.	%	Obs.	%	Obs.	%	Obs.	%
1 *	Accuracy	0	0.00%	7	14.58%	8	16.67%	29	60.42%	4	8.33%
2 **	Capacity	3	6.25%	6	12.50%	9	18.75%	27	56.25%	3	6.25%
3 **	Cost efficiency	3	6.25%	8	16.67%	21	43.75%	14	29.17%	2	4.17%
4	Delivery windows	1	2.08%	5	10.42%	17	35.42%	25	52.08%	0	0.00%
5	Flexibility and demand	1	2.08%	6	12.50%	22	45.83%	19	39.58%	0	0.00%
6 **	Dependability or predictability	1	2.08%	9	18.75%	16	33.33%	17	35.42%	5	10.42%
7	Public benefit	1	2.08%	3	6.25%	30	62.50%	12	25.00%	2	4.17%
8*	Quality	0	0.00%	2	4.17%	15	31.25%	28	58.33%	3	6.25%
9	Service (collaboration/follow-up)	0	0.00%	5	10.42%	13	27.08%	26	54.17%	4	8.33%
10	Sustainability (green supplychain)	0	0.00%	7	14.58%	28	58.33%	11	22.92%	2	4.17%
11	Speed (transport cycle time)	0	0.00%	6	12.50%	17	35.42%	21	43.75%	4	8.33%
12 *	Frequency of service	0	0.00%	1	2.08%	16	33.33%	25	52.08%	6	12.50%

Note: * Top 3 attributes with high satisfaction; ** bottom 3 attributes with low satisfaction.

contains a summary of the satisfaction levels with the current shipping mode, where the respondents ranked twelve attributes on a 5-point Likert scale (1 = extremely dissatisfied; 5 = extremely satisfied). The definition of attributes was as follows:

• Accuracy: In terms of meeting pickup and delivery appointment times;
• Capacity: The current mode has sufficient space for cargo;
• Cost efficiency: Cheaper for the service offered;
• Delivery windows: Supports specific delivery timings;
• Flexibility: Able to accommodate the changes in demand, market fluctuations, and seasonal variations;
• Predictability/Dependability: Meets commitments, does as expected;
• Public benefit: The current mode has fewer externalities toward public health and well-being, including congestion, longer waiting times, accidents, noise, infrastructure wear and tear;
• Quality: Indicates less damage;
• Service: Collaboration, planning, follow-up, friendly customer support, and issue resolution;
• Sustainability: Green supply chain, consumes renewable energy, fewer carbon emissions;
• Speed: Current mode is faster than others;
• Frequency: Available more often.

The attributes with high levels of satisfaction were “Accuracy”, “Quality”, and “Frequency of Service”, whereas “Cost Efficiency”, “Dependability/Predictability”, and “Capacity” ranked as the lowest levels. Hence, SSS can replace other modes as the former has higher capacity and is highly cost-effective. However, achieving higher “Dependability/Predictability” for SSS compared to other modes is challenging for policymakers. A majority (64.6%) of survey respondents cited “substantial cost savings” as the most valuable outcome from shifting to SSS, and 4.2% ranked “environmental benefits attributed to water transport” as the least valuable outcome. Other options included “improved service” and social benefits such as “congestion reduction”, “driver quality of life”, and “less road and highway infrastructure maintenance”.

Table 5. Switch to SSS, factors, and governing attributes. (A) Factors of High and Low Importance. (B) Importance of Governing Attributes.

(A)
No.	Factors Influencing Switch to SSS	High Importance				Low Importance
		Highest	Higher	High	Total	Lowest	Lower	Low	Total
1 *	Accuracy	28.3%	13.0%	17.4%	58.7%	0.0%	2.2%	0.0%	2.2%
2	Administrative Ease	4.3%	2.2%	2.2%	8.7%	19.6%	2.2%	4.3%	26.1%
3	Capacity	4.3%	8.7%	8.7%	21.7%	4.3%	0.0%	0.0%	4.3%
4	Cash flow	0.0%	2.2%	2.2%	4.3%	6.5%	10.9%	10.9%	28.3%
5*	Cost	34.8%	28.3%	13.0%	76.1%	0.0%	2.2%	0.0%	2.2%
6	Delivery windows	0.0%	2.2%	4.3%	6.5%	2.2%	6.5%	6.5%	15.2%
7	Flexibility/ /Seasonality	0.0%	4.3%	2.2%	6.5%	6.5%	8.7%	6.5%	21.7%
8	Frequency of Service	2.2%	8.7%	6.5%	17.4%	2.2%	2.2%	4.3%	8.7%
9 **	IT Systems	0.0%	2.2%	0.0%	2.2%	15.2%	10.9%	17.4%	43.5%
10 *	Predictability/Dependability	8.7%	13.0%	15.2%	37.0%	0.0%	0.0%	0.0%	0.0%
11 **	Public Benefit	4.3%	2.2%	2.2%	8.7%	28.3%	23.9%	17.4%	69.6%
12	Quality	4.3%	4.3%	2.2%	10.9%	2.2%	6.5%	10.9%	19.6%
13	Service	2.2%	4.3%	10.9%	17.4%	0.0%	2.2%	2.2%	4.3%
14 **	Sustainability	2.2%	0.0%	4.3%	6.5%	13.0%	19.6%	15.2%	47.8%
15	Transportation Cycle Time	4.3%	4.3%	8.7%	17.4%	0.0%	2.2%	4.3%	6.5%
(B)
No	Attribute	Importance given to shift from current mode to Short See Shipping (SSS)
		Very		Somewhat		Less		Least
		Obs.	%	Obs.	%	Obs.	%	Obs.	%
1	Leadership	26	55.32%	10	21.28%	6	12.77%	5	10.64%
2	Emergency	15	31.91%	20	42.55%	9	19.15%	3	6.38%
3	Public policy	6	12.77%	5	10.64%	14	29.79%	22	46.81%
4	Sustainability	0	0.00%	12	25.53%	18	38.30%	17	36.17%

Note: * Top 3 factors with high importance; ** bottom 3 factors with low importance.

summarizes the factors and governing attributes influencing switching to SSS from the current mode. The respondents were given fifteen factors (see Table 5A) and asked to rank the top three factors (“1” to “3”, where “1” indicates the highest importance) if improved would lead to choosing SSS as an alternative mode. Similarly, the respondents also ranked the three factors that were of least importance (“1” to “3”, where “1” indicates the lowest importance). There were three additional factors included based on the results from pilot tests in Table 5A from Table 4, namely, “Administrative Ease”, “Cash Flow”, and “IT systems”. “Cost”, “Accuracy”, and “Predictability” were the factors of high importance in the modal shift to SSS. Dependability/predictability provides an opportunity to trace the cargo, and estimated arrival times are mostly met. Unfortunately, the firms gave the lowest importance to “Public Benefit”, “Sustainability”, and “IT systems”. The table indicates that the other sustainable options, such as SSS, would be viable only if the factors of high importance (cost, accuracy, predictability, etc.) are relatively as reasonable as those of the current mode. Surprisingly, another factor of minor importance was he “cash flow”, which ensures that the payment process regularly transacts without delay in the fund transfer of payables, according to agreed terms.

Table 5B shows the ranking given by the participants across the following governing factors in switching from the current mode to SSS. These four factors are the most important factors based on the pilot tests conducted. This table lists the dependent variable for the quantitative analysis, i.e., the ordinal logit model estimates the probability that a firm chooses one of these attributes as importantly provided other factors mentioned in Table 3. A rank order question was created that asked respondents to rearrange and rank multiple choice options to a specific order, creating the following importance ranking:

• Leadership buy-in: Switch to SSS happens only based on the decisions made by the logistics leaders of the firm;
• Emergency: SSS would be used only in case of emergencies or catastrophes to the current mode of operation, examples being catastrophic infrastructure failure, weather, or accident-related shutdowns;
• Public Policy: A strong, supportive public policy is required for the SSS to become a viable option;
• Sustainability: The firm would shift to SSS if there are tangible benefits to the environment in terms of sustainability and green logistics.

The table shows that “leadership” was the most critical factor in shifting to SSS. The presence of a strong “Public Policy” and “Sustainability” were of less importance. Surprisingly, no respondent gave sustainability the highest rating, “very important”. Overall, SSS must provide a more accurate (in terms of meeting pickup and delivery appointment times), cost-effective, and predictable/dependable service and demands strong leadership for the firms to adopt.

4. Methodology

4.1. Qualitative Analysis

Thematic narrative analysis was used to interpret and extrapolate meaningful fragments of the IDIs from twelve experts in the field. This qualitative method provides a narrative on the subject matter and cognitive data points in each discussion [50]. Each interview lasted approximately 60 min and included eight open-ended questions. Interview questions were designed to elicit detailed responses regarding shippers’ perceptions of their current mode of shipping, what factors they valued when considering a shipping mode shift, and what conditions they deemed necessary to consider a shift to SSS in the Hudson River corridor. Interviewees were assured that their identity and affiliation would be kept confidential to ensure candid responses. Interviews were audio-recorded and transcribed using a professional transcription service. Transcripts were analyzed using thematic analysis following an inductive approach. Analysis began with multiple transcript readings to establish familiarity with the content. Next, coding involved assigning words and phrases to participant quotes. The codes were then grouped into categories based on similarities and patterns. Finally, themes were developed and refined before being finalized. Member checking was utilized to enhance internal validity.

4.2. Quantitative Modeling

The ordinal logit (OL) model was used to estimate the probability of the ranking given to the four key factors (leadership buy-in, emergency logistics, public policy, and sustainability/green supply chain benefits) affecting the shift from the current mode to SSS. The dependent variable (y_i) was an ordinal scale with four levels, varying from 1 to 4 (“1” indicates the most important, and “4” means the least important) for the importance given to various factors affecting the shift from the current mode to SSS (Table 5B). There were six independent categorical variables indicating the predominant mode (Pred. Mode) used, freight type (domestic, international, or both), frequency of imports and exports, the value of the commodity (high, medium, low), and the industry sector. Refer to Section 3.3, Table 2, Table 3 and Table 4, for a detailed description of the independent variables [51].

$$P\left(Y\le y_i\right)=e^{{\mu }_i-{\sum }_k{\beta }_k{X}_k}$$

(1)

where

• y_i = level of importance on an ordinal scale of 1 to 4 (1, 2, 3 and 4);
• µ_i = indicates the thresholds (cuts) between each level of the ordinal scale variable;
• β_k = coefficient (Coef.) vector;
• X_k = vector of independent variables.

5. Results, Analysis, and Policy Implications

This section presents the quantitative results supported by the findings from the qualitative analysis of the IDI with the shippers. Seven OL models were estimated for each of the four key aspects affecting the shift from the current mode to the SSS, i.e., leadership buy-in, emergency logistics, public policy, and sustainability/green supply chain benefits, as explained in Section 3.2. The independent variables were categorical and divided into six categories: predominant mode (Pred. Mode), freight type, import frequency, export frequency, value, and industry sector (see Table 3). A Z value close to one was taken as the benchmark for the statistical significance. These factors were divided into two sets. The factors in the first set were leadership buy-in and emergency logistics, which depended on the firms’ decisions (

Table 6. OL model for switch to SSS, leadership buy-in, and emergency logistics. (A) Leadership Buy-in. (B) Emergency Logistics. (C) Public policy. (D) Sustainability/Green Supply Chain Benefits.

(A)
Models /Variables		1. Pred. Mode		2. Freight Type		3. Import Frequency		4. Export Frequency		5. Value		6. Industry		7. All
		Coef	Z	Coef	Z	Coef	Z	Coef	Z	Coef	Z	Coef	Z	Coef	Z
Pred. Mode	Truck	−0.96	−1.23
	Waterways	−1.74	−2.07											−1.69	−2.32
Freight type	Domestic			−1.85	−2.55									−0.86	−0.94
	International			−0.89	−1.13
Import Freq	1–4					0.77	1.10
	5–9					1.02	1.03
	10–14					1.58	1.27							1.99	1.38
	15–19													−1.06	−0.91
	>50					1.30	1.35
Export Freq	1–4							1.53	2.02					1.24	1.33
	>50							1.32	1.45					0.87	0.83
Value	High									1.42	2.35			0.72	0.92
Cuts	4.0/3.0	−3.17	−3.99	−2.92	−4.72	−1.53	−2.68	−1.67	−3.31	−1.52	−2.89			−2.46	−2.90
	3.0/2.0	−0.15	−0.36	−0.10	−0.23	−0.17	−0.40	−0.17	−0.39	−0.14	−0.32			−0.03	−0.06
	2.0/1.0	0.11	0.37	0.20	0.67	0.11	0.36	0.14	0.50	0.12	0.40			0.41	1.38
GoF	Obs	46		46		46		46		46				46
	AIC	109.90		107.70		115.10		108.90		106.70				107.90
	BIC	119.00		116.80		127.90		118.00		114.00				126.20
	LL	−49.94		−48.85		−50.55		−49.44		−49.36				−43.95
(B)
Pred. Mode	Air	−2.01	−2.24											2.34	−1.96
	Intermodal	1.58	1.53											−2.60	−2.21
Freight type	Domestic			1.48	2.05
Import Freq	1–4					−1.41	−1.78							−1.17	−1.14
	5–9					−1.31	−1.47							−1.59	−1.39
	10–14					−2.10	−2.21							−3.03	−2.47
	>50					−1.99	−2.30							−2.64	−2.13
Export Freq	1–4							−2.82	−3.34					−2.74	−2.67
	5–9							−3.33	−2.79
	10–14							−1.18	−1.15
	>50							−2.48	−2.81					−1.78	−1.49
Value	Medium									0.92	1.57			−1.24	−1.49
Industry	Mining											−2.08	−1.29	−2.33	−1.25
	Retail											−0.94	−1.03
Cuts	4.0/3.0	−3.29	−4.78	−2.49	−4.12	−4.06	−4.92	−4.88	−5.44	−2.39	−3.87	−2.91	−4.55	−7.40	−4.81
	3.0/2.0	0.59	1.80	0.51	1.57	0.53	1.63	0.61	1.92	0.49	1.47	0.53	1.61	0.85	2.61
	2.0/1.0	0.66	3.25	0.64	3.19	0.71	3.45	0.90	4.13	0.61	3.05	0.61	3.04	1.01	4.66
GoF	Obs	46		46		46		46		46		46		46
	AIC	116.10		116.50		118.90		109.40		118.40		120.50		110.80
	BIC	125.20		123.80		131.70		122.20		125.80		129.60		134.50
	LL	−53.05		−54.23		−52.44		−47.70		−55.22		−55.24		−42.38
(C)
Pred. Mode	Truck	−0.44	−0.79											−0.94	−1.44
Freight type	International			0.28	0.32
Imprt Freq	15–19					0.84	0.73							1.90	1.37
Export Freq	1–4							0.93	1.28					2.29	2.47
	20–50							1.04	1.19					1.80	1.78
	>50							1.55	1.99					2.59	2.72
Value	High									−0.93	−1.65			−2.24	−2.89
Industry	Agriculture											2.64	1.68
	Manufacturing											1.00	1.41
	Transportation											1.39	1.96
Cuts	4.0/3.0	−0.28	−0.73	−0.06	−0.18	−0.04	−0.13	0.64	1.27	0.62	−1.41	0.67	1.35	−0.22	−0.35
	3.0/2.0	0.31	1.34	0.31	1.30	0.32	1.32	0.41	1.70	0.36	1.48	0.43	1.75	0.61	2.48
	2.0/1.0	−0.20	−0.46	−0.19	−0.43	−0.19	−0.43	−0.18	−0.41	−0.15	−0.36	−0.12	−0.29	−0.07	−0.17
GoF	Obs	46		46		46		46		46		46		46
	AIC	117.50		118.00		117.60		117.70		115.40		116.50		112.70
	BIC	124.80		125.40		124.90		128.70		122.70		127.50		129.20
	LL	−54.76		−55.02		−54.81		−52.84		−53.69		−52.26		−47.36
(D)
Pred. Mode	Air	2.22	2.03											1.61	1.33
	Truck	1.03	1.24											1.25	1.44
	Waterways	1.60	1.71											1.36	1.42
Freight type	International			0.25	0.36
Import Freq	20–50					−0.71	−0.85
Export Freq	exp_20_50							−2.24	−1.93					−2.26	−1.87
	exp_5_9							1.86	1.50					1.47	1.07
Value	Medium									1.41	1.11
	High									1.67	1.33
Industry	Construction											0.95	0.92
Cuts	4.0/3.0	0.45	0.63	−0.58	−1.75	−0.73	−2.18	−0.81	−2.30	0.81	0.68	−0.57	−1.78	0.25	0.33
	3.0/2.0	0.60	2.87	0.52	2.50	0.52	2.54	0.63	0.21	0.54	2.63	0.53	2.55	0.68	3.26
GoF	Obs	46		46		46		46		46		46		46
	AIC	104.70		105.70		105.10		99.78		105.80		105.00		102.90
	BIC	113.80		111.20		110.60		107.10		113.10		110.40		115.70
	LL	−47.33		−49.84		−49.54		−45.89		−48.89		−49.48		−44.47

). The second set, i.e., public policy and sustainability, required external intervention (

Table 7. OL model for switch to SSS, public policy, and sustainability. (A) Public policy. (B) Sustainability/Green Supply Chain Benefits.

(A)
Models /Variables		1. Pred. Mode		2. Freight Type		3. Import Frequency		4. Export Frequency		5. Value		6. Industry		7. All
		Coef	Z	Coef	Z	Coef	Z	Coef	Z	Coef	Z	Coef	Z	Coef	Z
Pred. Mode	Truck	−0.44	−0.79											−0.94	−1.44
Freight type	International			0.28	0.32
Imprt Freq	15–19					0.84	0.73							1.90	1.37
Export Freq	1–4							0.93	1.28					2.29	2.47
	20–50							1.04	1.19					1.80	1.78
	>50							1.55	1.99					2.59	2.72
Value	High									−0.93	−1.65			−2.24	−2.89
Industry	Agriculture											2.64	1.68
	Manufacturing											1.00	1.41
	Transportation											1.39	1.96
Cuts	4.0/3.0	−0.28	−0.73	−0.06	−0.18	−0.04	−0.13	0.64	1.27	−0.62	−1.41	0.67	1.35	−0.22	−0.35
	3.0/2.0	0.31	1.34	0.31	1.30	0.32	1.32	0.41	1.70	0.36	1.48	0.43	1.75	0.61	2.48
	2.0/1.0	−0.20	−0.46	−0.19	−0.43	−0.19	−0.43	−0.18	−0.41	−0.15	−0.36	−0.12	−0.29	−0.07	−0.17
GoF	Obs	46		46		46		46		46		46		46
	AIC	117.50		118.00		117.60		117.70		115.40		116.50		112.70
	BIC	124.80		125.40		124.90		128.70		122.70		127.50		129.20
	LL	−54.76		−55.02		−54.81		−52.84		−53.69		−52.26		−47.36
(B)
Pred. Mode	Air	2.22	2.03											1.61	1.33
	Truck	1.03	1.24											1.25	1.44
	Waterways	1.60	1.71											1.36	1.42
Freight type	International			0.25	0.36
Import Freq	20–50					−0.71	−0.85
Export Freq	exp_20_50							−2.24	−1.93					−2.26	−1.87
	exp_5_9							1.86	1.50					1.47	1.07
Value	Medium									1.41	1.11
	High									1.67	1.33
Industry	Construction											0.95	0.92
Cuts	4.0/3.0	0.45	0.63	−0.58	−1.75	−0.73	−2.18	−0.81	−2.30	0.81	0.68	−0.57	−1.78	0.25	0.33
	3.0/2.0	0.60	2.87	0.52	2.50	0.52	2.54	0.63	0.21	0.54	2.63	0.53	2.55	0.68	3.26
GoF	Obs	46		46		46		46		46		46		46
	AIC	104.70		105.70		105.10		99.78		105.80		105.00		102.90
	BIC	113.80		111.20		110.60		107.10		113.10		110.40		115.70
	LL	−47.33		−49.84		−49.54		−45.89		−48.89		−49.48		−44.47

).

Table 6 presents the OL models for leadership buy-in (6A) and emergency logistics (6B). Model 1 (Pred. Mode) in Table 6A shows that the “leadership buy-in” less influenced switching to SSS for firms with predominant mode as either truck or inland waterways. Nearly seven in 10 (68.8%, n = 33) study participants indicated they had the authority to alter their current shipping mode. It is reasonable that firms already inclined to use inland waterways (Models 1 and 7 in Table 6A) had a lower probability of choosing SSS despite their leadership. Economics drives the decision at the operational level [5,27,37]. S4, which is shipped by truck, rail, and water according to the destination site location, emphasized that a pilot program must show that SSS is not only cheaper but profitable without leading to increased risks. The bottom line is that it must be economically viable. Similarly, “Emergency logistics” had a weaker influence when the firms’ predominant mode was either air or intermodal (Model 1), i.e., firms were more likely to shift from truck to SSS only in case of emergencies. The responses during the in-depth interviews indicated that these areas of dissatisfaction and concern had been exacerbated by travel restrictions enacted by local governments to mitigate the spread of COVID-19. Such measures significantly compromised global supply chains, and the long-term effects remain unknown. Given that water modes of shipping are more energy efficient and less susceptible to fuel price increases than trucking, these findings also demonstrate an opportunity to address shippers’ needs with a multimodal option utilizing trucks and water.

Domestic firms had a high chance of choosing SSS in case of emergencies compared to the presence of leadership buy-in (Model 2). Leadership buy-in is an important factor in choosing SSS for firms with higher imports and exports per week (Models 3 and 4). In the case of “Emergency logistics”, firms with imports and exports of between 15 and 50 deliveries had higher chances of using SSS (Models 3 and 4). Compared to medium- and low-value cargo, firms shipping high-value goods require their leaders’ approval to shift to SSS (Model 5). One of the participants, who was shipping high-value cargo, explained in the IDI (S3) that they are afraid to use SSS unless there is approval from higher authorities since SSS has a higher risk of damaging goods. Those with medium-value commodities were more inclined to use SSS in emergencies (Model 5). The industry sector was found to be insignificant in the case of “leadership buy-in” (Model 6). At the same time, the mining and retail sectors showed less probability of using SSS for emergency logistics. Minimal discussions in the IDIs were related to proactive contingency planning and scenario analysis on the move to SSS to mitigate potential supply chain risks.

The OL models for the other two vital governing factors, public policy and sustainability, are presented in Table 7. “Sustainability” had only three levels (two, three, and four), and the highest ranking (=1) was not indicated by any participant. The firms with trucks as the predominant mode assigned lower importance to public policy for choosing SSS (Model 1). During the IDIs, half of the shippers expressed concerns about the infrastructure of “inland waterways” when discussing barriers to a mode shift to SSS. These participants suggested several improvements, including adding additional terminals, purchasing equipment with lifting capabilities, and increasing warehouse space and storage facilities. An improvement specific to the northeast included the need for services dedicated to breaking up winter ice on the Hudson River to avoid shipping delays. When considering improvements to inland waterways, policymakers must recognize the unique characteristics of sea transport instead of relying on the same approaches used to enhance roadway infrastructure [52]. “Sustainability” was given higher importance by air and waterway modes. Firms shipping internationally prioritized public policy and sustainability (Model 2).

Several participants in the IDIs referenced challenges associated with tractor-trailer access restrictions and heavy congestion on New York and New Jersey roadways. Several ranges of import frequencies were not statistically significant in either public policy or sustainability (Model 3). In contrast to sustainability, higher-frequency exporters required public policy intervention to shift to SSS, whereas lower-frequency exporters cared about sustainability while choosing SSS (Model 4). Several interviewees (10 out of 12) also discussed the need to consider freight rates in the context of port authority tariffs and road congestion and infrastructure in the New York and New Jersey areas. S2 stated the following when reflecting on freight rates:

“It’s probably a few of the port authority’s tariffs; the Port Authority of New York, New Jersey, sets the wharfage and dockages. You could go into economics, whether it’s taxes or whatever it is. So, you have stevedoring costs that are high, and then congestion drives waiting time. If I have a truck that must wait hours and hours to get loaded because it’s busy, you pay for that somewhere”.

High-value cargo shippers were less inclined to choose SSS despite public policy and sustainability (Model 5). S3, a company that predominantly shipped high-value equipment in this scenario, will not consider a mode shift if it increases the risk of damaging the cargo. Public policy should focus more on the agriculture, manufacturing, and transportation sectors to promote SSS, as these sectors are statistically significant in the OL model shown in Table 7 (Model 7A). When choosing between the current mode and SSS, the construction sector preferred sustainability more (Model 6).

Overall, the survey results indicated that shippers need lower operational costs and at least the same level of service to consider a mode shift. The findings from the IDIs indicated that these two factors did not stand alone and were considered within the context of other key factors (e.g., shipment distance, capacity, and product type). As noted by the shippers in this study, time-sensitive and just-in-time shipments are not ideal for the longer transit times associated with SSS service. Several interviewees (7 out of 12) also voiced concerns about unionized port labor and public policy. Unionized port labor has traditionally resisted the adoption of new operating practices. Requirements to pay stevedores overtime rates and outdated longshoreperson contracts contributed to the failure of the Albany Express Barge [53]. Therefore, future research is needed to better understand the organized labor reaction and its influence on the ability to shift modes and locations in potential operating negotiations. Overall, the shift to SSS requires all four governing factors. However, care should be taken to determine which sectors, modes, and sizes of the firms are targeted in SSS-related policies. These models provide a basis for making such decisions.

Policy Implications

This section describes potential strategies to support the sustainable mode shift to SSS based on the key findings from the models presented in Table 6 and Table 7, with support from the descriptive analysis from Section 3.3.

Incentives and subsidies: The models show that public policy is vital to the shift to SSS, especially for shippers who predominantly use trucks. Hence, the public sector strategies should aim at the specific industry sectors and business sizes that have a higher inclination to use SSS. Shippers belonging to agriculture, manufacturing, and transportation with more than fifteen deliveries a week must be the target stakeholders for these policies as these sectors displayed statistical significance in changing shipping mode to SSS with policy interventions. International shippers strongly require a policy change for SSS to work. The interviewees also showed severe concern pertaining to reforming the Jones Act, which has been a considerable hindrance to using water as a freight transportation mode.

Certification and recognition programs: Interestingly, although sustainability was rated as the least of shippers’ concerns compared to cost, accuracy, and reliability of the transport mode, there are a few exceptions where a certification program would be a great idea to promote SSS. International shippers, shippers using trucks and air as predominant modes, those with fewer deliveries (less than five per week), and those transporting high-valued cargo displayed significant preference toward SSS in a motive to move toward a greener supply chain. Hence, recognizing any efforts with ratings and certification programs exclusively for SSS would be a significant step toward encouraging these players to adopt SSS as a modal alternative.

Consolidation programs: Shippers indicated that the shipment size required for SSS is hard to meet for certain seasons when demand is low, which is augmented by high-priority shipments. Hence, third-party logistics providers specialized in consolidation and express shipping are required to sustain SSS as a reliable mode at any point in time.

Pilot programs: Based on the findings of this study, it is recommended that a proven-concept pilot program be executed to show that an SSS model in the Hudson River corridor will maintain or even improve the quality of service while decreasing operational costs. Several factors should be taken into consideration when designing a pilot program, including product type and value, frequency of service, and shipping distance. Policies should consider specific regional characteristics in the Hudson River Corridor, such as the freezing temperatures in the northeast, which make the Hudson River challenging to navigate in winter months; the required infrastructure; and the interaction with labor organizations.

6. Concluding Remarks

Despite being the backbone of the economy and prosperity, freight transportation presents a considerable threat to achieving a country’s sustainable development goals. It consumes vast amounts of fossil fuels and is a primary contributor to global pollution and climate change. One of the complex problems to solve is shifting from current shipping modes to a mode with lesser ecological impact, e.g., truck to rail or waterways and rail to waterways. The freight mode choice research has heavily concentrated on switching from truck to rail due to easy access to data. However, the use of waterways, especially short sea shipping (SSS), has rarely been studied. SSS involves relatively short-distance maritime transportation without crossing oceans. Since waterways are the most sustainable transport mode available, SSS has excellent potential to improve freight operations, particularly in a country like the U.S., which has a long coastal line and extensive navigable inland waterways.

Hence, this research is one of the unique attempts to investigate the possibilities of SSS as an alternative mode for shippers in the New York State Capital Region, which combined qualitative (narrative analysis of in-depth interviews, IDIs) and quantitative (descriptive analysis, and ordinal logit models) methods. The datasets comprised twelve IDI recordings and an online survey from over fifty shippers on freight operations, current mode choice, factors, and perspectives on SSS. Based on the findings from the pilot tests, the participants were asked to rank four governing factors (leadership buy-in, emergency logistics, public policy, and sustainability) affecting their choice of shifting to SSS. These factors were analyzed regarding shippers’ attributes such as current modes, imports, exports, industry sector, and commodity type. The findings from the qualitative research supported those of the quantitative analysis. The results also included a comprehensive description of influencing factors and policy implications. The IDIs showed that the cost and quality of service represent the top two factors affecting mode choice among shippers. The shippers surveyed for this research indicated that lower operational costs and at least the same level of service were both needed for them to consider a mode shift. The quantitative analysis proved that the shippers valued “leadership buy-in” the most and “sustainability” the least when using SSS. Firms using primarily trucks or inland waterways showed interest in SSS only in emergencies. High-value cargo shippers chose SSS only if the cargo’s safety (damage) is guaranteed. Public policy had a more substantial influence on firms in the agriculture, manufacturing, and transportation sectors. Sustainability was valued by international shippers and firms using air freight. Ultimately, economics drive the decision at the operational level, and shippers emphasized that a pilot program must show that SSS is not only cheaper but profitable without leading to increased risks. The bottom line is that SSS must be economically viable.

The modeling results and findings from this research provide vital inputs and tools to evaluate various policy outcomes on SSS. For instance, the ordinal logit models could be applied to the establishment census data, such as longitudinal business data, to estimate the acceptability and market share of SSS-related investments. However, this research has a few limitations and challenges that must be mentioned. The critical challenge common in freight research is the need for more data. A few firms are either currently using or planning to use SSS. Hence, finding a decent sample size for SSS-related data was the biggest challenge. Therefore, this quantitative part was complemented with additional qualitative analysis. Using mixed-methods approaches provides techniques to overcome small sample sizes by interviewing experts in the field to confirm model estimations and effects or point toward critical factors missing in model estimation. Such methods have been widely used in the literature when investigating transport mode choices [54,55,56]. The geographic scope was limited to the New York State Capital region, and the findings may not be replicable in another region. The majority of participants of this survey were large industries and freight forwarders with customers located close to the Hudson River Corridor. Hence, the proximity to the SSS facility should also be considered while using these models. As a future scope, a comprehensive stated-preference survey (with careful consideration of alternatives) would be of great value to further investigate SSS, its relevant factors, modeling efforts, and market shares.