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Abstract

This research focused on the offending diversity as an element of the corporate criminal career. Regulatory inspection data from four Dutch monitoring agencies collected between 2015 and 2020 were analyzed. First, the diversity index score (D) was calculated. Next, using Latent Class Analysis, the nature of the observed diversity was studied. The results show that ships and businesses fall into three categories. Those that are specialized, those with a tendency toward specialized behavior, and those with a tendency toward generalized behavior. No group with fully generalized offending behavior was identified. The Latent Class Analysis further revealed the clustering in the nature of offending behavior in this particular population. Potential explanations for offending behavior at distinct diversity levels are discussed.

Keywords

corporate crime offending diversity inland shipping latent class analysis

Introduction

Like natural persons, corporations must abide by wide and sometimes complex sets of rules and regulations. Specific offense types can differ between as well as within industries as they relate to different factors of daily practice (i.e., products, operations, and industry; Bottomley, 2022; De Jong & Herweijer, 2004). As a result, corporate crime research has covered a wide variety of offenses, including violations of administrative, environmental, financial, occupational health and safety legislation, instances of unfair trade, discrimination, antitrust, and product liability (e.g., Baucus & Near, 1991; Beckers, 2017; Clinard & Yeager, 1980/2017; Kluin, 2014; Simpson, 2019). The breadth of applicable regulations and the fact that corporations are often found to be repeat offenders (Hunter, 2021), raises the question whether corporations are typically diverse or not when it comes to their rule violating behavior.

Offending diversity is traditionally studied as an aspect of the criminal career of individual offenders (Blumstein, 2016; Blumstein et al., 1986; DeLisi & Piquero, 2011; Lynam et al., 2004; Petersilia, 1980; Simpson & Piquero, 2002; van Koppen, 2018), but has thus far received little attention from corporate crime researchers. Criminal career research shows that individual offenders vary widely in the diversity of their offending, with the opposing ends of the behavioral spectrum designated as generalized offending (highly diverse) and specialized offending (highly non-diverse) respectively. Rather than resulting from gradual between-individual differences in some general underlying characteristics, generalized and specialized individual offending have been suggested to require substantively distinct theoretical explanations (Lynam et al., 2004; Mazerolle & McPhedran, 2018).

Although other aspects of the criminal career have been translated from natural persons to corporate entities (Blokland et al., 2021; Hunter, 2021; Simpson, 2019, 2025; Simpson & Koper, 1992), the diversity of corporate offending has been studied scarcely so far. This despite the fact that, like in natural persons, the patterning of corporate offending diversity may suggest potentially different etiological factors for different behavioral patterns (Lynam et al., 2004; Mazerolle & McPhedran, 2018). At present, only two studies were found to directly address the diversity of offending in a corporate context; both were conducted in the Netherlands yet pertained to very different industries and applied different analytic approaches (Geelhoed, 2017; Kluin et al., 2025).

To add to this limited body of literature and to assess the generalizability of prior findings, the current study examines the diversity of corporate offending in a large Dutch industry, namely inland shipping. In the Netherlands, and elsewhere, inland shipping is a heavily regulated industry. As such, corporations and their ships are exposed to a wide variety of regulations of which compliance is frequently inspected. The current study first examines the level of offending diversity, answering the question: to what extent is offending in Dutch inland shipping at the ship and business level specialized or generalized? Second, the nature of the offending is further scrutinized by answering the question: to what extent and how are different offense types clustered together at the ship and at the business level? Answering these questions gives insight in the patterning of corporate offending behavior, guiding its further theoretical understanding and allowing for a better identification of its possible causes on different corporate levels. Unlike natural persons, corporations can consist of subunits, such as subsidiaries, branches or departments. Both applicable regulations and offending diversity may differ at the level of the corporation and the level of the subunit.

When corporate offending is found to be highly specialized, theoretical explanations might need to be specific as well, as such offending is most probably linked to factors specific to the actions that are required to comply with the specific regulatory provisions violated. Inversely, when offending is found to be predominantly generalized, theory may likewise best be general, as etiological factors should then be able to explain multiple types of offending behavior. For optimal effectivity and efficiency, interventions aimed at the root causes of corporate offending behavior should also be mindful of such a division. More generally, the juxtaposition between specialized and generalized offending patterns and their respective explanations reflects an ongoing debate on the need for either typological or general criminological theory (Blumstein & Cohen, 1987; Lynam et al., 2004; Mazerolle & McPhedran, 2018; Meester et al., 2025).

This article first discusses the context of Dutch inland shipping. Next, it will review literature on the diversity of (corporate) crime. To account for the limited body of literature, classical corporate crime studies are revisited to deduce the diversity of offending. The methodology section explains the use of the diversity index score and the latent class analysis. Following this, the results are presented and implications for policy and research are discussed.

Context

Inland shipping is an important industry for the Dutch economy as it provides the infrastructure that promotes the use of Dutch ports as transport nodes, facilitates related industries – such as the agricultural or utilities industry – and relieves other modes of transport. Dutch inland shipping has increased and is expected to continue its growth over the coming years (Harbers, 2022; Human Environment and Transport Inspectorate, 2020; Netherlands Institute for Transport Policy Analysis, 2021). The inland shipping industry has to comply with a complex legal framework. Compliance with at least twenty types of legislation pertaining to the vessel, port operations, cargo, crew, and shipping traffic is inspected regularly. Some of these rules apply to the entire industry, while others are associated with specific types of vessels or operations. Violations of these rules pose substantial physical, health, economic, and environmental risks (Human Environment and Transport Inspectorate, 2020, 2023). Estimated damages of collisions, illegal discharges and emissions, and health and safety incidents go well over fifty million euros per year (Human Environment and Transport Inspectorate, 2023). To mitigate these risks and prevent harms, various monitoring agencies carry out inspections to maintain and enhance compliance. The Human Environment and Transport Inspectorate (ILT) is the main regulatory inspection agency (Inland Shipping Act, art. 40; Inland Shipping Regulation, art. 10.1); others are: Rijkswaterstaat (RWS), the National Police (NP), the Port of Rotterdam (PoR), and the Port of Amsterdam (PoA). During an inspection, information on both the ship and, if applicable, the company is recorded to facilitate cooperation between monitoring agencies and to meet the ambitions of the involved monitoring agencies to reduce inspection load and apply risk-based regulation (Human Environment and Transport Inspectorate, 2018, 2020). The resulting longitudinal inspection data available for the Dutch inland shipping industry enable the analysis of criminal career dimensions such as the diversity of offending.

Diversity of Crime

For diversity to occur, offending must reoccur. The analysis of offending diversity is based on the assumption that past and future offending are somehow related and that offences are not stand-alone incidents. Offences are viewed as part of a behavioral pattern that is the criminal career (Mazerolle & McPhedran, 2018). Criminal career research – research on the sequence of individual offending over time (Blumstein et al., 1986) – has existed since the 1830s (Quetelet, 1831/1984). It further evolved with the adoption of birth cohort studies from the 1940s onward, resulting in the introduction of the “criminal career” concept in the 1970s (Blumstein & Cohen, 1987; Wolfgang et al., 1972/1987; Wolfgang & Tracy, 1982; see for a full history: Sullivan & Piquero, 2016). With the introduction of the criminal career concept came the recognition of several defining career aspects such as participation (including onset and desistance), seriousness (including escalation and de-escalation), length (persistence), and, as is the topic of the current study, diversity (Blumstein & Cohen, 1987; Blumstein et al., 1986; Petersilia, 1980).

Since then, the diversity of crime has been studied under a variety of names. It has been described as the offender’s crime mix, with crime switching being the behavioral process that contributes to the mix of offense types (Blumstein et al., 1986; Paternoster et al., 1998). Alternatively, it has been referred to as a dynamic process of specialization or generalization leading to the cross-sectional aggregates of specialized or generalized behavior respectively (Nieuwbeerta et al., 2011; Sullivan et al., 2009).

The general conclusion from these criminal career studies is that natural persons’ criminal careers are characterized by high levels of diversity (Farrington & Loeber, 2012). In a narrative review of available criminal career research, DeLisi and Piquero (2011) conclude that nearly all offenders are generalists. Though they also find evidence of intermediate periods of specialized behavior; overall, individuals’ criminal behavioral pattern seems characterized by an unpredictable assortment of offenses.

Diversity of Corporate Crime

In spite of the potential contribution to the understanding of causes of corporate crime and regulatory practice, research on the diversity of corporate crime is scarce; only two studies that specifically focused on offending diversity of individual corporations could be identified. Geelhoed (2017) qualitatively studied the “domain specificity” of rule violation in Dutch pig farming. Geelhoed found that participation in offending was high; twenty-four out of twenty-six farmers in her sample indicated that they had violated the rules once or more. Farmers did express that they were willing to comply, yet they also confided that economic or social strain could induce offending behavior. Offending in pig farming tended to be specialized. Offences were mostly limited to one of three domains of industry specific legislation. What type of legislation was violated, depended on corporate characteristics and farmers’ personal norms. For instance, not all farmers had to adhere to the same set of rules and regulations. As such, their possibilities to offend, and to reduce strain, were limited to legislation that was relevant to their products and operations. In addition, farmers sometimes choose to offend based on their personal judgement of particular (parts of) the legislation being unfair, unreasonable, and/or unhelpful.

Kluin et al. (2025) studied the diversity of offending in Dutch chemical industry by calculating a diversity index score. They found that offending in this industry is highly generalized (m: D = 0.84; min: D = 0.0; max: D = 1.0). These results seem to suggest that offending in Dutch chemical industry is associated with more general corporate characteristics, such as industrywide profit maximization or cultural deviance, and not with specific legal characteristics such as the complexity of the offended legislation.

While thus far these two studies are the only two in which the diversity of corporate offending is the main subject of analysis, it is possible to reuse the data from other studies on corporate crime to retrospectively calculate diversity index scores (cf. Kluin et al., 2025). To do this, it is required that a study includes either corporate-level or industry-level information on the frequency of offending per legislation type. Four studies with such quantitative information were found (Baucus & Near, 1991; Clinard & Yeager, 1980/2017; Simpson, 1986; Sutherland, 1949/1980). Sutherland (1949/1983) reviews the offending behavior of 70 large manufacturing, mining and trade corporations. The uncut version of the book provides the distribution of offending for each corporation. A total of 980 violations across six different legislation types were registered (p. 16–18). Applying the diversity index calculation to these data reveals an average diversity index score of 0.80. In other words, offending in Sutherland’s sample was mainly generalized with only four corporations displaying fully specialized offending (D = 0.0), and four corporations displaying fully generalized offending (D = 1.0). Clinard and Yeager (1980/2017) look at the violation histories of 582 corporations in 14 different industries. A total of 1415 violations across six different legislation types were reviewed (p. 340–341). Diversity index scores reveal that, in most industries, offending is generalized. Diversity is lowest in the oil refining (D = 0.42) and metal manufacturing industries (D = 0.42) and highest in the apparel (D = 0.94) and metal products industries (D = 0.95). In a study on the effect of profit-squeeze on offending behavior, Simpson (1986) analyzes the offending of 52 corporations in seven industries over a 55-year follow-up period. In this period, 477 violations across ten different types were made (p. 873). Offending in these industries turns out to be mainly generalized. Diversity is lowest in the motor vehicle industry (D = 0.78) and highest in the chemical industry (D = 0.92). Lastly, Baucus and Near (1991) study the predictability of corporate offending. They show that 88 firms across 33 industries (p. 17) have a total of 135 convictions based on four legislation types. Seventeen industries had multiple convictions. The average diversity index score for these industries was 0.66. In four industries (textile, metal manufacturing, metal products, and local passenger transit industries), offending was fully specialized (D = 0.0). In five other industries (apparel, rubber, stone, instruments and railroad transport industries), offending was fully generalized (D = 1.0).

Although these calculations are made with limited data, they suggest that corporate offending in most industries is general or at least shows moderate levels of diversity. They also indicate, however, that there is non-negligible variation in diversity both between and within industries. Given these contradicting results, formulating a clear a priori hypothesis on the level of diversity in offending in Dutch inland shipping seems premature at this point.

Methods

Sample

This research uses data on inland shipping violations collected by the Human Environment and Transport Inspectorate (ILT), Rijkswaterstaat (RWS), the National Police (NP), the Port of Rotterdam (PoR), and the Port of Amsterdam (PoA). The data are combined in the joined data system Inspection View Inland Shipping (Inspectieview Binnenvaart) and cover all inspections (N = 40,681) from March 2015 up until March 2020. Information on the ship, business, inspection, and, if applicable, any violations and interventions is recorded (Meester et al., 2024). Seventy-nine inspections are excluded due to missing ship-level identification data. As the National Police does not share information on the offence type, their inspections (N = 7987) are also excluded. Of the remaining 32,615 inspections on the ship level, 25,565 inspections (78.4%) have a registered Chamber of Commerce number or international alternative that can be used to aggregate offending information to the business level. The data cover 7142 ships and 3717 business. Inspections are recorded irrespective of whether an offence has been registered or not. In this study, ships and businesses without registered offences or with only one registered offence are excluded, as diversity cannot be calculated for these subjects. On the ship level, 4541 ships (63.6%) had registered offences. Of these, the offending diversity of 3106 ships (43.5% of the initial sample) with least two offences was analyzed. On the business level, 2559 businesses (68.9%) had registered offences. To reduce the overlap between the ship level and the business level analyses, the business level analysis only included businesses with at least two ships (and at least two offences). There were 1323 businesses (35.5%) with registered offences with at least two ships. Of these, 1042 businesses (28.0% of the initial sample) with at least two offences were analyzed.

Analyses

To answer the research questions, two analyses were performed on both the ship and business level. Both analyses were done in R (RStudio). In the first analysis, a diversity index was calculated for each ship and each business. This index reflects the level of diversity on an individual subject level (Farrington, 1986; Kluin et al., 2025; Nieuwbeerta et al., 2011; Piquero et al., 1999; Sullivan et al., 2009), indicating the probability of two random offences in the subject’s criminal career within the follow-up period being of different types. Diversity can be operationalized based on specific articles or sections of law, on particular laws and regulations, or on more general substantive themes or dimensions (Nieuwbeerta et al., 2011; Sullivan et al., 2009). The operationalization chosen consequently affects the analyses, results, and generalizability of the obtained results, as is illustrated by our analyses of historical datasets mentioned above. In the current study, diversity is operationalized based on the violated legislation; each legislation is regarded as a different offence type, whereas all offenses within a particular legislation are considered the same offence type.

The diversity index is calculated with:

(D i v e r s i t y i n d e x) D = 1 - \sum_{i = 1}^{N} {(O_{i})}^{2}

(1)

In formula (1), $N$ is the total number of different offence types by offender $i$ . $O_{i}$ is the percentage of offences of legislation $O$ in the offence history of that offender. As such, the index depends on the number of different offence types that are defined (here: offended legislations) and are actually inspected (assuming that violations of a certain legislation can only be registered in inspections in which adherence to this legislation was in fact reviewed). However, not all agencies inspect all legislations. To correct for this, $D$ is divided by the maximum possible value of the diversity index given the (combined) involvement of different monitoring agencies in a given inspection.¹ This correction corresponds with existing working instructions (Human Environment and Transport Inspectorate, 2025a, 2025b) The maximum diversity index given the inspected legislations can be calculated with:

D_{\max} | L = \frac{L - 1}{L}

(2)

In formula (2),

D_{\max} | L

is the maximum diversity index given the number of inspected legislations that follows from different combinations of monitoring agencies. Table 1 shows the observed combinations of agencies, the maximum number of inspected legislations by these agencies, the possible maximum diversity index, and the prevalence of such inspections in the sample.

Table 1.

Combinations of Monitoring Agencies, Assumed Inspected Legislation, Maximum Diversity Index, and Frequencies on Ship and Business Level.

Involved Monitoring Agencies	Number of Inspected Legislations (L)	Maximum Diversity Index (L-1)/(L)	Ships (n = 4541)	Businesses (n = 1323)
ILT	11	0.91	427	74
RWS	6	0.83	93	22
PoA	11	0.91	686	122
PoR	11	0.91	259	47
ILT, RWS	11	0.91	397	126
ILT, PoA	17	0.94	436	125
ILT, PoR	19	0.95	103	26
RWS, PoA	14	0.93	108	22
RWS, PoR	15	0.93	22	4
PoA, PoR	14	0.93	353	81
ILT, RWS, PoA	17	0.94	810	277
ILT, RWS, PoR	19	0.95	106	39
ILT, PoA, PoR	20	0.95	266	83
RWS, PoA, PoR	17	0.94	89	28
ILT, RWS, PoA, PoR	20	0.95	386	247

Formula (1) can be divided by formula (2) to give the following corrected diversity index:

D_{c} = \frac{1 - \sum_{i = 1}^{N} {(O_{i})}^{2}}{\frac{L - 1}{L}} = \frac{(1 - \sum_{i = 1}^{N} {(O_{i})}^{2}) \cdot L}{L - 1}

(3)

The corrected diversity index $D_{c}$ reflects the probability of two random offences being of a different type, independent from the number of different offence types that are defined and inspected. The outcome is a score between 0 and 1, where 0 indicates the absence of diversity (specialized behavior), and 1 indicates the presence of maximum diversity (generalized behavior). Scores below 0.5 indicate a tendency toward specialized behavior; scores above 0.5 indicate a tendency toward generalized behavior.

In the second analysis, a Latent Class Analysis (LCA) is performed using the “poLCA” package in R (Linzer & Lewis, 2011, 2022; Pratt, 2020). While the diversity index indicates the level of diversity, the LCA indicates the nature of that diversity. The LCA indicates how and to what extent different offence types cluster. Latent groups are distinguished based on shared patterns of offending, which is displayed using item response probabilities. Item response probabilities reflect the likelihood that a certain legislation has been violated (scored between 0 and 1). For this, a binary variable indicating whether a certain legislation has been offended or not is used instead of the number of offences for that legislation. (Lanza et al., 2003; Lanza & Rhoades, 2013; Schreiber, 2017; Vaughn et al., 2009; Weller et al., 2020). In contrast to cluster, factor, k-means, and hierarchical cluster analysis, LCA does not require normality or linearity (Fox & Farrington, 2012; Vaughn et al., 2009). Models for 2 latent classes up to 10 latent classes are estimated. Each estimation is based on multiple iterations of the same model to improve reliability (Pratt, 2020). The best model is selected using the Bayesian Information Criterion (BIC; Jones et al., 2001; Kass & Raftery, 1995; Killian et al., 2019; Weller et al., 2020), the Akaike Information Criterion (AIC; Neath & Cavanaugh, 2012), the likelihood ratio (G²) and chi square scores (χ²; Linzer & Lewis, 2011). Lower BIC and AIC scores indicate a better model fit. BIC focusses on specificity (less false positives), AIC focusses on sensitivity (less false negatives; Dziak et al., 2020). Here, a lower BIC score is preferred. The tables used for model selection are displayed in the appendix.

Results

Descriptives

In total, data from 3106 offending ships and 1042 offending businesses with at least two ships, inspected from March 2015 up until March 2020, were analyzed. There were 21,264 ship level inspections resulting in 15,629 offences. That amounts to averages of 6.85 inspections and 5.03 offences per ship. On the business level there were 13,915 inspections with 8718 offences. That gives 13.35 inspections and 8.37 offences per business on average. Table 2 shows the frequencies of the different offence types (i.e., legislations violated) on the ship and business level respectively. Violations of the Inland Shipping Act (BVW) are most common at both levels of analysis (Ship: 50.76%; n = 7934; Business, 48.97%; n = 4269). Next, violations of the Inland Shipping Police Regulations (BPR), the European Agreement Concerning the International Carriage of Dangerous Goods by Inland Waterways (ADN), the Shipping Traffic Act (Svw), the Working Hours Act (ATW), and the Ship Waste Decree (SAB) are most common, each with over 450 registered offences on the ship level.

Table 2.

Frequency of Offences per Legislation on the Ship and Business Level.

Legislation	Ship level		Business level
Legislation	n	%	n	%
Total	15,629	100	8718	100
Inland shipping Act (BVW)	7934	50.76	4269	48.97
Inland shipping police regulation (BPR)	3353	21.45	2065	23.69
European agreement concerning the international carriage of dangerous goods by inland waterways (int.: ADN)	1889	12.09	978	11.22
Shipping traffic Act (Svw)	671	4.29	380	4.36
Working hours Act (ATW)	556	3.56	323	3.70
Ship waste decree (SAB)	461	2.95	247	2.83
Ship waste treaty (SAV; int.: CDNI)	292	1.87	159	1.82
International safety guide for oil tankers and terminals (ISGOTT)	138	0.88	82	0.94
Water Act (wtw)	65	0.42	45	0.52
Dangerous goods transport regulation (WVGS)	51	0.33	27	0.31
Permits	45	0.29	41	0.47
Port management regulations	24	0.15	11	0.13
Environmental management Act (WM)	23	0.15	14	0.16
Rhine vessel inspection regulations (ROSR; int.: RVIR)	21	0.13	14	0.16
Working conditions Act (ARBO)	12	0.08	7	0.08
Inland shipping regulations (BVR)	6	0.04	8	0.09
Port authority check lists	7	0.04	0	0.00
Criminal code	1	0.01	0	0.00
Unknown	80	0.51	48	0.55

Note. Abbreviations are Dutch unless otherwise specified.

Diversity Index

The diversity index was calculated for each repeatedly offending ship and business. Figures 1 and 2 show the distributions of diversity index scores on each respective level. A score of 0 indicates maximally specialized behavior; a score of 1 indicates maximally generalized behavior. On both levels, two groups can be distinguished. There is one group with fully specialized behavior (D = 0) and there is one group with varying degrees of diversity (0.10 ≤ D ≤ 0.90). A group with maximally generalized behavior could not be distinguished.

Figure 1.

Distribution of diversity index score for offending ships with at least two offences (n = 3106).

Figure 2.

Distribution of diversity index score for offending businesses with at least two ships and at least two offences (n = 1042).

The average diversity on the ship level is 0.22. Maximally specialized behavior, D = 0, is found for 1208 ships with at least two offences (38.89%). On average, these specialized ships have 3.94 offences per ship. Of the remaining ships, 750 ships (24.15%) with 0 < D < 0.5 show a tendency toward specialized behavior and 1148 ships (36.96%) with 0.5 ≤ D < 1 show a tendency toward generalized behavior.

On the business level, the average diversity is 0.30. Maximum specialized behavior, D = 0, is found for 300 businesses with at least two offences (28.79%). These specialized businesses have 4.80 offences on average. Of the remaining businesses, 294 businesses (28.21%) with 0 < D < 0.5 show a tendency toward specialized behavior and 448 businesses (42.99%) with 0.5 ≤ D < 1 show a tendency toward generalized behavior.

Latent Class Analysis

Using Latent Class Analysis, the nature of the observed diversity can be assessed. The results show how different offence types cluster in a limited number of distinct corporate offending careers. Based on the Bayesian Information Criterion the three-class model is selected on the ship level and the four-class model is selected on the business level (Linzer & Lewis, 2011, 2022).

Figure 3 shows the results of the three-class model on the ship level. Class 1 is largest (47.52%). Class 1 is also most specialized with the item response probabilities demonstrating the clustering of a limited number of different offence types. All ships (100%) in class 1 have violated the Inland Shipping Act (BVW) in some way. The other classes have BVW violations as well, but to a lesser degree, yet showcase more diverse offending patterns. In class 2 (43.46%) offending probability is high for the Inland Shipping Police Regulations (BPR; 83%). Additionally, there is a high probability in this class to offend the European Agreement Concerning the International Carriage of Dangerous Goods by Inland Waterways (ADN; 51%), but not other legislations regarding the transport of dangerous goods. Ships in class 3 (9.02%) have a high probability to offend the Shipping Traffic Act (Svw; 85%), the Inland Shipping Act (BVW; 50%), the Inland Shipping Police Regulations (BPR; 49%), and the Working Hours Act (ATW; 47%). The offence types with the highest item response probabilities are also the legislations with the most registered offences (see Table 2). While class 1 is specialized in Inland Shipping Act offences, class 2 and 3 can be regarded as more diverse.

Figure 3.

Ship-level three-class latent class model. Class sizes and distribution of item response probabilities (IRP) between legislations (n = 3106; BIC = 22,283.83; 2ΔBIC = −744.08).

Figure 4 shows the results of the four-class model on the business level. In contrast to the ship-level results, offending on the business level seems more diverse. Businesses in class 1 almost exclusively offend the Inland Shipping Act (BVW; 100%), they only make up 38.83% of the sample. Businesses in class 2 constitute the largest class (44.82% of the sample) and have a high probability to offend both the Inland Shipping Act (BVW; 57%) and Inland Shipping Police Regulations (BPR; 87%). The smaller classes 3 (11.75%) and 4 (4.61%) both show diversity, but in different types of offenses. Class 3 has high probabilities for the Working Hours Act (ATW; 60%), Inland Shipping Police Regulations (BPR; 67%), Inland Shipping Act (BVW; 71%), Ship Waste Decree (SAB; 43%), and Shipping Traffic Act (Svw; 98%). Class 4 has high probabilities for the European Agreement concerning the International Carriage of Dangerous Goods by Inland Waterways (ADN; 100%), Inland Shipping Police Regulations (BPR; 96%), Inland Shipping Act (BVW; 100%), Ship Waste Decree (SAB; 59%), and Ship Waste Treaty (SAV; 44%).

Figure 4.

Business-level four-class latent class model. Class sizes and distribution of item response probabilities (IRP) between legislations (n = 1042; BIC = 8593.60; 2ΔBIC = −54.20).

Discussion

This study examined to what extent offending in Dutch inland shipping is specialized or generalized and what the level and nature of offending diversity is. The offences of 3106 ships and 1042 business over a five-year period were analyzed. The diversity of corporate offending was calculated with respect to nineteen types of legislation regarding the ship, port, crew, shipping traffic, or other subjects inspected by the Human Environment and Transport Inspectorate, Rijkswaterstaat, the National Police, the Port of Rotterdam, and the Port of Amsterdam.

The diversity index scores and the Latent Class Analysis indicate that there is both specialized offending and varying levels of diversity within Dutch inland shipping. Based on these findings, ships and businesses in Dutch inland shipping can be categorized into three distinct groups: ships and businesses that are specialized, those with a tendency toward specialized behavior, and those with a tendency toward generalized behavior. The Latent Class Analysis further revealed (the distribution of) clustering in the nature of offending behavior in this particular population. Specialized offending behavior on both the ship and business level predominantly involves Inland Shipping Act violations. Other types of violations mostly occur in the classes with a more diverse offending pattern. From this, potential explanations can be derived. Specialized offending behavior indicates involvement in distinct types of offending. In line with typological theories, specialized offending behavior may best be explained by factors that are specific to the particular type of offending. In contrast, generalized offending seems to indicate an indifference in offence types, which may require a more comprehensive explanation, such as those put forward in general criminological theories, which would apply to a wide(r) range, if not all, types of offending behavior (Lynam et al., 2004; Mazerolle & McPhedran, 2018).

Typological theories would argue that specialized offending results from specific features of the legislation involved. Motives for specialized offending may relate to the impracticality, complexity, or ambiguity of the particular legislation or unclear and inconsistent enforcement of that specific legislation (Kluin et al., 2025; Meester et al., 2025). On both ship and business level specialized offending in Dutch inland shipping mainly involved offenses of the Inland Shipping Police Regulation and Inland Shipping Act. The Inland Shipping Police Regulation and Inland Shipping Act prescribe a wide array of rules that may additionally vary depending on specific products and operations. This can result in impracticalities or ambiguities for potential violators. In addition, responsibilities for supervision are shared and sometimes divided between agencies (as illustrated in Table 1), which may result in inconsistencies in monitoring or enforcement.

To explain the more diverse offending patterns, Meester et al. (2025) suggest considering more general offender characteristics and internal motives for offending, covering the multiple offence types. Accordingly, in inland shipping, ships and businesses with a tendency toward generalized behavior might be motivated by a more general criminogenic corporate culture (i.e., little attention to safety norms), business strategies influenced by the strains of the inland shipping industry as a whole (i.e. low profit margins) or the business’ structure within the industry (i.e., small businesses in a complex industry; Huisman, 2016).

Lastly, there is an intermediate group of ships and businesses displaying some variety in offending, but with a tendency toward specialized behavior. The identification of this group underscores the importance of a diversity analysis with which intermediate scores can be detected as to prevent a too simplistic specialized-generalized behavior dichotomy. Explanations for low but non-zero levels of diversity may result from a combination of those factors resulting in specialized or generalized behavior. However, low but non-zero levels of diversity may also result from behavioral changes over time. Offending actors may display periods of either specialized or generalized behavior (cf. DeLisi & Piquero, 2011). Transitions from one offending pattern to the other are not captured by the current cross-sectional application of the diversity score. To address this problem, the process of specialization and generalization should be reviewed through longitudinal analyses (cf. Nieuwbeerta et al., 2011).

As the Latent Class Analysis identified groups on the level of companies that differed in number and content from those at the ship level, explanatory variables may also differ between levels of corporate structure (i.e., corporation or facility).

Although at present, the exact relation between specific types of corporate violations and specific causes and motives in inland shipping has yet to be determined. Detailed descriptions of offending diversity based on registration data as in the current study, only make some explanations more or less likely a priori, and suggest whether these explanations are likely to be typological or rather general in nature. Future quantitative research (i.e., regression analyses, multilevel modeling) for instance, could assess the relation between offending diversity and factors such as legal complexity, enforcement practice, or link diversity to self-reported knowledge deficits, resistance, economical strain, emotions, indifference, and negligence. Qualitative analyses (i.e. interviews), could then be used to assess the appropriateness of the explanations suggested by quantitative analyses.

Inherent to diversity research is the need to clearly define the concept of diversity. Herein lies the first limitation of this study. Diversity in this study has been operationalized as (the number of) different offended legislations, as opposed to legal articles, paragraphs or themes within a given legislation. Especially if a single legislation covers a broad array of topics, the current operationalization may structurally underestimate the level of offending diversity. Definitional differences furthermore hinder the synthesized evaluation of findings across studies (Sullivan et al., 2009). For instance, Geelhoed (2017) defined diversity as offences in different legal domains that were, by nature, exclusively relevant for the population under her scrutiny, while Kluin et al. (2025) reviewed elements or subjects within a sector-specific legislative framework. For meaningful between-industry comparisons adopting the same or highly similar definitions of diversity, based on thematic or theoretical aggregates of the offending behavior and the harms associated with those behaviors, is required. Conversely, an entirely legalistic operationalization, based on legal articles, improves within-industry analysis, but may limit comparisons with studies looking into other industries with different legal frameworks. Note that, since the legal framework in criminal career research on natural persons (i.e., the criminal code) applies to all individuals, these considerations are unique to corporate criminal career research.

Regardless of the exact operationalization chosen, diversity is best understood in terms of its distribution; do all corporations in a certain industry cluster at either high or low levels of diversity, or can different subgroups be distinguished? A narrow distribution of diversity scores in a particular sample may signal that a general explanation for offending is most applicable in the industry under scrutiny, while a wider or bimodal distribution may signal the need for a typological approach.

A second limitation to the current study was that it was not possible to correct the diversity index score for the exact number of defined and inspected offense types as outlined in an inspection agenda (cf. Kluin et al., 2025). Such a correction would partly account for the fact that not all types of legislation apply to all (potential) offenders, and the possibility that not all types of legislation are inspected or enforced with equal effort. Instead, an alternative correction, based on the informed assumption that each involved inspection agency always inspects all legislations under their responsibility, was carried out; an assumption that is in line with current work instructions (Human Environment and Transport Inspectorate, 2025a, 2025b). Still, unobserved divergence from these instructions by inspectors in practice may negatively affect the validity of the current results. Regulatory studies indeed show that certain subjects or offenses are often prioritized over others based on policy, financial, and political factors. This also applies to inspection and supervision (Dal Bó, 2006; Laffont & Tirole, 1991; Mascini, 2013; May & Winter, 2011; Short, 2021). Thus, both for the purpose of research as for the purpose of agenda setting, future data recordings of offending should systematically include which inspection topics are scheduled for each inspection. This allows for distinction between legislations that are “not inspected”, “inspected, without observed offence”, and “inspected, with observed offence” (cf. Kluin et al., 2025). Additionally, this enables the analysis of inspection diversity, which can, due to aforementioned prioritization, be specialized or generalized similar to offending (Apel & Nagin, 2014; Weisburd & Eck, 2004). Note that, unlike the aforementioned difficulties in operationalizing offending diversity, the latter issue is not specific to corporate criminal career research.

The application of diversity analysis to corporate offending may help further develop efficient and effective supervision and enforcement (Custers, 2014; Van der Voort et al., 2020). Targeted interventions seem especially appropriate for the specialized offender group in which risks for certain offence types can be determined, while diverse offending requires more general interventions aimed at multiple offence types and shared causes (Lynam et al., 2004; Mazerolle & McPhedran, 2018; Meester et al., 2025; Piquero, 2000).

Conclusion

This research provides an initial step toward studying the nature of offending in Dutch inland shipping, an underexplored, yet criminologically relevant sector experiencing economic growth and increasing risks. Findings from the diversity index and the Latent Class Analysis indicate that different groups of violators can be identified: those fully specialized, those with a tendency toward specialized offending behavior, and those with a tendency toward generalized offending behavior. Full generalized offending behavior was not found. The various latent classes distinguished exhibit different combinations of offended legislations. In future diversity analyses, diversity may be operationalized to allow for between-industry comparisons. This would require a theoretical classification of violations, based on, for instance, the type of harm the rule is aiming to mitigate, the type of behavior (or non-behavior) required from the corporation subjected to the rule, or the costs involved in being compliant. Within the limits of the available data and operationalizations chosen, the results presented here provide new insights into the diversity of corporate offending, revealing distinct offending patterns in Dutch inland shipping industry, which, in turn, may inform and enhance the practice of fitted supervision, inspection, and intervention.

Supplemental Material

Supplemental Material - The Diversity of Corporate Offending. A Case Study on Dutch Inland Shipping

Supplemental Material for The Diversity of Corporate Offending. A Case Study on Dutch Inland Shipping by The Diversity of Corporate Offending. A Case Study on Dutch Inland Shipping in Journal of White Collar and Corporate Crime

Footnotes

Acknowledgments

We would like to thank Drs. Margje C. M. Schuur and Dr. Stephanie I. Wassenburg from the Human Environment and Transport Inspectorate (The Netherlands) for their contributions to the conception of this research and their extensive comments on previous versions of this paper.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research project is being funded by the Dutch Research Council (NWO) (grant number 406.18.R8.039). Additional funding was provided by the Human Environment and Transport Inspectorate.

Declaration of Conflicting Interest

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

ORCID iDs

Jelmar J. Meester

Marieke H. A. Kluin

Arjan A. J. Blokland

Wim Huisman

Data Availability Statement

Due to the sensitive nature of the data, no data will be published. Data can be reviewed through the corresponding author. Data were provided by the Human Environment and Transport Inspectorate. Privacy and safety concerns were considered.*

Supplemental Material

Supplemental material for this article is available online.

Note

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