SemOpenAlex |

SemOpenAlex

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2958633917> ?p ?o ?g. }

Showing items 1 to 66 of 66 with 100 items per page.

W2958633917 endingPage "28" @default.
W2958633917 startingPage "1" @default.
W2958633917 abstract "Few subject areas span as many disciplinary boundaries as does corporate crime. Since Sutherland's famous Presidential Address to the American Sociological Association in 1939 and subsequent publication of White Collar Crime ten years later, business scholars, economists, sociologists, political scientists, lawyers, and psychologists, and criminologists have speculated not just about the etiological origins of corporate crime but about the success of various strategies for its prevention and control. Yet, scholars and policy-makers know very little about “what works, what doesn't, and what's promising” in this area. This is due to several related issues: (1) the ambiguity, scope, and complexity of the subject matter; (2) little systematic program or policy evaluation; (3) a lack of readily available and accessible data for research purposes which ultimately affects (4) the type and quality of research in this area. Beginning with the first point, it is useful to define what we mean by corporate crime. Braithwaite (1984:6) describes corporate crime as “the conduct of a corporation, or of employees acting on behalf of a corporation, which is proscribed and punishable by law.” Corporate crime, therefore, encompasses a wide array of illegal activities that are criminally, civilly, and administratively proscribed and which may be undertaken by individual managers/employees as well as by the firm (as an organizational actor). Corporate crimes generally are distinguished from other types of white-collar offenses by the use of organizational resources and by who gains from the offense. Thus, when Raymond Scott Stevenson, head of Tyco's tax department, directed a series of transactions designed to reduce Tyco's state tax liability by back-dating transactions to avoid reporting a $170 million dollar federal capital gain, he used organizational resources to “benefit” the company's bottom line.6 This distinction between white-collar and corporate offending is by no means unambiguous. For instance, a top manager may utilize organizational resources to enrich him or herself–described as “collective embezzlement” by Calavita and Pontell (1991). In addition, although many acts of corporate crime are undertaken to achieve organizational goals, such acts may indirectly benefit the individual through promotion or salary bonuses. However, in accordance with Braithwaite's definition and consistent with our focus on corporate deterrence, we are interested in the kinds of behaviors typically characterized as “corporate” and not “white-collar” offenses where the motivation for offending is organizational, not personal. It is useful to categorize the kinds of offenses that meet our definitional criteria. Broadly conceived, corporate crimes in the United States7 can include the following categories of offenses: administrative noncompliance, environmental violations, financial violations, labor violations, manufacturing violations, and/or unfair trade practices (Clinard and Yeager, 1980:113-116). Similar to classifications of street crimes (e.g., violent crimes), each category contains a variety of specific offenses, often with distinct laws that define illegalities and provide remedies and sanctions for violators. For instance, unfair trade practices include monopolization, price-fixing, unfair advertising, and price discrimination, among other illegal activities (Simpson, 1986). The Federal Trade Commission Act, Robinson-Patman Act, and the Sherman-Clayton Antitrust Act are some of the more significant pieces of legislation that define what constitutes unfair trade practices and the range of penalties for violators. Environmental violations are classified by different media (e.g., air, water, land) and statutes (e.g., Clean Water Act, Clean Air Act, Resource Conservation and Recovery Act). Similar to anti-competitive illegality, some of these practices are defined as criminal offenses while others fall within the civil-administrative realm. While many corporate offenses are financial, others are “violent” in nature, where human lives are lost and individuals injured (for instance, Occupational Health and Safety Administration violations). A key feature of corporate offending is crime complexity. Although some offenses may be quite simple (bribery or accounting fraud), others often involve multiple interconnected actors and organizations, occur over long periods of time, and entail manipulating shell companies and billions of dollars (such as Enron). Corporate crimes also vary by seriousness. Egregious offenses can carry substantial criminal and civil sanctions while others are fairly minor “technical” violations (e.g., failure to submit a report to a regulatory agency). While definitional murkiness, breadth, and complexity make the phenomenon difficult to study, there are other barriers to research as well. Perhaps the most salient barrier to research lies with the lack of high quality data. There is no UCR-like national data base that can be used to “measure” the corporate crime problem, nor are there any systematic procedures for identifying the “hidden” figure of crime. Most studies of corporate offending are qualitative, case study investigations of sensational events. There are only a handful of systematic scientific studies of corporate offending (including Sutherland's original study) because most federal agencies that fund criminological research (e.g., National Institute of Justice) focus on “street” crime. These agencies are also more apt to fund evaluation research on programs and policies in these same areas. We therefore have learned a great deal about the successes or failures of drug courts, boot camps, or gun seizures, but relatively little about whether internal compliance systems (such as ethics training, randomized audits, hotlines) reduce illegal behavior by companies or if criminal prosecution promotes corporate deterrence and compliance better that civil litigation or regulatory interventions. Because the subject matter crosses so many disciplinary boundaries, there are studies and evaluations outside of criminology and criminal justice that can inform a systematic review in this area. In addition, because a review of this type has never been conducted, it is essential to learn where empirical studies are concentrated (i.e., what kinds of interventions and outcomes) to assess the empirical quality. We began this work at a very general level, conducting an extensive search of the white-collar and corporate offending literatures without regard to specific types of crime prevention or intervention strategies.8 Consequently, we have already completed the overall search of the published and unpublished literature and have found that many studies examine the effectiveness of legal restraints (including laws, official sanctions, and regulatory actions). Therefore, we have narrowed the scope of this protocol to that particular domain. Our overall objective is to identify and synthesize the extant empirical literature on formal legal and administrative prevention and control—i.e., the actions and programs of government law enforcement agencies, legislative bodies, and regulatory agencies. This review will consider all types of legal and regulatory practices as long as corporate crime prevention is part of the outcome. Other outcomes and information, if relevant, will also be collected. Second, we need to assess the “quality” of this evidence (i.e., the kinds of studies and data that exist to answer our research questions) to determine whether a meta-analytic review is possible in this domain. Once we have retrieved and fully coded relevant publications (including the calculation of effect sizes), we plan to focus on the effectiveness of the identified strategies and programs. Specifically, we will address the following questions: As part of a larger study attempting to identify the universe of studies on prevention of corporate crime (see footnote 8), we will conduct a search for articles using a broad set of search terms (see section 3.2) and gleanstudies that involve both corporate crime behaviors and are empirical (including studies using either quantitative and qualitative methods) in nature. After retrieving those articles, we will further cull the articles to find those we consider to be eligible for coding. Eligible articles are those that meet the following criteria: Our search will include published and unpublished articles, reports, documents, and other readily available sources. The studies will be identified via an exhaustive search of multiple online data bases and other sources using 74 search terms. These databases and legal/deterrence search terms are described below. In addition to the online searches, we will review the bibliographies of seminal articles/books that address corporate crime deterrence, prevention, and control. We also plan to e-mail the final list of articles deemed eligible for coding to leading corporate crime scholars in case we have missed other important sources. The databases used in our search for published articles include: After conducting the search for published documents described above (including reviewing the articles' bibliographies and later articles citing eligible studies in Web of Science), we will conduct subsequent searches for unpublished and missed published documents in the following sites: The search terms used to collect studies from the above databases are given below: Sanction and Accounting Fraud Sanction and Anti-competitive Behavior Sanction and Antitrust Sanction and Business Corruption Sanction and Business Crime Sanction and Business Misconduct Sanction and Business Violations Sanction and Corporate Corruption Sanction and Corporate Manslaughter Sanction and Corporate Crime Sanction and Corporate Misconduct Sanction and Corporate Violations Sanction and Environmental Crime Sanction and Health Care Fraud Sanction and Organizational Corruption Sanction and Organizational Crime Sanction and Organizational Misconduct Sanction and Organizational Violations Sanction and Securities Fraud Sanction and Ethical Business Culture Sanction and Unethical Conduct Sanction and Unethical Behavior Sanction and White Collar Crime Fine and Accounting Fraud Fine and Anti-competitive Behavior Fine and Antitrust Fine and Business Corruption Fine and Business Crime Fine and Business Misconduct Fine and Business Violations Fine and Corporate Corruption Fine and Corporate Manslaughter Fine and Corporate Crime Fine and Corporate Misconduct Fine and Corporate Violations Fine and Environmental Crime Fine and Health Care Fraud Fine and Organizational Corruption Fine and Organizational Crime Fine and Organizational Misconduct Fine and Organizational Violations Fine and Securities Fraud Fine and Ethical Business Culture Fine and Unethical Conduct Fine and Unethical Behavior Fine and White Collar Crime Regulatory Policy and Accounting Fraud Regulatory Policy and Anti-competitive Behavior Regulatory Policy and Antitrust Regulatory Policy and Business Corruption Regulatory Policy and Business Crime Regulatory Policy and Business Misconduct Regulatory Policy and Business Violations Regulatory Policy and Corporate Corruption Regulatory Policy and Corporate Manslaughter Regulatory Policy and Corporate Crime Regulatory Policy and Corporate Misconduct Regulatory Policy and Corporate Violations Regulatory Policy and Environmental Crime Regulatory Policy and Health Care Fraud Regulatory Policy and Organizational Corruption Regulatory Policy and Organizational Crime Regulatory Policy and Organizational Misconduct Regulatory Policy and Organizational Violations Regulatory Policy and Securities Fraud Regulatory Policy and Ethical Business Culture Regulatory Policy and Unethical Conduct Regulatory Policy and Unethical Behavior Regulatory Policy and White Collar Crime The first task involving these searches is to keep track of the number of “hits” each search term reveals within each data base. Next, we will review all titles and abstracts to determine: (1) whether the article is relevant to our study; and (2) whether the article is quantitative or not. Next, we will sort the empirical articles by keywords across search engines to eliminate article redundancy between search engines. We will then identify articles that are eligible for complete coding based on the criteria defined in section 3.1. We include studies that use a wide variety of methods, but will concentrate on identifying studies in which a treatment group that was subject to a specific legal restriction was compared to a control group that was not. Studies can be experimental, quasi-experimental, or pre-post evaluations. We will also include observational studies in which groups were constructed by natural means (e.g., analyzing adjacent jurisdictions). In the case of observational data, we will include studies that report standardized regression coefficients or Pearson correlations as well as those that have enough information to allow the calculation of an effect size. The studies included will include various samples, including individuals (e.g., employees, students, CEOs), corporations, or geographical areas. These different units of analysis will be kept separate for the purpose of our analyses. Given our definition of corporate crime9, the outcome variables included in our study will be very broad. Some examples of the outcomes (but not an exhaustive list) include: variations in pollution emissions, official records of compliance with regulations (e.g., environmental, employment, OSHA), recidivism, safety violations/compliance, number of financial transactions, perceived intentions to offend, perceptions of ethicality of behaviors, injuries from safety violations or environmental accidents, convictions, citations, noncompliant inspections, compliance measures (e.g., self-ratings), accuracy of regulatory records, complaints (e.g., about consumer fraud), and perceptions of enforcement effectiveness. Many studies report more than one outcome that is relevant to our domain of interest and many authors publish more than one article using data from the same sample. In order to statistically analyze our coded articles properly, we must make sure that the effect sizes we calculate come from independent samples. To ensure that this is the case, we will enter the articles into a data file (using Microsoft Excel). As the coders code the articles, they will note where a sample may have overlapped with another study. For each study, we will differentiate truly unique outcomes derived from the same sample, and then will combine multiple effect sizes describing the same outcome from the same sample. Before completing our analyses, we will review all of the sample characteristics from the population of studies to verify that any effect sizes from different studies utilizing the same sample are combined for our final analysis. We created a coding protocol for the larger systematic review (in which we were searching the entire domain of corporate crime prevention/deterrence research) that included the specific treatment variables we are interested in here—legal restraints. The entire coding protocol is attached as Appendix A. In this document, the variable named “TREATMENT” (p. 18 of the current document) provides all potential descriptions of the treatment program; those treatments falling under 2. Law, 3. Official Sanction/Fine, or 4. Regulatory Policy are the most relevant to the current discussion. The protocol includes codes used to describe the source of the study (Section I of Appendix A; e.g., country of publication, journal's disciplinary area), characteristics of the study (Section II; e.g., randomized experiment or not, start/end date of data collection, concerns about validity), sample characteristics (Section III; e.g., whether individuals or corporations), the methods and procedures used by the study authors (Section IV; e.g., use of a control group), descriptions of the independent variable (Section V; e.g., construct and operationalization), descriptions of the dependent variable (Section VI; e.g., construct and operationalization), effect size data (Section VII; i.e., coding the data provided that will be employed to calculate an effect size), and then conclusions made by the study authors (Section VIII). There are also shaded boxes at the very end that describe the various types of effect sizes and relevant statistics needed for future analysis. Articles will be coded by two coders, who will input all data into a Microsoft Excel spreadsheet. An initial coding session was completed in which 80 articles collected at that point were coded by two coders. Based on initial coding, inter-rater reliabilities were calculated. Coders would then resolve differences between the two databases. Often, this collaboration would result in decision rules which are provided at the very bottom of the codebook. After reviewing approximately 80 articles in this manner, an acceptable inter-rater reliability was established for most variables (those not reaching either a Kappa value or Pearson correlation value of 0.70 will not be used in further analyses). The coders will split the rest of the articles for independent coding. No changes were made to the coding sheet after an acceptable inter-rater reliability was established and no further decision rules were necessary. Due to the breadth of the outcomes included in our systematic review, we will likely be coding various forms of data that will result in multiple types of effect sizes being calculated. For example, dichotomous outcomes will likely be calculated as an odds ratio, while continuous outcomes in a two-group comparison will likely result in a standardized mean-difference effect size. However, we also have data in which both the independent variable and dependent variable are continuous; such data is used to calculate a product-moment correlation effect-size statistic. When reporting the results, we will only compare similar effect sizes to each other and combine within types for the appropriate analysis. Following Lipsey and Wilson (2001), mean effect sizes and the homogeneity of effects across studies will be computed using the inverse variance weight method. We assume a random effects model and will calculate variance components accordingly. Computations will be run using Stata macros provided by D.B. Wilson. Sample output from these macros from a previous analysis (Rorie et al., 2009) is presented in Appendix B. Although we consider all empirical studies (using either qualitative or quantitative methods) in this review, we only use studies that allow us to code usable quantitative data. Therefore, we do not currently plan on including purely qualitative studies in our systematic review.10 We have already begun work on collecting studies, and many of the tasks proposed have already been completed for studies up to 2003. We hope to have a complete bibliography of studies (through 2011) by May of 2012. Also in May 2012, we will begin to examine the coded data and calculate effect sizes where possible. This entails the following steps: The projected completion date for calculating effect sizes is August 2012, after which we will begin work on written products and a Campbell Collaboration report. We hope to have a written report to the Campbell Collaboration by June 2013. Once we submit the written report to the Campbell Collaboration and at least one journal publication, we will begin work on updating the review. We plan on updating the review every three years in accordance with Campbell Collaboration guidelines. We also plan to carve other areas out of the initial review (see footnote 8) and update the literature in these areas. This research would not have been possible without the assistance of several undergraduate and graduate research assistants who have helped with finding, collecting, and coding studies for eligibility. We gratefully acknowledge Katy DeCelles, Megan Bears, Kerry Richmond, Rachael Powers, Patricia Joseph, Cliff Akiyama, Alex Bob, and other undergraduate assistants for their work on this project. In addition, many others provided methodological input and steered us to literature that we did not find in our original searches. Special thanks go out to Michael Benson, John Braithwaite, Mark Cohen, Peter Grabosky, Michael Levi, Christine Parker, Henry Pontell, David Weisburd, David B. Wilson, and Peter Yeager. Not Applicable CC Meta Analysis Coding Sheets: Study-Level Coding Protocol Bibliographic Reference (APA format): ______________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ I. Source Descriptors 1) Study ID number: 2) Type of Publication: 4) Disciplinary Affiliation of Publication/Journali: 5) Source of funding for the research: 8) Coder: II. Study Characteristics 1) Type of study: 6) What is the unit of analysis in this study (i.e., the type of outcome)? 7) Did the researcher empirically assess the reliability of the data collected? 1. Yes 0. No 8) Did the researcher assess the validity of the data collected (e.g., discussed whether measures used accurately represented the construct of interest)? 1. Yes 0. No 9) Did the researcher express any concern over the quality of the data or data collection procedures? (Even if the author thinks he/she addressed them adequately, include as a concern and describe solution in 9b) 1. Yes 0. No III. Sample Descriptors 2) Does the sample consist of individuals or corporations? 3) Was the sample drawn from more than one organization? 1. Yes 0. No 5) Predominant Race of sample 6) Predominant Sex of sample 7) Predominant management level of sample: 8) Who were the participants of the study? 9) Predominant education level of sample 10) Length of employment of the target population: ________ 11) From what industry was the sample drawn? (choose all that apply) IV. Methods and Procedures 1) Was the sample randomly selected? 1. Yes 0. No 888. Unclear or not reported 2) Sampling procedures 3) Survey design Other (specify) 777. Not applicable (not a survey) 4) Is the research design cross-sectional or longitudinal? 5) Did the authors assess the differences between survey respondents' and non-respondents' background characteristics? 1. Yes 0. No 777. Not applicable (not a survey) 5b) If yes, were significant differences found between responders' and nonresponders' background characteristics? 1. Yes 0. No 777. Not applicable 9) Nature of control group 10) Did the authors assess pre-test differences between tx/control groups? 1. Yes 0. No 10b) If so, were differences found between groups? 1. Yes 0. No V. Description of Independent Variable 1) What form did the treatment take?iii 1c) Was the independent variable binary or continuous? 3) What “authority” implemented the treatment/ was perceived to be implementing the treatment?? 4) What data sources were used to measure the independent variables? (Select all that apply) 5) Did the authors control for potentially spurious variables? 1. Yes 0. No VI. Dependent Variable Descriptors 1) Did the outcome describe actual behavior (e.g., arrests) or intentions (e.g., hypothetical situations)? 2) What type of data was used to measure the outcome covered on this coding sheet? 3)How was the DV measured? 5) Is the DV measured using illegal or unethical behavior?v Other (specify): (Unclear whether sanctionable/only related to company policies) 888. Unknown/Not reported 6)Does the behavior affect the company or society, according to Akers' (1977) list? VII. Effect Size Datavi 1) Was attrition a problem for this outcome? 1. Yes 0. No 777. Not Applicable (not a panel survey) 888. Not reported/unknown 4) Raw difference favors (i.e. shows more success for): Neither (exactly equal) 888. Unknown 777. Not applicable 5) Did a test of statistical significance indicate statistically significant differences between either the control and treatment groups or the pre and post tested treatment group? 1. Yes 0. No 888. Unknown 777. Not applicable 6) Was a standardized effect size reported? 1. Yes 0. No 9) If no, is there data available to calculate an effect size? 1. Yes 0. No 10) Type of data effect size can be calculated from:vii 10c) If the data presented is an unstandardized regression coefficient, what type of regression was used? VIII. Conclusions made by the author 1) Did the assessment find evidence for the effectiveness of the treatment? (e.g., significant statistical test in the hypothesized direction) 0. No 1. Yes 2. Not tested 2) Did the author(s) conclude there a relationship between the corporate crime prevention technique and a reduction in illegal corporate activities/violations, regardless of significant finding? 0. No 1. Yes 2. Can't tell/Author did not discuss i If book or unclear, code from author bio ii For our purposes, we will include studies that examine criminal and regulatory violations by corporations or their employees. The majority of corporate offenses are handled be regulatory agencies, like the EPA & OSHA. Thus, a focus on strictly criminal behaviors would limit this study and miss a great deal of corporate misconduct. According to Clinard and Yeager (1980), corporate crime is “any act committed by corporations that is punished by the state, regardless of whether it is punished under administrative, civil, or criminal law” (p. 16). This offense-based definition encompasses a wide range of behaviors such as antitrust offenses, intentionally polluting the environment, unsafe labor practices, and tax and securities violations. iii We are looking for variables that measure: iv Regarding measures of the dependent variable, we are not looking at overcompliance in and of itself. v An illegal act is one that has been formalized as a law or regulatory statute—i.e., you can be sued, cited, or arrested for it. vi Decision rules on including ESs: vii Anytime an article has more than one model, NOES_DTA should only have one value and there needs to be another case. There needs to be a new case anytime you have a new independent variable, dependent variable, or model (e.g., anytime you have data coming from a different place). viii For time-series, the baseline/pre-intervention numbers belong under the “control group.” The post-test is the treatment group. Appendix B: Sample Output from SPSS and Stata Macros" @default.
W2958633917 created "2019-07-23" @default.
W2958633917 creator A5025242084 @default.
W2958633917 creator A5045560586 @default.
W2958633917 creator A5049924211 @default.
W2958633917 creator A5064365790 @default.
W2958633917 creator A5091553919 @default.
W2958633917 date "2012-01-01" @default.
W2958633917 modified "2023-10-12" @default.
W2958633917 title "PROTOCOL: Corporate Crime Deterrence" @default.
W2958633917 cites W2118273886 @default.
W2958633917 cites W2334625319 @default.
W2958633917 doi "https://doi.org/10.1002/cl2.98" @default.
W2958633917 hasPublicationYear "2012" @default.
W2958633917 type Work @default.
W2958633917 sameAs 2958633917 @default.
W2958633917 citedByCount "0" @default.
W2958633917 crossrefType "journal-article" @default.
W2958633917 hasAuthorship W2958633917A5025242084 @default.
W2958633917 hasAuthorship W2958633917A5045560586 @default.
W2958633917 hasAuthorship W2958633917A5049924211 @default.
W2958633917 hasAuthorship W2958633917A5064365790 @default.
W2958633917 hasAuthorship W2958633917A5091553919 @default.
W2958633917 hasBestOaLocation W29586339171 @default.
W2958633917 hasConcept C142724271 @default.
W2958633917 hasConcept C144133560 @default.
W2958633917 hasConcept C15744967 @default.
W2958633917 hasConcept C204787440 @default.
W2958633917 hasConcept C2780385302 @default.
W2958633917 hasConcept C2780982575 @default.
W2958633917 hasConcept C38652104 @default.
W2958633917 hasConcept C41008148 @default.
W2958633917 hasConcept C71924100 @default.
W2958633917 hasConcept C73484699 @default.
W2958633917 hasConceptScore W2958633917C142724271 @default.
W2958633917 hasConceptScore W2958633917C144133560 @default.
W2958633917 hasConceptScore W2958633917C15744967 @default.
W2958633917 hasConceptScore W2958633917C204787440 @default.
W2958633917 hasConceptScore W2958633917C2780385302 @default.
W2958633917 hasConceptScore W2958633917C2780982575 @default.
W2958633917 hasConceptScore W2958633917C38652104 @default.
W2958633917 hasConceptScore W2958633917C41008148 @default.
W2958633917 hasConceptScore W2958633917C71924100 @default.
W2958633917 hasConceptScore W2958633917C73484699 @default.
W2958633917 hasIssue "1" @default.
W2958633917 hasLocation W29586339171 @default.
W2958633917 hasLocation W29586339172 @default.
W2958633917 hasOpenAccess W2958633917 @default.
W2958633917 hasPrimaryLocation W29586339171 @default.
W2958633917 hasRelatedWork W1503551567 @default.
W2958633917 hasRelatedWork W2012338008 @default.
W2958633917 hasRelatedWork W2150330834 @default.
W2958633917 hasRelatedWork W2291724454 @default.
W2958633917 hasRelatedWork W2354357239 @default.
W2958633917 hasRelatedWork W2505553694 @default.
W2958633917 hasRelatedWork W2914331763 @default.
W2958633917 hasRelatedWork W3214213355 @default.
W2958633917 hasRelatedWork W4220832793 @default.
W2958633917 hasRelatedWork W797152432 @default.
W2958633917 hasVolume "8" @default.
W2958633917 isParatext "false" @default.
W2958633917 isRetracted "false" @default.
W2958633917 magId "2958633917" @default.
W2958633917 workType "article" @default.