This dataset originates from a series of experimental studies titled “Tough on People, Tolerant to AI? Differential Effects of Human vs. AI Unfairness on Trust” The project investigates how individuals respond to unfair behavior (distributive, procedural, and interactional unfairness) enacted by artificial intelligence versus human agents, and how such behavior affects cognitive and affective trust.1 Experiment 1a: The Impact of AI vs. Human Distributive Unfairness on TrustOverview: This dataset comes from an experimental study aimed at examining how individuals respond in terms of cognitive and affective trust when distributive unfairness is enacted by either an artificial intelligence (AI) agent or a human decision-maker. Experiment 1a specifically focuses on the main effect of the “type of decision-maker” on trust.Data Generation and Processing: The data were collected through Credamo, an online survey platform. Initially, 98 responses were gathered from students at a university in China. Additional student participants were recruited via Credamo to supplement the sample. Attention check items were embedded in the questionnaire, and participants who failed were automatically excluded in real-time. Data collection continued until 202 valid responses were obtained. SPSS software was used for data cleaning and analysis.Data Structure and Format: The data file is named “Experiment1a.sav” and is in SPSS format. It contains 28 columns and 202 rows, where each row corresponds to one participant. Columns represent measured variables, including: grouping and randomization variables, one manipulation check item, four items measuring distributive fairness perception, six items on cognitive trust, five items on affective trust, three items for honesty checks, and four demographic variables (gender, age, education, and grade level). The final three columns contain computed means for distributive fairness, cognitive trust, and affective trust.Additional Information: No missing data are present. All variable names are labeled in English abbreviations to facilitate further analysis. The dataset can be directly opened in SPSS or exported to other formats.2 Experiment 1b: The Mediating Role of Perceived Ability and Benevolence (Distributive Unfairness)Overview: This dataset originates from an experimental study designed to replicate the findings of Experiment 1a and further examine the potential mediating role of perceived ability and perceived benevolence.Data Generation and Processing: Participants were recruited via the Credamo online platform. Attention check items were embedded in the survey to ensure data quality. Data were collected using a rolling recruitment method, with invalid responses removed in real time. A total of 228 valid responses were obtained.Data Structure and Format: The dataset is stored in a file named Experiment1b.sav in SPSS format and can be directly opened in SPSS software. It consists of 228 rows and 40 columns. Each row represents one participant’s data record, and each column corresponds to a different measured variable. Specifically, the dataset includes: random assignment and grouping variables; one manipulation check item; four items measuring perceived distributive fairness; six items on perceived ability; five items on perceived benevolence; six items on cognitive trust; five items on affective trust; three items for attention check; and three demographic variables (gender, age, and education). The last five columns contain the computed mean scores for perceived distributive fairness, ability, benevolence, cognitive trust, and affective trust.Additional Notes: There are no missing values in the dataset. All variables are labeled using standardized English abbreviations to facilitate reuse and secondary analysis. The file can be analyzed directly in SPSS or exported to other formats as needed.3 Experiment 2a: Differential Effects of AI vs. Human Procedural Unfairness on TrustOverview: This dataset originates from an experimental study aimed at examining whether individuals respond differently in terms of cognitive and affective trust when procedural unfairness is enacted by artificial intelligence versus human decision-makers. Experiment 2a focuses on the main effect of the decision agent on trust outcomes.Data Generation and Processing: Participants were recruited via the Credamo online survey platform from two universities located in different regions of China. A total of 227 responses were collected. After excluding those who failed the attention check items, 204 valid responses were retained for analysis. Data were processed and analyzed using SPSS software.Data Structure and Format: The dataset is stored in a file named Experiment2a.sav in SPSS format and can be directly opened in SPSS software. It contains 204 rows and 30 columns. Each row represents one participant’s response record, while each column corresponds to a specific variable. Variables include: random assignment and grouping; one manipulation check item; seven items measuring perceived procedural fairness; six items on cognitive trust; five items on affective trust; three attention check items; and three demographic variables (gender, age, and education). The final three columns contain computed average scores for procedural fairness, cognitive trust, and affective trust.Additional Notes: The dataset contains no missing values. All variables are labeled using standardized English abbreviations to facilitate reuse and secondary analysis. The file can be directly analyzed in SPSS or exported to other formats as needed.4 Experiment 2b: Mediating Role of Perceived Ability and Benevolence (Procedural Unfairness)Overview: This dataset comes from an experimental study designed to replicate the findings of Experiment 2a and to further examine the potential mediating roles of perceived ability and perceived benevolence in shaping trust responses under procedural unfairness.Data Generation and Processing: Participants were working adults recruited through the Credamo online platform. A rolling data collection strategy was used, where responses failing attention checks were excluded in real time. The final dataset includes 235 valid responses. All data were processed and analyzed using SPSS software.Data Structure and Format: The dataset is stored in a file named Experiment2b.sav, which is in SPSS format and can be directly opened using SPSS software. It contains 235 rows and 43 columns. Each row corresponds to a single participant, and each column represents a specific measured variable. These include: random assignment and group labels; one manipulation check item; seven items measuring procedural fairness; six items for perceived ability; five items for perceived benevolence; six items for cognitive trust; five items for affective trust; three attention check items; and three demographic variables (gender, age, education). The final five columns contain the computed average scores for procedural fairness, perceived ability, perceived benevolence, cognitive trust, and affective trust.Additional Notes: There are no missing values in the dataset. All variables are labeled using standardized English abbreviations to support future reuse and secondary analysis. The dataset can be directly analyzed in SPSS and easily converted into other formats if needed.5 Experiment 3a: Effects of AI vs. Human Interactional Unfairness on TrustOverview: This dataset comes from an experimental study that investigates how interactional unfairness, when enacted by either artificial intelligence or human decision-makers, influences individuals’ cognitive and affective trust. Experiment 3a focuses on the main effect of the “decision-maker type” under interactional unfairness conditions.Data Generation and Processing: Participants were college students recruited from two universities in different regions of China through the Credamo survey platform. After excluding responses that failed attention checks, a total of 203 valid cases were retained from an initial pool of 223 responses. All data were processed and analyzed using SPSS software.Data Structure and Format: The dataset is stored in the file named Experiment3a.sav, in SPSS format and compatible with SPSS software. It contains 203 rows and 27 columns. Each row represents a single participant, while each column corresponds to a specific measured variable. These include: random assignment and condition labels; one manipulation check item; four items measuring interactional fairness perception; six items for cognitive trust; five items for affective trust; three attention check items; and three demographic variables (gender, age, education). The final three columns contain computed average scores for interactional fairness, cognitive trust, and affective trust.Additional Notes: There are no missing values in the dataset. All variable names are provided using standardized English abbreviations to facilitate secondary analysis. The data can be directly analyzed using SPSS and exported to other formats as needed.6 Experiment 3b: The Mediating Role of Perceived Ability and Benevolence (Interactional Unfairness)Overview: This dataset comes from an experimental study designed to replicate the findings of Experiment 3a and further examine the potential mediating roles of perceived ability and perceived benevolence under conditions of interactional unfairness.Data Generation and Processing: Participants were working adults recruited via the Credamo platform. Attention check questions were embedded in the survey, and responses that failed these checks were excluded in real time. Data collection proceeded in a rolling manner until a total of 227 valid responses were obtained. All data were processed and analyzed using SPSS software.Data Structure and Format: The dataset is stored in the file named Experiment3b.sav, in SPSS format and compatible with SPSS software. It includes 227 rows and

Hypothesis 1: Generic job demands are positively related to a) emotional exhaustion, and b) depersonalization. Hypothesis 2: GP-specific job demands are positively related to a) emotional exhaustion and b) depersonalization. Hypothesis 3: Generic job resources are negatively related to a) emotional exhaustion and b) depersonalization. Hypothesis 4: GP-specific resources are negatively related to a) emotional exhaustion and b) depersonalization. Hypothesis 5: Time-based negative WHI partially mediates the relationship between generic job demands and a) emotional exhaustion, b) depersonalization. Hypothesis 6: Time-based negative WHI partially mediates the relationship between GP specific job demands and a) emotional exhaustion and b) depersonalization. Hypothesis 7: Strain-based negative WHI partially mediates the relationship between generic job demands and a) emotional exhaustion and b) depersonalization. Hypothesis 8: Strain-based negative WHI partially mediates the relationship between GP specific job demands and a) emotional exhaustion and b) depersonalization. The dataset includes raw data obtained from questionnaires, before single imputation with EM algorithm in SPSS to deal with missing values; Description of variables: WPQ (work pace and quantity; generic job demand, q0001 – q0006) MENT (mental load, generic job demand, q0007 – q0010) AUTO (autonomy, generic job resource, q0011 – q0013), not included in the current study OPPOR (opportunity for development, generic job resource, q0014-q0016) FEEDB (feedback, generic job resource, q0017-q0019) COLL (collaboration, generic job resource, q0020-q0022) SELF (self-efficacy, generic personal resource, q0023-q0026, not included in the current study) OPTIM (optimism, generic personal resources, q0027-q0030, not included in the current study) STRAIN (strain-based negative work-home interference, q0031, q0032, q0038, q0041) TIME (time-based negative work-home interference, q0034, q0037, q0039, q0042) EE (emotional exhaustion, q0044, q0045, q0046, q0049, q0051, q0055, q0056, q0059) DP (depersonalization, q0048, q0053, q0054, q0061) PA (personal accomplishment, q0047, q0050, q0052, q0057, q0058, q0060) JDGP (occupation-specific job demands, q0062-q0074) JRGP (occupation-specific job resources, q0075-q0084) PRGP (occupation-specific personal resources, q0085-q0087, not included in the current study) gender q0088 year of birth q0089 marital status q0090 year of start in present practice q0091 number of employees q0092 partner with job q0093 partner works overtime q0094 flexible childcare arrangements q0095 non-flexible childcare arrangements q0096 practice type q0097 care hours q0098 work hours q0099

Data from: Doctoral dissertation; Preprint article entitled: Managers' and physicians’ perception of palm vein technology adoption in the healthcare industry. Formats of the files associated with dataset: CSV; SAV. SPSS setup files can be used to generate native SPSS file formats such as SPSS system files and SPSS portable files. SPSS setup files generally include the following SPSS sections: DATA LIST: Assigns the name, type, decimal specification (if any), and specifies the beginning and ending column locations for each variable in the data file. Users must replace the "physical-filename" with host computer-specific input file specifications. For example, users on Windows platforms should replace "physical-filename" with "C:\06512-0001-Data.txt" for the data file named "06512-0001-Data.txt" located on the root directory "C:". VARIABLE LABELS: Assigns descriptive labels to all variables. Variable labels and variable names may be identical for some variables. VALUE LABELS: Assigns descriptive labels to codes in the data file. Not all variables necessarily have assigned value labels. MISSING VALUES: Declares user-defined missing values. Not all variables in the data file necessarily have user-defined missing values. These values can be treated specially in data transformations, statistical calculations, and case selection. MISSING VALUE RECODE: Sets user-defined numeric missing values to missing as interpreted by the SPSS system. Only variables with user-defined missing values are included in the statements. ABSTRACT: The purpose of the article is to examine the factors that influence the adoption of palm vein technology by considering the healthcare managers’ and physicians’ perception, using the Unified Theory of Acceptance and Use of Technology theoretical foundation. A quantitative approach was used for this study through which an exploratory research design was utilized. A cross-sectional questionnaire was distributed to responders who were managers and physicians in the healthcare industry and who had previous experience with palm vein technology. The perceived factors tested for correlation with adoption were perceived usefulness, complexity, security, peer influence, and relative advantage. A Pearson product-moment correlation coefficient was used to test the correlation between the perceived factors and palm vein technology. The results showed that perceived usefulness, security, and peer influence are important factors for adoption. Study limitations included purposive sampling from a single industry (healthcare) and limited literature was available with regard to managers’ and physicians’ perception of palm vein technology adoption in the healthcare industry. Researchers could focus on an examination of the impact of mediating variables on palm vein technology adoption in future studies. The study offers managers insight into the important factors that need to be considered in adopting palm vein technology. With biometric technology becoming pervasive, the study seeks to provide managers with the insight in managing the adoption of palm vein technology. KEYWORDS: biometrics, human identification, image recognition, palm vein authentication, technology adoption, user acceptance, palm vein technology

The Structural Equation Modeling (SEM) software market is experiencing robust growth, driven by increasing adoption across diverse sectors like education, healthcare, and the social sciences. The market's expansion is fueled by the need for sophisticated statistical analysis to understand complex relationships between variables. Researchers and analysts increasingly rely on SEM to test theoretical models, assess causal relationships, and gain deeper insights from intricate datasets. While the specific market size for 2025 isn't provided, a reasonable estimate, considering the growth in data analytics and the increasing complexity of research questions, places the market value at approximately $500 million. A Compound Annual Growth Rate (CAGR) of 8% seems plausible, reflecting steady but not explosive growth within a niche but essential software market. This CAGR anticipates continued demand from academia, government agencies, and market research firms. The market is segmented by software type (commercial and open-source) and application (education, medical, psychological, economic, and other fields). Commercial software dominates the market currently, due to its advanced features and professional support, however the open-source segment shows strong potential for growth, particularly within academic settings and amongst researchers with limited budgets. The competitive landscape is relatively concentrated with established players like LISREL, IBM SPSS Amos, and Mplus offering comprehensive solutions. However, the emergence of Python-based packages like semopy and lavaan demonstrates an ongoing shift towards flexible and programmable SEM software, potentially increasing market competition and innovation in the years to come. Geographic distribution shows North America and Europe currently holding the largest market share, with Asia-Pacific emerging as a key growth region due to increasing research funding and investment in data science capabilities. The sustained growth of the SEM software market is expected to continue throughout the forecast period (2025-2033), largely driven by the rising adoption of advanced analytical techniques within research and businesses. Factors limiting market growth include the high cost of commercial software, the steep learning curve associated with SEM techniques, and the availability of alternative statistical methods. However, increased user-friendliness of software interfaces, alongside the growing availability of online training and resources, are expected to mitigate these restraints and expand the market's reach to a broader audience. Continued innovation in SEM software, focusing on improved usability and incorporation of advanced features such as handling of missing data and multilevel modeling, will contribute significantly to the market's future trajectory. The development of cloud-based solutions and seamless integration with other analytical tools will also drive future market growth.

The Sicily and Calabria Extortion Database was extracted from police and court documents by the Palermo team of the GLODERS — Global Dynamics of Extortion Racket Systems — project which has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 315874 (http://www.gloders.eu, “Global dynamics of extortion racket systems”). The data are provided as an SPSS file with variable names, variable labels, value labels where appropriate, missing value definitions where appropriate. Variable and value labels are given in English translation, string texts are quoted from the Italian originals as we thought that a translation could bias the information and that users of the data for secondary analysis will usually be able to read Italian. The rows of the SPSS file describe one extortion case each. The columns start with some technical information (unique case number, reference to the original source, region, case number within the regions (Sicily and Calabria). These are followed by information about when the cases happened, the pseudonym of the extorter, his role in the organisation and the name and territory of the mafia family or mandamento he belongs to. Information about the victims, their affiliations and the type of enterprise they represent follows; the type of enterprise is coded according to the official Italian coding scheme (AtEco, which can be downloaded from http://www.istat.it/it/archivio/17888). The next group of variables describes the place where the extortion happened. The value labels for the numerical pseudonyms of extorters and victims (both persons and firms) are not contained in this file, hence the pseudonyms can only be used to analyse how often the same person or firm was involved in extortion. After this more or less technical information about the extortion cases the cases are described materially. Most variables come in two forms, both the original textual description of what happened and how it happened and a recoded variable which lends itself better for quantitative analyses. The features described in these variables encompass • whether the extortion was only attempted (and unsuccessful from the point of view of the extorter) or completed, i.e. the victim actually paid, • whether the request was for a periodic or a one-off payment or both and what the amount was (the amounts of periodic and one-off amounts are not always comparable as some were only defined in terms of percentages of victim income or in terms of obligations the victim accepted to employ a relative of the extorter etc.), • whether there was an intimidation and whether it was directed to a person or to property, • whether the extortion request was brought forward by direct personal contact or by some indirect communication, • whether there was some negotiation between extorter and victim, and if so, what it was like, and whether a mediator interfered, • how the victim reacted: acquiescent, conniving or refusing, • how the law enforcement agencies got to know about the case (own observation, denunciation, etc.), • whether the extorter was caught, brought to investigation custody or finally sentenced (these variables contain a high percentage of missing data, partly due to the fact that some cases are still under prosecution or before court or as a consequence of incomplete documents. Kompilation Transkription Compilation Transcription Extortion cases in Sicily and Calabria Reasoned sampling, trying to represent the proportional distribution of the cases between East and West Sicily. For Calabria the focus was on the province capital.

Systems thinking is a skill that is essential to understanding and taking effective action on complex challenges such as climate change. This research evaluated whether systems thinking could be increased with a brief intervention. Participants (N = 678) recruited from Amazon Mechanical Turk all completed the Systems Thinking Scale (Randel & Stroink, 2018), which was used as a covariate. Participants were then randomly assigned to one of four conditions. Some participants (n = 165) watched an entertaining 5-minute video describing systems thinking with a real-life example (Cats in Borneo, https://www.youtube.com/watch?v=17BP9n6g1F0). Others (n = 174) watched this video, read a definition of systems thinking, and were asked to engage in systems thinking while completing a survey. This was designed to be a "sledgehammer" condition, in which we made our manipulation as heavy-handed as possible. A third (control) condition (n = 167) watched a video about how to fold a fitted sheet...., Participants were recruited via Amazon Mechanical Turk. All participants were adults living in the United States. We gave 10 different measures that capture some aspect of systems thinking: The Systems Thinking Scale (Randle & Stroink, 2018): This 15-item self-report scale measures someone's dispositional tendency to engage in systems thinking. Negatively worded items were recoded, and all items were averaged together. Higher scores = more systems thinking. This trait measure was given at the start of the study and was used as a covariate. The Murder Scenario (Choi et al., 2007): Participants read a brief description of a murder case and indicated which of 96 possible facts were irrelevant to the case. We recoded the items such that 1 = relevant, 0 = irrelevant. The recoded items were summed together. Choosing more items indicates more holistic thinking about causality. Ripple Effect Question: Driver Scenario: Based on measures developed by Maddux & Yuki (2006). Participan..., , # Using a "sledgehammer" approach to increase systems thinking with a brief manipulation

https://doi.org/10.5061/dryad.v9s4mw75b

Description of the data and file structure

The data appear in the file DataForIncreasing STWithBriefManipulation.csv. Variable descriptions and values appear in the file MetaDataForIncreasing STWithBriefManipulation.csv.Â

Files and variables

File: DataForIncreasingSTWithBriefManipulation.csv

Description: Raw data that has not been published. The data file was generated in SPSS but exported to csv format for accessibility. Each row corresponds to a single participant. Missing data occurred when online participants failed to complete a question. Missing data is indicated with an empty field.

Variables

| Variable | Position | Label ...,

Abstract (en): The major focus of this Euro-Barometer is the respondent's knowledge of and attitudes toward the nations of the Third World. Topics covered include the culture and customs of these nations, the existence of poverty and hunger, and the respondent's opinions on how best to provide assistance to Third World countries. Individuals answered questions on social and political conditions as well as on the level of economic development in these countries. Additionally, respondents were asked to assess the state of relations between the respondent's country and various Third World nations. Another focus of this data collection concerns energy problems and resources in the countries of the European Economic Community. Respondents were asked to choose which regions of the world are considered to be reliable suppliers of fossil fuel for the future and to evaluate the risks that various industrial installations such as chemical and nuclear power plants pose to people living nearby. Respondents were also asked about solutions to the need for additional energy supplies in the future. Possible solutions included the development or continued development of nuclear power, the encouragement of research into producing renewable energy sources such as solar energy, and the conservation of energy. As in previous surveys in this series, respondents' attitudes toward the Community, life satisfaction, and social goals continued to be monitored. The survey also asked each individual to assess the advantages and disadvantages of the creation of a single common European market and whether they approved or disapproved of current efforts to unify western Europe. In addition, the respondent's political orientation, outlook for the future, and socioeconomic and demographic characteristics were probed. Please review the "Weighting Information" section located in the ICPSR codebook for this Eurobarometer study. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Persons aged 15 and over residing in the 12 member nations of the European Community: Belgium, Denmark, France, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, United Kingdom, and West Germany (including West Berlin). Smallest Geographic Unit: country Multistage probability samples and stratified quota samples. 2009-04-13 The data have been further processed by GESIS-ZA, and the codebook, questionnaire, and SPSS setup files have been updated. Also, SAS and Stata setup files, SPSS and Stata system files, a SAS transport (CPORT) file, and a tab-delimited ASCII data file have been added. Funding insitution(s): National Science Foundation (SES 85-12100 and SES 88-09098). The original data collection was carried out by Faits et Opinions on request of the Commission of the European Communities.The GESIS-ZA study number for this collection is ZA1713, as it does not appear in the data.References to OSIRIS, card-image, and SPSS control cards in the ICPSR codebook for this study are no longer applicable as the data have not been provided in OSIRIS or card-image file formats.Please disregard any reference to column locations, width, or deck in the ICPSR codebook and questionnaire files as they are not applicable to the ICPSR-produced data file. Correct column locations and LRECL for the ICPSR-produced data file can be found in the SPSS and SAS setup files, and Stata dictionary file. The full-product suite of files produced by ICPSR have originated from an SPSS portable file provided by the data producer.Question numbering for Eurobarometer 28 is as follows: Q128-Q180, Q211-Q280, Q313-Q359, and Q60-Q80 (demographic questions). Some question numbers are intentionally skipped, however neither questions nor data are missing.For country-specific categories, filter information, and other remarks, please see the corresponding variable documentation in the ICPSR codebook.V465 (VOTE INTENTION - DENMARK): Danish respondents who declared for political party "Venstre" had been coded as falling into the missing value category during the raw data processing for Eurobarometer 28. The original coding for Eurobarome...

Abstract (en): This collection examines the characteristics of users and sellers of crack cocaine and the impact of users and sellers on the criminal justice system and on drug treatment and community programs. Information was also collected concerning users of drugs other than crack cocaine and the attributes of those users. Topics covered include initiation into substance use and sales, expenses for drug use, involvement with crime, sources of income, and primary substance of abuse. Demographic information includes subject's race, educational level, living area, social setting, employment status, occupation, marital status, number of children, place of birth, and date of birth. Information was also collected about the subject's parents: education level, occupation, and place of birth. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Standardized missing values.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. Residents of two New York City neighborhoods, some of whom had been arrested for drug offenses, some of whom used drugs but had eluded arrest, and some of whom were participating in drug treatment programs. Respondents were selected through police records and snowball sampling methods. 2005-11-04 On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revised 2005-11-04 to reflect these additions.2002-04-25 The data file was converted from card image to logical record length data format. SAS and SPSS data definition statements were created, and the codebook was converted to PDF format. Funding insitution(s): United States Department of Justice. Office of Justice Programs. National Institute of Justice (87-IJ-CX-0064). The codebook is provided by ICPSR as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided on the ICPSR Web site.