A blockchain-based evaluation approach to analyse customer satisfaction using AI techniques

Due to technological advancements and consumer demands, online shopping creates new features and adapts to new standards. A robust customer satisfaction prediction model concerning trust and privacy platforms can encourage an organization to make better decisions about its service and quality. This study presented an approach to predict consumer satisfaction using the blockchain-based framework combining the Multi-Dimensional Naive Bayes-K Nearest Neighbor (MDNB-KNN) and the Multi-Objective Logistic Particle Swarm Optimization Algorithm (MOL-PSOA). A regression model is employed to quantify the impact of various production factors on customer satisfaction. The proposed method yields better levels of measurement for customer satisfaction (98%), accuracy (95%), necessary time (60%), precision (95%), and recall (95%) compared to existing studies. Measuring consumer satisfaction with a trustworthy platform facilitates to development of the conceptual and practical distinctions influencing customers' purchasing decisions.publishedVersio

The Impact of Corporate Climate Action on Financial Markets: Evidence from Climate-Related Patents

We study the impact of climate-related patents on financial markets. We exploit the quasi-random assignment of patent examiners with different degrees of leniency as an exogenous shock in patent approvals to allow for causal interpretations. We find that firms with more lucky climate-related patents subsequently display higher positive cumulative abnormal stock returns and enjoy a lower cost of capital, compared with similarly innovative but unlucky firms. These results hold especially during periods of high attention towards climate change and for initial climate patent granting. Firms with more lucky climate-related patents also exhibit better environmental ratings and attract more responsible institutional investors. OLS regressions show that firms developing more climate-related technologies reduce more direct carbon emission intensity

Predicting Pervious Concrete Pavement Performance for Usage in Cold Climates

Pervious Concrete Pavement (PCP) has the potential to provide significant benefits. To better understand the technical, economical, and environmental impacts of PCP, the performance must be comprehensively evaluated and quantified. Because PCP is a new material, there is no mechanism for properly quantifying its performance. In addition, the application of this technology in cold climates is limited and therefore limited in-service performance data is available. A comprehensive engineering based performance model quantifies the deterioration rate and predicts future performance. Pavement performance models are developed using a pavement condition index and extensive pavement condition databases. A pavement condition index is a value which expresses the overall condition of pavement by considering various factors such as surface distresses, structural defects, and ride quality. This research will assist pavement engineers and managers in the design, construction, and management of PCP. The review of published literature reveals that there is currently a large gap in the performance evaluation of PCP in cold climates. Neither extensive condition indices nor comprehensive performance models have been developed for PCP. This research involves development of comprehensive performance models for PCP in cold climates using laboratory and field experiments and existing available data in order to predict functionality (permeability rate) and surface distresses of PCP. This study is, furthermore, aimed at developing an extensive condition index for better management of PCP by predicting and quantifying the various types of distresses and the associated functionality of PCP with particular emphasis on cold climate usage and performance. The scope of this research is to design a comprehensive tool which is simple and cost-effective. The tool involves first defining the typical types of distresses that are occurring on PCP. This is facilitated through laboratory and field design, construction, and evaluation of two test sites located in Ontario. It also involves continuous evaluation of these sites and evaluation of several other sites in the United States. The main sources of data in this research are panel rating data and field investigations data. A panel rates the condition of PCP in terms of surface distresses and permeability rates. In addition to this, field measurements of distresses and permeability rates are obtained manually. As a result, the Pervious Concrete Condition Index (PCCI) is developed through incorporation of field measurements and panel ratings. By using regression analysis, performance models are developed between PCCI and pavement age. The performance models are validated using the data splitting technique. Ultimately, the performance models are calibrated using field data by applying the Markov Chain process (acquiring expert knowledge by distributing questionnaires) and the Bayesian technique

Understanding the Role of Attorneys in Plea Bargaining

Plea bargaining is the primary case resolution tool in the United States court system, but most plea bargaining occurs outside the courtroom and off the record. As a result, researchers have limited knowledge of how the process occurs. Given the centrality of their roles to plea bargaining, criminal defense and prosecuting attorneys likely are influential in determining plea outcomes. My colleagues and I conducted four studies across three articles to examine the role of attorneys in plea bargaining. In the first article (Chapter 2), we interviewed 23 attorneys about plea bargaining to lay foundational knowledge about the charging, negotiation, communication, and acceptance or rejection procedures. In the second article (Chapter 3), we conducted two experiments of mock defendant plea decision-making. In Experiment 1, undergraduate participants (N = 188) were assigned a condition in a 2 (Guilt status: Innocent vs. Guilty) × 2 (Crime: Robbery vs. Furnishing drugs) design to examine the effects of guilt status on adopted frame, subjective estimates of conviction probability, and plea decisions. In Experiment 2, undergraduate participants (N = 335) were assigned a condition in a 2 (Guilt status: Innocent vs. Guilty) × 2 (Frame: Gain vs. Loss) × 2 (Crime: Robbery vs. Furnishing drugs) design, in which we directly manipulated the framing of the plea offer. In both experiments, probability of conviction estimates partially mediated the relationship between guilt and plea decisions, and frame directly predicted plea decisions. In the third article (Chapter 4), we studied the effects of anchoring and presumed defendant guilt status on defense attorney plea recommendations. Defense attorneys recruited from CloudResearch (N = 183) and a snowball sample (N = 352) completed an experimental survey. The findings showed no effect of anchor on attorneys’ plea sentence recommendations, but higher defendant guiltiness ratings predicted longer sentencing recommendations. Overall, the findings demonstrate the importance of guilt and legal and extralegal factors in both understanding how attorneys make plea bargaining decisions and in improving decision-making models

Henkilöstön valintaprosessin kehittäminen pienille ja keskisuurille yrityksille

The costs of unsuccessful hiring decisions might be unbearable for small and medium-sized enterprises (SMEs). At the same time, many Finnish SMEs are struggling with employee selection. The SMEs mostly rely on interviews, and they do not usually use any tests or outside help. Digitalization is also making its way to recruitment and employee selection, but for the SMEs the question remains: what tools and methods should be used in employee selection? There is a lot of previous research studying different employee selection methods, but basically no research that would combine the methods and suggest an employee selection process specifically for SMEs. This thesis studied how SMEs should develop their employee selection process. The study applied a qualitative research approach, and data were collected with theme interviews. The interviewees were employee selection specialists from three top-class SMEs, a human resources manager from a global top tier management consulting company, two psychologists working with employee selection and headhunting, a professor of psychology, and a recruitment and employee selection specialist who has acted as an adjunct professor. The data were analyzed with an abductive perspective utilizing a thematic analysis method. Generalized statements, direct quotes, tables and figures were used to introduce and review the results. The main findings of the study suggest that SMEs should use objective and structured selection methods such as structured online application forms, structured interviews, work sample tests, cognitive ability tests and structured reference checks. Furthermore, the assessment should be objective and utilize a screen out approach, where applicants are compared to predetermined cutoff scores. In SMEs the subjective feeling of whether the assessor wants to work with the applicant or not is a necessary, but not a sufficient condition. The subjective feeling should not affect the objective assessment of the applicant’s skills and abilities. Lastly, the job analysis phase is highly crucial in SMEs, and general mental ability (GMA) and previous work experience are often important selection criteria. Entrepreneurs, CEOs, recruiters and other people working with employee selection in SMEs can use the findings of this study to improve their own employee selection processes. In order to help the SMEs to apply the results in practice, the study presents a framework and a set of important points that guide the development of the selection process.Epäonnistuneiden rekrytointien kustannukset voivat olla kestämättömän suuria pienille ja keskisuurille (pk) yrityksille. Samaan aikaan monilla suomalaisilla pk-yrityksillä on haasteita henkilöstön valinnan kanssa. Pk-yritykset luottavat enimmäkseen haastatteluihin, ja testejä tai ulkopuolista apua ei yleensä käytetä. Myös digitalisaatio tekee tuloaan rekrytointiin ja valintaprosesseihin, mutta pk-yritysten kannalta tärkeä kysymys kuuluu, mitä työkaluja ja menetelmiä yritysten tulisi käyttää henkilöstövalinnoissa? Aikaisemmat tutkimukset käsittelevät laajalti erillisiä valintamenetelmiä, mutta käytännössä mitään sellaisia tutkimuksia ei löydy, jotka yhdistäisivät eri menetelmät ja suosittelisivat pk-yrityksille sopivaa valintaprosessia. Tämä työ tutki, miten pk-yrityksen tulisi suunnitella henkilöstön valintaprosessi. Työssä käytettiin laadullista tutkimussuuntausta, ja materiaalit kerättiin teemahaastatteluilla. Haastateltavina oli asiantuntijoita kolmesta huippuluokan pk-yrityksestä, kansainvälisen huipputason liikkeenjohdon konsulttiyrityksen henkilöstöpäällikkö, kaksi henkilöstövalintojen ja suorahaun parissa työskentelevää psykologia, yksi psykologian professori ja yksi henkilöstövalintojen parissa työskentelevä asiantuntija, joka on toiminut ennen dosenttina. Aineisto analysoitiin abduktiivisella lähestymistavalla käyttäen hyödyksi teemoittelua. Yleistettyjä lausumia, suoria lainauksia, taulukoita ja kaavioita käytettiin tulosten esittämiseen ja tarkasteluun. Työn tärkeimmät tulokset suosittelevat, että pk-yritysten tulisi käyttää objektiivisia ja strukturoituja valintamenetelmiä, kuten strukturoituja kyselylomakkeita, strukturoituja haastatteluita, työsimulaatioita, yleisälykkyyden testejä ja strukturoitua hakijoiden referenssien tarkistusta. Lisäksi osaamisen ja kykyjen arvioinnin tulisi olla objektiivista ja hyödyntää karsivaa menetelmää, jossa hakijoita verrataan tiettyyn ennalta määrättyyn tasoon. Subjektiivinen tunne, haluaako arvioija työskennellä hakijan kanssa, on pk-yrityksissä välttämätön, mutta ei riittävä ehto. Tämä subjektiivinen näkemys ei saa vaikuttaa hakijan osaamisen objektiiviseen arviointiin. Lisäksi työssä havaittiin, että työanalyysi on erittäin tärkeä pk-yrityksissä, ja että yleisälykkyys (GMA) sekä aikaisempi työkokemus ovat usein tärkeitä valintakriteerejä. Yrittäjät, toimitusjohtajat ja muut pk-yrityksissä henkilöstön valintaan osallistuvat henkilöt voivat hyödyntää työn tuloksia omien valintaprosessien parantamisessa. Jotta pk-yritykset pystyisivät soveltamaan työn tuloksia käytännössä, työ esittelee viitekehyksen ja listan tärkeitä asioita, jotka ohjaavat valintaprosessin kehitystä

Effectiveness correlates of transformational and transactional leadership: A meta-analytic review of the MLQ literature

A meta-analysis of the transformational leadership literature using the Multifactor Leadership Questionnaire (MLQ) was conducted to (a) integrate the diverse findings, (b) compute an average effect for different leadership scales, and (c) probe for certain moderators of the leadership style-effectiveness relationship. Transformational leadership scales of the MLQ were found to be reliable and significantly predicted work unit effectiveness across the set of studies examined. Moderator variables suggested by the literature, including level of the leader (high or low), organizational setting (public or private), and operationalization of the criterion measure (subordinate perceptions or organizational measures of effectiveness), were empirically tested and found to have differential impacts on correlations between leader style and effectiveness. The operationalization of the criterion variable emerged as a powerful moderator. Unanticipated findings for type of organization and level of the leader are explored regarding the frequency of transformational leader behavior and relationships with effectiveness

A Toolkit for uncertainty reasoning and representation using fuzzy set theory in PROLOG expert systems

This thesis examines the issue of uncertainty reasoning and representation in expert systems. Uncertainty and expert systems are defined. The value of uncertainty in expert systems as an approximation of human reasoning is stressed. Five alternative methods of dealing with uncertainty are explored. These include Bayesian probabilities, Mycin confirmation theory, fuzzy set theory, Dempster-Shafer\u27s theory of evidence and a theory of endorsements. A toolkit to apply uncertainty processing in PROLOG expert systems is developed using fuzzy set theory as the basis for uncertainty reasoning and representation. The concepts of fuzzy logic and approximate reasoning are utilized in the implementation. The toolkit is written in C-PROLOG for the PYRAMID UNIX system at the Rochester Institute of Technology

Prosecutorial Regulation Versus Prosecutorial Accountability

No government official has as much unreviewable power or discretion as the prosecutor. Few regulations bind or even guide prosecutorial discretion, and fewer still work well. Most commentators favor more external regulation by legislatures, judges, or bar authorities. Neither across-the-board legislation nor ex post review of individual cases has proven to be effective, however. Drawing on management literature, this article reframes the issue as a principal-agent problem and suggests corporate strategies for better serving the relevant stakeholders. Fear of voters could better check prosecutors, as could victim participation in individual cases. Scholars have largely neglected the most promising avenue of reform, namely changing the internal structure and management of prosecutors\u27 offices. Leaders could do more to develop office cultures, norms, and ideals that value more than just maximizing conviction statistics. Hierarchical office structures and internal procedural and substantive office policies could promote deliberation, give fair notice, and increase consistency. Hiring, training, promotion, and tenure practices could better shape prosecutors and their behavior. Pay structures and feedback from judges, defense counsel, and victims could encourage good behavior. Finally, publishing more data on charges, convictions, plea bargains, and sentences could also improve accountability