University of Economics in Katowice

Almost fifty million people are food insecure in the United States, which makes food insecurity one of the nation's leading health and nutrition issues. We examine recent research evidence of the health consequences of food insecurity for children, nonsenior adults, and seniors in the United States. For context, we first provide an overview of how food insecurity is measured in the country, followed by a presentation of recent trends in the prevalence of food insecurity. Then we present a survey of selected recent research that examined the association between food insecurity and health outcomes. We show that the literature has consistently found food insecurity to be negatively associated with health. For example, after confounding risk factors were controlled for, studies found that food-insecure children are at least twice as likely to report being in fair or poor health and at least 1.4 times more likely to have asthma, compared to food-secure children; and food-insecure seniors have limitations in activities of daily living comparable to those of food-secure seniors fourteen years older. The Supplemental Nutrition Assistance Program (SNAP) substantially reduces the prevalence of food insecurity and thus is critical to reducing negative health outcomes.

Abstract It is well known that a linear combination of forecasts can outperform individual forecasts. The common practice, however, is to obtain a weighted average of forecasts, with the weights adding up to unity. This paper considers three alternative approaches to obtaining linear combinations. It is shown that the best method is to add a constant term and not to constrain the weights to add to unity. These methods are tested with data on forecasts of quarterly hog prices, both within and out of sample. It is demonstrated that the optimum method proposed here is superior to the common practice of letting the weights add up to one.

ChatGPT is an AI tool that assisted in writing, learning, solving assessments and could do so in a conversational way. The purpose of the study was to develop a model that examined the predictors of adoption and use of ChatGPT among higher education students. The proposed model was based on a previous theory of technology adoption. Seven predictors were selected to build a model that predicted the behavioral intention and use behavior of ChatGPT. The partial-least squares method of structural equation modeling was used for data analysis. The model was found to be reliable and valid, and the results were based on a self-reported data of 534 students from a Polish state university. Nine out of ten proposed hypotheses were confirmed by the results. Habit was found to be the best predictor of behavioral intention, followed by performance expectancy and hedonic motivation. The dominant determinant of use behavior was behavioral intention, followed by personal innovativeness. The research highlighted the need for further examination of how AI tools could be adopted in learning and teaching.

The introduction of Big Data Analytics (BDA) in healthcare will allow to use new technologies both in treatment of patients and health management. The paper aims at analyzing the possibilities of using Big Data Analytics in healthcare. The research is based on a critical analysis of the literature, as well as the presentation of selected results of direct research on the use of Big Data Analytics in medical facilities. The direct research was carried out based on research questionnaire and conducted on a sample of 217 medical facilities in Poland. Literature studies have shown that the use of Big Data Analytics can bring many benefits to medical facilities, while direct research has shown that medical facilities in Poland are moving towards data-based healthcare because they use structured and unstructured data, reach for analytics in the administrative, business and clinical area. The research positively confirmed that medical facilities are working on both structural data and unstructured data. The following kinds and sources of data can be distinguished: from databases, transaction data, unstructured content of emails and documents, data from devices and sensors. However, the use of data from social media is lower as in their activity they reach for analytics, not only in the administrative and business but also in the clinical area. It clearly shows that the decisions made in medical facilities are highly data-driven. The results of the study confirm what has been analyzed in the literature that medical facilities are moving towards data-based healthcare, together with its benefits.

Abstract AI-powered chat technology is an emerging topic worldwide, particularly in areas such as education, research, writing, publishing, and authorship. This study aims to explore the factors driving students' acceptance of ChatGPT in higher education. The study employs the unified theory of acceptance and use of technology (UTAUT2) theoretical model, with an extension of Personal innovativeness, to verify the Behavioral intention and Use behavior of ChatGPT by students. The study uses data from a sample of 503 Polish state university students. The PLS-SEM method is utilized to test the model. Results indicate that Habit has the most significant impact (0.339) on Behavioral intention, followed by Performance expectancy (0.260), and Hedonic motivation (0.187). Behavioral intention has the most significant effect (0.424) on Use behavior, followed by Habit (0.255) and Facilitating conditions (0.188). The model explains 72.8% of the Behavioral intention and 54.7% of the Use behavior variance. While the study is limited by the sample size and selection, it is expected to be a starting point for more research on ChatGPT-like technology in university education, given that this is a recently introduced technology.

Purpose The primary purpose of this paper is to examine how generative Artificial Intelligence (AI) such as ChatGPT may serve as a new context for management theories and concepts. Design/methodology/approach The paper presents the analyses of selected management theories on decision-making, knowledge management, customer service, human resource management and administrative tasks and explains what may change after generative AI adoption. Findings The paper indicates that some management theories and concepts need to be studied in the generative AI environment that may influence managerial work at the strategic, functional and administrative levels. Research limitations/implications This paper is an opinion piece article and does not refer to empirical data. It formulates some conclusions to further empirical research studies. Originality/value The paper analyzes selected management theories in a new technological setting. The paper also provides information about the functions of generative AI that are useful in understanding and overcoming how new technology may change organizations and management.

This paper is dedicated to the higher education institutions shifting towards distance learning processes due to the global pandemic situation caused by COVID-19 in 2020. The paper covers the pandemic situation in Poland generally, analyzing governmental ordinances and tracking the gradual extension of restrictions for educational institutions. The purpose of this study is to investigate the influence of Experience, Enjoyment, Computer Anxiety, and Self-Efficacy on students' acceptance of shifting education to distance learning. The study tested and used the adapted General Extended Technology Acceptance Model for E-Learning (GETAMEL) in the context of coronavirus pandemic. The partial least squares method of structural equation modeling was employed to test the proposed research model. The study utilizes an online survey to obtain data from 1692 Polish undergraduate and graduate students in both full- and part-time study. The dataset was analyzed using SmartPLS 3 software. Results showed that the best predictor of student's acceptance of shifting education to distance learning is Enjoyment, followed by Self-Efficacy. Both Perceived Ease of Use and Perceived Usefulness predict student's Attitude Towards Using and Intention to Use the distance learning. The findings improve understanding regarding the acceptance of distance learning and this work is therefore of particular interest to teachers and practitioners of education.

The development of artificial intelligence technology is currently bringing about new opportunities in construction. Machine learning is a major area of interest within the field of artificial intelligence, playing a pivotal role in the process of making construction “smart”. The application of machine learning in construction has the potential to open up an array of opportunities such as site supervision, automatic detection, and intelligent maintenance. However, the implementation of machine learning faces a range of challenges due to the difficulties in acquiring labeled data, especially when applied in a highly complex construction site environment. This paper reviews the history of machine learning development from shallow to deep learning and its applications in construction. The strengths and weaknesses of machine learning technology in construction have been analyzed in order to foresee the future direction of machine learning applications in this sphere. Furthermore, this paper presents suggestions which may benefit researchers in terms of combining specific knowledge domains in construction with machine learning algorithms so as to develop dedicated deep network models for the industry.

Despite extensive corporate social responsibility (CSR) literature most of research has examined corporate performance as its only outcome. We aim to fill this gap by assessing companies' perceptions of their CSR activities’ benefits for society and specific stakeholders. We discuss societal trends such as diversity and inclusion embedded in employee-focused CSR conceptualization as a prerequisite for the perception of CSR’s societal impact. We bring together CSR and relationship marketing theories to test a conceptual model on a sample of 411 business-to-business (B2B) companies. The results confirm a positive relationship between employee-oriented CSR and the perceived usefulness of CSR actions for society, customers and employees (but not suppliers). In order to maximize relationship quality, CSR activities should be targeted at specific stakeholders (customers and employees) and not at society at large. Finally, differences are observed between SMEs and large B2B firms with opposite perceptions of antecedents and outcomes of relationship quality.

Getting a good feature representation of data is paramount for Human Activity Recognition (HAR) using wearable sensors. An increasing number of feature learning approaches-in particular deep-learning based-have been proposed to extract an effective feature representation by analyzing large amounts of data. However, getting an objective interpretation of their performances faces two problems: the lack of a baseline evaluation setup, which makes a strict comparison between them impossible, and the insufficiency of implementation details, which can hinder their use. In this paper, we attempt to address both issues: we firstly propose an evaluation framework allowing a rigorous comparison of features extracted by different methods, and use it to carry out extensive experiments with state-of-the-art feature learning approaches. We then provide all the codes and implementation details to make both the reproduction of the results reported in this paper and the re-use of our framework easier for other researchers. Our studies carried out on the OPPORTUNITY and UniMiB-SHAR datasets highlight the effectiveness of hybrid deep-learning architectures involving convolutional and Long-Short-Term-Memory (LSTM) to obtain features characterising both short- and long-term time dependencies in the data.

The article deals with distance education, which as a teaching method had to be suddenly introduced in schools and higher education institutions as a result of the global pandemic situation. The paper captures the second wave of Poland’s pandemic situation in relation to global circumstances and the methods of conducting distance learning used across the globe. The purpose of this study was to investigate first-year students’ expectations about the education shift to distance learning. GETAMEL, which is the adapted General Extended Technology Acceptance Model for E-Learning, was used in the study. The study analyzed the influence of Experience, Subjective Norms, Enjoyment, Computer Anxiety, and Self-Efficacy on students’ expectations in the context of distance learning during the COVID-19 pandemic. To test the research model presented during the research, The Partial Least Squares method of Structural Equation Modeling was used. An online survey was created to conduct the research, which collected data from 670 Polish first-year undergraduate students. The acquired data were analyzed using the SmartPLS 3 software. The results of the research indicated that the most important factors that influence the feelings of students and can convince them to change from teaching in the classroom to teaching in the distance learning model are the feeling of pleasure in this form of education and a sense of self-efficacy. The results of this study may be of particular interest to education practitioners, including teachers, and a starting point for further research on e-learning models, including, in particular, the understanding of students’ expectations regarding distance learning.

Abstract This study delves into the implications of incorporating AI tools, specifically ChatGPT, in higher education contexts. With a primary focus on understanding the acceptance and utilization of ChatGPT among university students, the research utilizes the Unified Theory of Acceptance and Use of Technology (UTAUT) as the guiding framework. The investigation probes into four crucial constructs of UTAUT—performance expectancy, effort expectancy, social influence and facilitating conditions—to understand their impact on the intent and actual use behaviour of students. The study relies on data collected from six universities in two countries and assessed through descriptive statistics and structural equation modelling techniques, and also takes into account participants' gender and study level. The key findings show that performance expectancy, effort expectancy, and social influence significantly influence behavioural intention. Furthermore, behavioural intention, when considered alongside facilitating conditions, influences actual use behaviour. This research also explores the moderating impact of gender and study level on the relationships among these variables. The results not only augment our comprehension of technology acceptance in the context of AI tools but also provide valuable input for formulating strategies that promote effective incorporation of ChatGPT in higher education. The study underscores the need for effective awareness initiatives, bespoke training programmes, and intuitive tool designs to bolster students' perceptions and foster the wider adoption of AI tools in education. Practitioner notes What is already known about this topic ChatGPT is a tool that is quickly gaining worldwide recognition. ChatGPT helps with writing essays and solving assignments. ChatGPT raises ethical concerns about authorship, plagiarism and ethics. What this paper adds This study explores students' acceptance of ChatGPT as an aid in their education, which has not been studied previously. We used the extended Unified Technology Acceptance and Use of Technology theory to test what factors mostly influence the use of ChatGPT by students. We conducted a multiple study in Poland and Egypt based on sampling strategy from six universities. Implications for practice and/or policy ChatGPT is a global game changer and should be incorporated into study programmes. The limitations of ChatGPT should be well explained and known since it is prone to making mistakes. Higher education teachers should be aware of ChatGPT's capabilities.

An international association advancing the multidisciplinary study of informing systems. Founded in 1998, the Informing Science Institute (ISI) is a global community of academics shaping the future of informing science.

An international association advancing the multidisciplinary study of informing systems. Founded in 1998, the Informing Science Institute (ISI) is a global community of academics shaping the future of informing science.

Purpose The purpose of this paper is to build a research model that examines the impact of leadership on trust, knowledge management and organizational performance. Design/methodology/approach An instrument containing six constructs (leadership: leading organization; leadership: leading people; leadership: leading self, trust, knowledge management and organizational performance) was designed and administered to subjects from all levels of management in various organizations in nine regions of the USA. Collected data were analyzed using partial least squares path modeling to test the hypotheses. Findings The study’s findings revealed positive and significant linear connection among leadership (leading organization, leading people and leading self), trust, knowledge management and organizational performance. Practical implications The findings imply that effective leadership (leading organization, leading people and leading self) contributes to elevated trust among people, promotes the successful implementation of knowledge management processes, and in turn enhances organizational performance. Therefore, leadership training and development must be a top strategic priority for any organization. Originality/value This study enriches the literature by demonstrating that effective leadership stands as the bedrock of the elevated trust, the successful knowledge management processes and the enhanced organizational performance.

Effective evaluation of cities’ and communities’ sustainability is important for sustainable development. From a methodological point of view, Multi-Criteria Decision Analysis (MCDA) methods proved their usability in the sustainability evaluation domain. Nevertheless, the process of building a model in the classical MCDA paradigm is based on a single set of input data. Therefore, it may lead to oversimplification, especially in the domain of sustainability. Moreover, in addition to the current assessment, it is also important to know the dynamics of sustainability change over time. Therefore, this paper proposes an innovative sustainability assessment method that integrates the MCDA approach with the variability of the alternatives’ performance measurement called Data vARIability Assessment Technique for Order of Preference by Similarity to Ideal Solution (the DARIA-TOPSIS method). This method was used to assess sustainable cities and communities in 26 European countries. Time-based analyses conducted using the DARIA-TOPSIS method for aggregated data (countries), individual sustainability dimensions, and alternatives proved our new approach’s usefulness, suitability, and effectiveness in sustainable cities and society domain.

This article provides valuable information on the chances and the possibilities of Business Intelligence applying in organizations. Three theories—the Resource-Based View, Maturity Models, and Critical Success Factors—were used to investigate Business Intelligence issues. They provided a comprehensive view on Business Intelligence. Using a semi-structured interview method, the results from 20 organizations applying Business Intelligence are presented. Finally, based on the analysis of the literature and on the qualitative surveys, conclusions for future research in Business Intelligence are provided.

Purpose The purpose of this paper is to propose and empirically investigate the concept of networking capability (NC) for the management of supplier relationships and their dynamics in order to leverage product innovations. NC in the context of supplier relationships is conceptualized based on dynamic capabilities aimed at relationship initiation, relationship development, and relationship ending. Furthermore, the study tests the interaction of NC with relationship proclivity as an organizational feature, and analyzes latent classes of NC affecting product innovation. Design/methodology/approach This study brings together prior research on company routines related to inter-firm networking, the dynamic capability approach to strategy, and literature on inter-firm innovation. The study utilizes multiple informant survey data gathered from 156 firms operating in the automotive parts industry in Iran. Data are analyzed with partial least square structural equation modeling, as well as latent class analysis using finite mixture modeling (FIMIX-PLS). Findings This research provides evidence for the positive influence of NC with respect to supplier relationships on firm product innovation, as well as overall firm performance. Relationship proclivity is shown to amplify this effect. At the same time, the research illustrates that NC may be applied in different combinations in the context of supplier relationship portfolio management. Two mechanisms are tentatively identified: firms using “static optimization” focus mainly on supplier relationship development capabilities, while those using “dynamic optimization” utilize supplier relationship initiation and ending capabilities. Research limitations/implications This research focuses on one setting (i.e. the automotive parts industry in Iran). Further studies need to broaden these findings to other industries and countries, specifically those which show a different cultural make-up from Iran. Furthermore, this research indicates the existence of two distinct mechanisms as to how different aspects of NC impact product innovation. While it is reasonable to identify these mechanisms as networking “strategies,” this study does not clarify whether this represents intended strategies by firms or relates to emerging capability patterns. Practical implications The study contributes to managerial knowledge by illustrating the need for a dynamic approach with regard to networking-related routines in supplier relationships in the context of product innovation. This study suggests that managers should devote equal attention to strengthening existing supplier relationships as well as to initiating new supplier relationships (e.g. screening for promising partners and signaling firm’s relationship value to attract new counterparts) and managing non-performing supplier relationships (e.g. by developing routines to exit from those supplier relationships). Originality/value The paper contributes to a better understanding of dynamic approaches to networking with suppliers and their impact on product innovation from the perspective of the focal firm. It furthermore provides a fine-grained understanding of different latent classes of firms in terms of how they utilize networking capabilities.

The paper presents the most comprehensive and large-scale global study to date on how higher education students perceived the use of ChatGPT in early 2024. With a sample of 23,218 students from 109 countries and territories, the study reveals that students primarily used ChatGPT for brainstorming, summarizing texts, and finding research articles, with a few using it for professional and creative writing. They found it useful for simplifying complex information and summarizing content, but less reliable for providing information and supporting classroom learning, though some considered its information clearer than that from peers and teachers. Moreover, students agreed on the need for AI regulations at all levels due to concerns about ChatGPT promoting cheating, plagiarism, and social isolation. However, they believed ChatGPT could potentially enhance their access to knowledge and improve their learning experience, study efficiency, and chances of achieving good grades. While ChatGPT was perceived as effective in potentially improving AI literacy, digital communication, and content creation skills, it was less useful for interpersonal communication, decision-making, numeracy, native language proficiency, and the development of critical thinking skills. Students also felt that ChatGPT would boost demand for AI-related skills and facilitate remote work without significantly impacting unemployment. Emotionally, students mostly felt positive using ChatGPT, with curiosity and calmness being the most common emotions. Further examinations reveal variations in students' perceptions across different socio-demographic and geographic factors, with key factors influencing students' use of ChatGPT also being identified. Higher education institutions' managers and teachers may benefit from these findings while formulating the curricula and instructions/regulations for ChatGPT use, as well as when designing the teaching methods and assessment tools. Moreover, policymakers may also consider the findings when formulating strategies for secondary and higher education system development, especially in light of changing labor market needs and related digital skills development.

Recent public policies increasingly emphasize the role of communities in service co-production. Collaboration between the state and the public is frequently associated with social enterprise activities. However, the assumption that social enterprises can be successfully built and developed in remote and rural areas might be faulty. Current policy does not recognize contextual factors relating to rural social enterprise development. Drawing on a qualitative study in the Highlands of Scotland the article questions the role of social enterprise in creating sustainable rural communities; it presents promoters and barriers to rural social enterprise development. Findings suggest that although rural communities do not control all the conditions that affect them, they have the ability to adapt to some structural features. This means that in spite of social and economic challenges, rural communities might benefit from rural social enterprise through practising ‘adaptive capacity’.