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This case study sought to investigate the relationship between pre-service teachers’ participation in designing and delivering one-on-one literacy intervention lessons to beginning readers and their own evolving self-efficacy in literacy instruction.

The study was embedded within a 4000-level course in the elementary education major where pre-service teachers learn to administer, analyze and interpret a variety of literacy assessments. Based on the results of these assessments, pre-service teachers designed and implemented literacy lessons (twice a week, 30-min sessions) that addressed the beginning readers' specific instructional needs. Through collecting pre/post data with their first-grade intervention students, and participating in reflective “check-ins” (surveys, a focus group and end-of-course written reflection), a portrait of increased pre-service teacher self-efficacy in literacy instruction comes into focus.

The data showed, primarily through the thematic analysis of qualitative data, that the experience of conducting a one-on-one intervention with a striving reader impacted pre-service teachers’ self-efficacy positively.

The methodology of this study was limited by the small sample size and the low participant response rate on the quantitative survey measure.

This paper highlights one aspect in which clinically-rich field experiences can make a difference in the literacy instruction self-efficacy of pre-service teachers.

This study adds to the support for authentic instructional applications of course content in educator preparation programs, specifically in Professional Development School (partner school system) contexts. The aspect of observing and measuring intervention student progress was one lens through which pre-service teachers viewed their efficacy. Further investigations focusing on other assessment-instruction cycles could provide additional insights.

Exposure to indoor air pollutants poses a significant health risk, contributing to various ailments such as respiratory and cardiovascular diseases. These unhealthy consequences are specifically alarming for athletes during exercise due to their higher respiratory rate. Therefore, studying, predicting and curtailing exposure to indoor air contaminants during athletic activities is essential for fitness facilities. The objective of this study is to develop a neural network model designed for predicting optimal (in terms of health) occupancy intervals using monitored indoor air quality (IAQ) data.

This research study presents an innovative approach employing a long short-term memory (LSTM) recurrent neural network (RNN) to determine optimal occupancy intervals for ensuring the safety and well-being of occupants. The dataset was collected over a 3-month monitoring campaign, encompassing 15 meteorological and indoor environmental parameters monitored. All the parameters were monitored in 5-min intervals, resulting in a total of 77,520 data points. The dataset collection parameters included the building’s ventilation methods as well as the level of occupancy. Initial preprocessing involved computing the correlation matrix and identifying highly correlated variables to serve as inputs for the LSTM network model.

The findings underscore the efficacy of the proposed artificial intelligence model in forecasting indoor conditions, yielding highly specific predicted time slots. Using the training dataset and established threshold values, the model effectively identifies benign periods for occupancy. Validation of the predicted time slots is conducted utilizing features chosen from the correlation matrix and their corresponding standard ranges. Essentially, this process determines the ratio of recommended to non-recommended timing intervals.

Humans do not have the capacity to process this data and make such a relevant decision, though the complexity of the parameters of IAQ imposes significant barriers to human decision-making, artificial intelligence and machine learning systems, which are different. Present research utilizing multilayer perceptron (MLP) and LSTM algorithms for evaluating indoor air pollution levels lacks the capability to predict specific time slots. This study aims to fill this gap in evaluation methodologies. Therefore, the utilized LSTM-RNN model can provide a day-ahead prediction of indoor air pollutants, making its competency far beyond the human being’s and regular sensors' capacities.

The study explores the potential barriers to data science (DS) implementation in organizations and identifies the key barriers. The identified barriers were explored for their interconnectedness and characteristics. This study aims to help organizations formulate apt DS strategies by providing a close-to-reality DS implementation framework of barriers, in conjunction with extant literature and practitioners' viewpoints.

The authors synthesized 100 distinct barriers through systematic literature review (SLR) under the individual, organizational and governmental taxonomies. In discussions with 48 industry experts through semi-structured interviews, 14 key barriers were identified. The selected barriers were explored for their pair-wise relationships using interpretive structural modeling (ISM) and fuzzy Matriced’ Impacts Croise's Multiplication Appliquée a UN Classement (MICMAC) analyses in formulating the hierarchical framework.

The lack of awareness and data-related challenges are identified as the most prominent barriers, followed by non-alignment with organizational strategy, lack of competency with vendors and premature governmental arrangements, and classified as independent variables. The non-commitment of top-management team (TMT), significant investment costs, lack of swiftness in change management and a low tolerance for complexity and initial failures are recognized as the linkage variables. Employee reluctance, mid-level managerial resistance, a dearth of adequate skills and knowledge and working in silos depend on the rest of the identified barriers. The perceived threat to society is classified as the autonomous variable.

The study augments theoretical understanding from the literature with the practical viewpoints of industry experts in enhancing the knowledge of the DS ecosystem. The research offers organizations a generic framework to combat hindrances to DS initiatives strategically.

This study aims to establish an assessment model to measure big data readiness in the public sector, specifically targeting local governments at the provincial and city/regency levels. Additionally, the study aims to gain valuable insights into the readiness of selected local governments in Indonesia by using the established assessment model.

This study uses a mixed-method approach, using focus group discussions (FGDs), surveys and exploratory factor analysis (EFA) to establish the assessment model. The FGDs involve gathering perspectives on readiness variables from experts in academia, government and practice, whereas the survey collects data from a sample of selected local governments using a questionnaire developed based on the variables obtained in FGDs. The EFA is used on survey data to condense the variables into a smaller set of dimensions or factors. Ultimately, the assessment model is applied to evaluate the level of big data readiness among the selected Indonesian local governments.

FGDs identify 32 essential variables for evaluating the readiness of local governments to adopt big data. Subsequently, EFA reduces this number by five and organizes the remaining variables into four factors: big data strategy, policy and collaboration, infrastructure and human resources and data collection and utilization. The application of the assessment model reveals that the overall readiness for big data in the selected local governments is primarily moderate, with those in the Java cluster displaying higher readiness. In addition, the data collection and utilization factor achieves the highest score among the four factors.

This study offers an assessment model for evaluating big data readiness within local governments by combining perspectives from big data experts in academia, government and practice.

This research investigates the critical role of data governance (DG) in shaping a data-driven culture (DDC) within organizations, recognizing the transformative potential of data utilization for efficiency, opportunities, and productivity. The study delves into the influence of DG on DDC, emphasizing the mediating effect of data literacy (DL).

The study empirically assesses 125 experienced managers in Indonesian public service sector organizations using a quantitative approach. Structural Equation Modeling (SEM) analysis was chosen to examine the impact of DG on DDC and the mediating effects of DL on this relationship.

The findings highlight that both DG and DL serve as antecedents to DDC, with DL identified as a crucial mediator, explaining a significant portion of the effects between DG and DDC.

Beyond unveiling these relationships, the study discusses practical implications for organizational leaders and managers, emphasizing the need for effective policies and strategies in data-driven decision-making.

This research fills an important research gap by introducing an original model and providing empirical evidence on the dynamic interplay between DG, DL, and DDC, contributing to the evolving landscape of data-driven organizational cultures.

The current paper is aimed to explore the relationship between virtuous leadership (VL) dimensions and the dimensions of innovation management (IM) among employees in pharmaceutical companies of the public business sector – Egypt.

The current paper relied on the descriptive and analytical method and the survey paper in dealing with the paper variables. Participants for this paper consisted of (312) employees who completed a questionnaire that assessed VL and IM.

The results revealed that there is a positive, statistically significant relationship between VL and IM, Specifically, there is a positive effect of courage, justice and prudence on strategic innovation, a positive effect of courage, humanity and asceticism on technical innovation, and there is a positive effect of prudence, humanity and courage on management innovation.

The paper concluded that VL acts as an important tool that facilitates IM and promotes high levels of innovation for employees.

The current paper contributed to understanding the conditions in which employees of pharmaceutical companies have VL and provided additional guidance for effective practices of quality IM in pharmaceutical companies of the public business sector. In this study, a model was built to analyze the mechanism underlying the relationship between virtuous leadership and innovation management in pharmaceutical companies.

The purpose of the article is to gain more insight into factors that can explain students' success in business subjects. The focus is on the connection between performance in introductory courses in business mathematics (BM) and business statistics (BS) and success in various business subjects.

Use of a regression model with administrative data from a business school in Norway over a period of 10 years.

The findings show a strong correlation, especially in quantitative subjects. The results suggest that statistical skills are more strongly related to academic success than mathematical skills.

The data are collected from only one school. No information on undergraduates' personalities and behaviours is available.

There are limited published studies that have explored the relationship between success in statistics and later achievements in business courses. This is useful knowledge for planning the content of the bachelor's programme.

The purpose of this study was to development of a new chocolate-flavored powdered food supplement enriched with mesquite pod flour, oak extract and agave fructans, with proper sensory characteristics as well as its physicochemical and glycemic quality.

A powdered shake was formulated using experimental design (23) with mesquite (Prosopis laevigata) pod flour, oak (Quercus convallata) extracts, nonalkalinized cocoa, agave fructans, milk protein and xanthan gum. Sensory analysis (choice profile method, ranking test, focus group, quantitative descriptive analysis), moisture, ash, fiber, protein and lipids, pH, color, wettability, dispersibility and rheological tests were done. Phenolic profiling analysis to samples was done by ultraperformance liquid chromatography coupled to photodiode array detection and electrospray ionization tandem mass spectrometry, antioxidant activity was measured by 2,2’-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt radical cation, ferric reducing antioxidant power and oxygen radical absorbance capacity, glycemic index (GI) and glycemic load were evaluated.

The main sensory attributes in the powders were chocolate, bitter, astringent, grass/linseed flavors (p < 0.05). The product has protein [66.9%], carbohydrates [22.0%], lipids [1.6%], ash [2.7%] and moisture [6.8%], with wettability (23 s), and dispersibility of 77.9%. Catechin, epicatechin, gallocatechin, procyanidin B2, chlorogenic, coumaric and ferulic acids were identified. GI and caloric load not show differences between men (73.3±2.4, 4.4±0.1) and women (67.0±2.1, 4.1±0.1) (p > 0.05).

The use of mesquite pods, oak and agave fructans in powder food supplement is an alternative to obtain a product high in protein, with good sensory properties, antioxidant activity and moderate GI.

The purpose of this viewpoint article is to serve as a discussion starting point regarding organizational leadership’s increasing reliance on AI – in particular, how the technology is used as a supplemental tool for supporting rational decision-making. Practical implications and directions for further research are presented.

With its inception in economics, the concept of rationality has a rich history across multiple research domains. Based on that literature, coupled with the recent advancements in AI, the paper asks: will AI afford organizational leadership the ability to move from making bounded rational decisions to making fully rational decisions? The paper only scratches the surface of such a large question; however, the goal is to start the discussion around the topic.

While bounded rationality supports efficient decision-making, a complete understanding of any given decision is typically limited, and as a result, neither accuracy nor effectiveness is guaranteed. As AI systems grow in speed and accuracy, they should provide positive support for organizational leaders to make fully rational decisions. AI’s ability to collect and organize data, analyze it, and offer decision alternatives may help close the gap between bounded and rational decision-making.

Although AI research is not new, the recent developments in natural language processing engines has rapidly brought about new possibilities for their use in rational decision-making in the business and organizational context. This is fertile ground for future research, particularly in the area of organizational decision-making.

The primary purpose of this exploratory paper is to propose a novel analytical framework for examining corruption from a behavioral perspective by highlighting multiple issues associated with consumerism.

This paper examines the relationship between excessive consumption activities and corrupt acts, drawing upon existing literature on corruption, consumerism and consumption, as well as multiple reports and cases of corruption and money laundering in Indonesia. With regard to corruption networks, this paper analyses the associated behavioral patterns and social dynamics by using the Fraud Triangle and the Fraud Elements Triangle frameworks to examine the phenomenon of living beyond one’s means. This paper also addresses the notion of sacredness in the context of consumer activities and how such sacredness plays a role in causing otherwise honest individuals to engage in corrupt acts.

The author established that corruption represents a complex societal issue that extends across several dimensions of society, encompassing both horizontal and vertical aspects. Consequently, addressing this problem poses significant challenges. Excessive consumption has been identified as one of the various behavioral concerns that are implicated in the widespread occurrence of corruption in many nations. Individuals who partake in excessive consumption play a role in shaping ethical norms that serve to legitimize and rationalize immoral behavior, therefore fostering a society marked by corruption. The act of engaging in excessive consumption is also associated with cases of money laundering offenses that are connected to corruption and several other illicit activities. The lifestyle of corrupt individuals is one of the primary behavioral concerns associated with corruption, as “living beyond means” is the most common behavioral red flag among occupational fraud offenders worldwide. The phenomenon of consumerism may also shape the minds of individuals as if it were an “implicit religion” due to the fact that it may generate human experiences that elicit highly positive emotions and satisfy certain sacredness-associated characteristics. The pursuit of transcendental experiences through the acquisition and consumption of sacred consumption objects may heighten the incentive to commit fraudulent acts such as corruption.

This self-funded exploratory study uses document analysis to examine the corruption phenomenon in Indonesia. Future studies will benefit from in-depth interviews with former offenders and investigators of corruption.

This exploratory study contributes to advancing corruption prevention strategies. It does this by introducing a novel analytical framework that allows for the examination of several behavioral issues associated with consumerism, which have the potential to foster the proliferation of corruption.

This exploratory study highlights the importance of comprehending the intricacies of consumerism, namely, its adverse effects on the proliferation of corruption.