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1 – 10 of 24John C. Pruit, Carol Rambo and Amanda G. Pruit
This performance autoethnography may or may not be interpreted as a continuation of a conversation regarding the experiences of those with devalued statuses in academic settings…
Abstract
This performance autoethnography may or may not be interpreted as a continuation of a conversation regarding the experiences of those with devalued statuses in academic settings. The authors rely on “strange accounting” to consider their experiences in the academy from various standpoints: before and after promotion, before and after leaving academia. While reflecting on our past experiences, we introduce the concept of “everyday precariousness” as a way of explaining the normalization of instability, insecurity, and negative affect that is part of everyday life for those with devalued statuses in academic settings and beyond. Everyday precariousness is an embodied experience for those in vulnerable positions. Normalized exposure to risks, such as discrimination, harassment, bullying, or structural instability, produces an undercurrent of threat that permeates academic culture. Our stories of everyday precariousness span race, ethnicity, class, academic roles, and gender boundaries (among many others). Analyzing these experiences furthers previous work on the uses of strange accounting as well as the dynamics of status silencing. In the final analysis, unresisted and unabated, everyday precariousness and status silencing can lead to institutional failure and resonance disasters.
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Thalia Anthony, Juanita Sherwood, Harry Blagg and Kieran Tranter
Patrice Silver, Juliann Dupuis, Rachel E. Durham, Ryan Schaaf, Lisa Pallett and Lauren Watson
In 2022, the Baltimore professional development school (PDS) partner schools, John Ruhruh Elementary/Middle School (JREMS) and Notre Dame of Maryland University (NDMU) received…
Abstract
Purpose
In 2022, the Baltimore professional development school (PDS) partner schools, John Ruhruh Elementary/Middle School (JREMS) and Notre Dame of Maryland University (NDMU) received funds through a Maryland Educational Emergency Revitalization (MEER) grant to determine (a) to what extent additional resources and professional development would increase JREMS teachers’ efficacy in technology integration and (b) to what extent NDMU professional development in the form of workshops and self-paced computer science modules would result in greater use of technology in the JREMS K-8 classrooms. Results indicated a statistically significant improvement in both teacher comfort with technology and integrated use of technology in instruction.
Design/methodology/approach
Survey data were collected on teacher-stated comfort with technology before and after grant implementation. Teachers’ use of technology was also measured by unannounced classroom visits by administration before and after the grant implementation and through artifacts teachers submitted during NDMU professional development modules.
Findings
Results showing significant increases in self-efficacy with technology along with teacher integration of technology exemplify the benefits of a PDS partnership.
Originality/value
This initiative was original in its approach to teacher development by replacing required teacher professional development with an invitation to participate and an incentive for participation (a personal MacBook) that met the stated needs of teachers. Teacher motivation was strong because teammates in a strong PDS partnership provided the necessary supports to induce changes in teacher self-efficacy.
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Dhruba Jyoti Borgohain, Raj Kumar Bhardwaj and Manoj Kumar Verma
Artificial Intelligence (AI) is an emerging technology and turned into a field of knowledge that has been consistently displacing technologies for a change in human life. It is…
Abstract
Purpose
Artificial Intelligence (AI) is an emerging technology and turned into a field of knowledge that has been consistently displacing technologies for a change in human life. It is applied in all spheres of life as reflected in the review of the literature section here. As applicable in the field of libraries too, this study scientifically mapped the papers on AAIL and analyze its growth, collaboration network, trending topics, or research hot spots to highlight the challenges and opportunities in adopting AI-based advancements in library systems and processes.
Design/methodology/approach
The study was developed with a bibliometric approach, considering a decade, 2012 to 2021 for data extraction from a premier database, Scopus. The steps followed are (1) identification, selection of keywords, and forming the search strategy with the approval of a panel of computer scientists and librarians and (2) design and development of a perfect algorithm to verify these selected keywords in title-abstract-keywords of Scopus (3) Performing data processing in some state-of-the-art bibliometric visualization tools, Biblioshiny R and VOSviewer (4) discussing the findings for practical implications of the study and limitations.
Findings
As evident from several papers, not much research has been conducted on AI applications in libraries in comparison to topics like AI applications in cancer, health, medicine, education, and agriculture. As per the Price law, the growth pattern is exponential. The total number of papers relevant to the subject is 1462 (single and multi-authored) contributed by 5400 authors with 0.271 documents per author and around 4 authors per document. Papers occurred mostly in open-access journals. The productive journal is the Journal of Chemical Information and Modelling (NP = 63) while the highly consistent and impactful is the Journal of Machine Learning Research (z-index=63.58 and CPP = 56.17). In the case of authors, J Chen (z-index=28.86 and CPP = 43.75) is the most consistent and impactful author. At the country level, the USA has recorded the highest number of papers positioned at the center of the co-authorship network but at the institutional level, China takes the 1st position. The trending topics of research are machine learning, large dataset, deep learning, high-level languages, etc. The present information system has a high potential to improve if integrated with AI technologies.
Practical implications
The number of scientific papers has increased over time. The evolution of themes like machine learning implicates AI as a broad field of knowledge that converges with other disciplines. The themes like large datasets imply that AI may be applied to analyze and interpret these data and support decision-making in public sector enterprises. Theme named high-level language emerged as a research hotspot which indicated that extensive research has been going on in this area to improve computer systems for facilitating the processing of data with high momentum. These implications are of high strategic worth for policymakers, library stakeholders, researchers and the government as a whole for decision-making.
Originality/value
The analysis of collaboration, prolific authors/journals using consistency factor and CPP, testing the relationship between consistency (z-index) and impact (h-index), using state-of-the-art network visualization and cluster analysis techniques make this study novel and differentiates it from the traditional bibliometric analysis. To the best of the author's knowledge, this work is the first attempt to comprehend the research streams and provide a holistic view of research on the application of AI in libraries. The insights obtained from this analysis are instrumental for both academics and practitioners.
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Md Rakibul Hasan, Yosef Daryanto, Chefi Triki and Adel Elomri
The rapidly growing e-commerce industry with its special characteristics brings new challenges to the optimization of the supply chain and inventory management. This study aims to…
Abstract
Purpose
The rapidly growing e-commerce industry with its special characteristics brings new challenges to the optimization of the supply chain and inventory management. This study aims to investigate the inventory-related optimization of an e-marketplace official store that works on a business-to-customer system when cashback promotion is used to attract more customers. Also, it proposes a new inventory model to maximize the e-commerce profit by optimizing the cashback amount and delivery period.
Design/methodology/approach
The proposed model assumes that customer demand is a function of price and delivery time and that price is affected by the cashback amount. The e-commerce operator has a profit-sharing contract with an e-payment company that facilitates the payment. E-commerce also builds collaboration under a cost-sharing contract with a supplier to ensure product delivery. A mathematical model is developed and the related theories are investigated. A numerical example illustrates the validity of the model and a sensitivity analysis is carried out to give useful insights.
Findings
A new inventory model for an e-market system has been introduced which shows the impact of a cashback promotion on the e-commerce business. This study shows that managers can optimize the cashback amount and its delivery time to get the maximum profit. In certain cases, the manager may set a high cashback amount (e.g. 100%) to attract customers to place more orders.
Originality/value
This study presents a new inventory model for today’s fast-growing e-commerce business; therefore, the results contribute to the understanding of promotion program practices and inventory management and provide insights to develop efficient e-commerce managerial decisions.
Graphical abstract
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Carlos Gastelum-Acosta, Jorge Limon-Romero, Yolanda Baez-Lopez, Diego Tlapa, Jorge Luis García-Alcaraz, Cesar Puente and Armando Perez-Sanchez
This paper aims to identify the relationships among critical success factors (CSFs) for lean six sigma (LSS) implementation in higher education institutions (HEIs).
Abstract
Purpose
This paper aims to identify the relationships among critical success factors (CSFs) for lean six sigma (LSS) implementation in higher education institutions (HEIs).
Design/methodology/approach
An extensive literature review was conducted to design the survey instrument, which the authors later administered in Mexican public HEIs to identify the existing relationships among the CSFs and their impact on the benefits obtained from implementing LSS projects. The data were empirically and statistically validated using exploratory and confirmatory factor analysis. Additionally, the authors applied the structural equation modeling (SEM) technique on SPSS Amos to validate the nine hypotheses supporting the research.
Findings
The results suggest that the success of LSS projects in HEIs is highly bound to a serious commitment from top management and several interrelated factors.
Research limitations/implications
The main limitations of the study are that the research is cross-sectional in nature and regional in focus. Namely, the data used to validate the structural model were gathered from a small representative subset of the study population – i.e. Mexican public HEIs – and at a specific point in time.
Practical implications
The results reported here represent a reference framework for HEIs worldwide that wish to continuously improve their processes through LSS improvement projects.
Originality/value
This study proposes a statistically validated model using the SEM technique that depicts the relationships among LSS CSFs in HEIs.
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Sachin Kashyap, Sanjeev Gupta and Tarun Chugh
The present work has proposed and employed an innovative hybrid method based on the combination of factor analysis and an artificial neural network (ANN) model to forecast…
Abstract
Purpose
The present work has proposed and employed an innovative hybrid method based on the combination of factor analysis and an artificial neural network (ANN) model to forecast customer satisfaction from the identified dimensions of service quality in India, a developing country.
Design/methodology/approach
The qualitative study is conducted with Internet banking users to understand e-banking clients' perceptions. The data is collected with the help of a questionnaire from randomly selected 208 customers in India. Firstly, factor analysis was performed to determine the influential factors of customer satisfaction, and four factors i.e. efficiency, reliability, security and privacy, and issue and problem handling were extracted accordingly. The neural network model is then applied to the factor scores to validate the key elements. Lastly, the comparative analysis of the actual ANN and the regression predicted result is done.
Findings
The success ability of the linear regression model is challenged when approximated to nonlinear problems such as customer satisfaction. It is concluded that the ANN model is a better fit than the linear regression model, and it can recognise the complex connections between the exogenous and endogenous variables. The results also show that reliability, security and privacy are the most influencing factors; however, problem handling and efficiency have the slightest effect on bank client satisfaction.
Research limitations/implications
This research is conducted in India, and the sample is chosen from the urban area. The limitation of the purposeful sampling technique and the cross-sectional nature of the data may hamper the generalisation of the results.
Originality/value
The conclusions from the study will be helpful for policymakers, bankers and academicians. To our knowledge, few studies used ANN modelling to predict customer satisfaction in the service sector
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Wiwit Ratnasari, Tzu-Chuan Chou and Chen-Hao Huang
This paper examines the evolution of massive open online courses (MOOCs) literature over the past 15 years and identifies its significant developments.
Abstract
Purpose
This paper examines the evolution of massive open online courses (MOOCs) literature over the past 15 years and identifies its significant developments.
Design/methodology/approach
Utilizing main path analysis (MPA) on a dataset of 1,613 articles from the Web of Science (WoS) databases, the authors construct the main pathway in MOOC literature through a citation analysis. Pajek software is used to visualize the 34 influential articles identified in the field.
Findings
Three phases emerge in MOOC research: connectivism as a learning theory, facilitating education reform and breaking barriers to MOOCs adoption. Multiple-Global MPA highlights sub-themes including self-regulated learning (SRL), motivation, engagement, dropouts, student performance and the impact of COVID-19.
Research limitations/implications
First, data limitations from the WoS core collection might not cover all research, but using reputable sources enhances data validity. Second, despite careful algorithm selection to enhance accuracy, there remains a limitation inherent in the nature of citations. Such biased citations may result in findings that do not fully align with scholars' perspectives.
Practical implications
The authors' findings contribute to the understanding of MOOCs literature development, enabling educators and researchers to grasp key trends and focus areas in the field. It can inform the design and implementation of MOOCs for more effective educational outcomes.
Originality/value
This study presents novel methodologies and important findings for advancing research and practice in MOOCs.
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Shalini Talwar, Puneet Kaur, Sushant Kumar, Michel Laroche and Amandeep Dhir
The use of over-the-top (OTT) platforms grew substantially after the declaration of the COVID-19 pandemic in 2020. With the pandemic receding, there is a concern that users may…
Abstract
Purpose
The use of over-the-top (OTT) platforms grew substantially after the declaration of the COVID-19 pandemic in 2020. With the pandemic receding, there is a concern that users may not continue with their subscriptions. To counter this, OTT service providers must strategize proactively to retain and acquire new users once the pandemic abates. Positing that understanding the consumption values that users ascribe to OTT platform usage can provide useful customer retention insights, the purpose of this paper is to use the theory of consumption value (TCV) to study the values that users derived from their use of OTT following the onset of the pandemic.
Design/methodology/approach
The mixed-method approach is used to collect qualitative and quantitative data. Analysis of qualitative responses collected through interviews of 12 current OTT platform users helped identify two categories of OTT platform-specific values: attribute-level and benefit-based. Next, the study examined the association of values thus identified with one another, as well as with continued intentions to use OTT platforms, by analyzing data collected from 371 existing users.
Findings
The findings indicated that functional value quality and social value, representing the attribute-level values, were positively associated with two benefit-based values – functional value price and emotional value (EMV). Next, EMV was not only associated with intentions but also partially mediated the association of attribute-level values with intentions. Premium subscription purchased and increased viewing time were confirmed to have moderating effects on the association between attribute-level and benefit-based values.
Originality/value
The study is amongst the foremost research initiatives to examine consumption values derived from OTT platform usage after the onset of the pandemic. Its novelty also comes from its identifying OTT platform-specific consumption values for the first time and adding a new dimension to the TCV by examining the interplay of these values in the OTT platform context.
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