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Mobile self-checkout refers to scanning products using a mobile device inside a brick-and-mortar store and completing the checkout process on mobile devices. Even though mobile self-checkout has been used in other industries for several years, it is a new application in the fashion industry and only limited numbers of retailers have implemented mobile self-checkout in their stores. The purpose of this study is to understand consumers' acceptance of mobile self-checkout in fashion retail stores by analyzing determinants of using a new system.

Part of the Unified Theory of Acceptance and Use of Technology (UTAUT) was used as a theoretical framework. Openness to experience, variety seeking and adventure shopping were added to the model. Empirical data (with 229 valid responses) were collected from the top 20 metropolitan areas in the US via Qualtrics Panel services. Exploratory factor analysis, confirmatory factor analysis, structural equation modeling and multi-group moderation were used to estimate construct validity and test the proposed hypotheses and theoretical framework.

The results indicated that consumers' intentions toward using mobile self-checkout in fashion retail stores were predicted by facilitating conditions, social influence and openness to experience. Moreover, consumers' previous experience of using mobile self-checkout in fashion retail stores moderated the path from facilitating conditions to behavioral intention and the path from social influence to behavioral intention. In addition, different genders and smartphone usage frequency did not vary significantly on the model paths.

The findings show how fashion retailers can understand consumers' preference and their willingness to use mobile self-checkout in fashion retail stores. Moreover, the authors addressed ways for fashion retailers to promote mobile self-checkout in the future.

As a new technology in the fashion industry, literature is deficient concerning consumers' intention to adopt mobile self-checkout. This research provided suggestions for fashion retailers about adopting and improving acceptance of mobile self-checkout. Results will lead to theoretical and managerial implications for future technology development.

This research studies the development of the evolving dynamic system model and explores the important elements or factors and what detailed attributes are the main influences model in achieving the success of a business, industry and management. It also identifies the real and major differences between static and dynamic business management models and the detailed factors that influence them. Later, this research investigates the benefits/advantages and limitations/disadvantages of some research studies. The studies conducted in this research put more emphasis on the capabilities of system dynamics (SD) in modeling and the ability to measure, analyse and capture problems in business, industry, manufacturing etc.

The research presented in this work is a qualitative research based on a literature review. Publicly available research publications and reports have been used to create a research foundation, identify the research gaps and develop new analyses from the comparative studies. As the literature review progressed, the scope of the literature search was further narrowed down to the development of SD models. Often, references to certain selected literature have been examined to find other relevant literature. To do so, a supporting tool (that connects related articles) provided by Google Scholar, Scopus, and particular journals has been used.

The dynamic business and management model is very different from the static business model in complexity, formality, flexibility, capturing, relationships, advantages, innovation model, new goals, updated information, perspective and problem-solving abilities. The initial approach of a static system was applied in the canvas business model, but further developments can be continued with a dynamic system approach.

Based on this study, which shows that businesses are developing more towards digitalisation, wanting the ability to keep up with the era that is moving so fast and the desire to increase profits, an instrument is needed that can help describe the difficulties of the needs and developments of the future world. This instrument, or tool of SD, is also expected to assist in drawing future models and in building a business with complex variables that can be predicted from the beginning.

This study will contribute to the SD study for many business incubator research studies. Many practical in business incubator management to have a benefit how to achieve the business performance management (BPM) in SD review.

The significant differences between static and dynamics to be used for business research and strategic performance management. This comparative study analyses some SD models from many authors worldwide. Their goals behind their strategic business models and encounter for their respective progress.

The purpose of this paper is to develop a chemotherapy scheduling template that accounts for nurse resource availability and patient treatment needs to alleviate the mid-day patient load and provide quality services for patients.

Owing to treatment complexity in chemotherapy administration, nurses are required at the beginning, end and during treatment. When nurses are not available to continue treatment, the service is compromised, and the resource constraint is violated, which leads to inevitable delay that risks service quality. Consequently, an optimization method is used to create a scheduling template that minimizes the violation between resource assignment and treatment requirements, while leveling patient load throughout a day. A case study from a typical clinic day is presented to understand current scheduling issues, describe nursing resource constraints, and develop a constraint-based optimization model and leveling algorithm for the final template.

The approach is expected to reduce the variation in the system by 24 percent and result in five fewer chemo chairs used during peak hours. Adjusting staffing levels could further reduce resource constraint violations and more savings on chair occupancy. The actual implementation results indicate a 33 percent reduction on resource constraint violations and positive feedback from nursing staff for workload.

Other delays, including laboratory test, physician visit and treatment assignment, are potential research areas.

The study demonstrates significant improvement in mid-day patient load and meeting treatment needs using optimization with a unique objective.

The purpose of this study was to develop a new folding method for modeling complicated folded fabric with surfaces of revolution.

Irregular wrinkles and mesh distortions easily appear in the fold modeling of a complex curved surface. Aimed at this key technical problem, the segmentation mapping folding method (SMFM) is proposed in this paper. First, high-precision flattened planes were obtained by using segmentation mapping techniques. Second, the segmented planes were transformed into a folded and continuous geometric model by using matrix transformations. Finally, initial stress was used to modify the geometric folding errors, which ensured agreement with the inflated flexible fabric’s geometry and the original design.

Compared with the traditional folding method, SMFM has the advantages of good finite-element mesh quality, large radial compression rate, regular folds, etc. The surface area error and the volume error of the inflated single torus established by SMFM were only 1.2 per cent, showing that SMFM has high modeling accuracy. The numerical results of an inflatable re-entry vehicle are presented to demonstrate the reliability, feasibility and applicability of SMFM. Moreover, the stress modification reduced the problems of stress concentration and mesh distortions, improving the accuracy and stability of the numerical calculations.

In this paper, for the first time, a folding method for modeling complicated folded fabric is proposed. This methodology can be used to model the multidimensional compression and regular folds of complex surfaces of revolution that cannot be flattened and to improve the accuracy and stability of the numerical calculations.

The purpose of this study is to apply altmetrics and bibliometric indicators on the top 100 most mentioned articles published related to the sustainable development goal (SDG)-13, Climate Action.

The authors used the Altmetric Explorer’s SDGs filter to extract the most mentioned articles belonging to Climate Action and their other characteristics, such as DOI, titles, tools mentioning them and their demographic descriptions. The same set of papers was searched in the Dimensions database to extract them in the format importable in R-studio to check the distribution of papers across various journals and identify their subject category, countries and institutions publishing these papers. Further, SPSS was used to check the correlation between altmetric attention score (AAS) and citations.

The results of the paper showed the mean of AAS and the citations received by the articles was 3,556.35 and 304.04, respectively. Twitter has been the most used social media platform for mentioning the research related to climate action, covering 88.1% of the total mentions. The Twitter and the News mention demographics show the USA contributing the most tweet mentions (15.2%) as well as news mentions (57.65%) to the papers. Also, the USA has solely published 49 papers from the total papers selected for the study. The papers were published in 31 journals most of them belonging to the quartile first (Q1) category and primarily belonged to the subject category “Earth Sciences.” Pearson’s correlational method showed a significant but low positive correlation between AAS and citation counts (r = 0.365, p = <0.001) and a strong positive correlation between the citations and Mendeley readership counts (r = 0.907).

The research is original in nature and discovered very interesting results about climate action using altmetric and bibliometric techniques.

The study aims to draw lessons from other countries and propose a framework for developing smart libraries in transformation in South Africa. The framework will be useful to policymakers and scholars in library and information science.

This study used a qualitative approach based on a systematic literature review and the researchers’ experience concerning the transformation of smart libraries in South Africa. The researchers selected databases to access full-text, abstract or full-text linking facilities. The researchers generated the study’s literature review using keywords derived from themes. The keywords used include “smart libraries,” “functions of smart libraries” and “smart library infrastructure,” based on the objectives of the study.

The findings reveal that developing smart libraries in South Africa is feasible, but all stakeholders must cooperate and collaborate to create smart libraries. The findings of this study show that South Africa can draw lessons from other countries on the way forward in developing smart libraries. The results indicate that lack of funding, digital infrastructure and technical skills are among the factors inhibiting the development of smart libraries.

The issue of smart libraries has become a key on the agenda on a global scale, and South Africa is no exception to such developments. This study proposes a framework for developing smart libraries based on lessons learned within and outside South Africa.

The aim of the paper is to analyze quantitatively and qualitatively requirements of Russian micro‐ and small‐firms in financial sources, along with opportunities and restrictions in the mobilization of investment at the different stages of a firm's life cycle.

In this paper the determinants of the propensity to invest and the supply of funding are investigated by using the Global Entrepreneurship Monitor (GEM) data set for Russia in the time period from 2006 to 2011.

The paper provides the analysis of Russian early entrepreneurs' and established business owners' decisions about the preferred structure of financial sources, comprising both statistical and logistic regression approaches for this investigation. The findings indicate that in Russia the structure of financial sources of start‐up entrepreneurs is predominated by “love capital” (mainly private and family savings), meanwhile, the percentage of business angels' financing is low in comparison with innovation‐driven countries. Moreover, there are merely extra‐economic factors, which influence informal investors' decision making on funding: personal relations with a borrower, an optimistic view on macroeconomic perspective and high status of an entrepreneur.

The findings in this paper suggest that this research can help the officials to formulate a program of SMEs' support at different stages of the financial chain in Russia.

In this paper the early and middle stages of a firm's life cycle are examined and some practical advice on a company's development and expansion are given.

With the massive use of fossil energy polluting the natural environment, clean energy has gradually become the focus of future energy development. The purpose of this article is to propose a new hybrid forecasting model to forecast the production and consumption of clean energy.

Firstly, the memory characteristics of the production and consumption of clean energy were analyzed by the rescaled range analysis (R/S) method. Secondly, the original series was decomposed into several components and residuals with different characteristics by the ensemble empirical mode decomposition (EEMD) algorithm, and the residuals were predicted by the fractional derivative grey Bernoulli model [FDGBM (p, 1)]. The other components were predicted using artificial intelligence (AI) models (least square support vector regression [LSSVR] and artificial neural network [ANN]). Finally, the fitting values of each part were added to get the predicted value of the original series.

This study found that clean energy had memory characteristics. The hybrid models EEMD–FDGBM (p, 1)–LSSVR and EEMD–FDGBM (p, 1)–ANN were significantly higher than other models in the prediction of clean energy production and consumption.

Consider that clean energy has complex nonlinear and memory characteristics. In this paper, the EEMD method combined the FDGBM (P, 1) and AI models to establish hybrid models to predict the consumption and output of clean energy.