Search results

The purpose of this paper is to supply an in-depth description of the educators’ values, beliefs and confidence changing from a traditional learning environment to a learning environment integrating technology.

The descriptive case study design was employed using descriptive statistical analysis and inductive analysis on the data collected.

Themes on a high level of confidence, the importance of professional development and training, self-motivation, and excitement about the way technology can enhance the learning, along with concerns over the lack of infrastructure and support for integrating technology, and about the ability of students to use the technology tools for higher ordered thinking surfaced.

Additional research may include a more diverse population, including educators at the kindergarten to high school level. Another recommendation would be to repeat the study with a population not as vested in technology.

A pre-assessment of the existing values, beliefs and confidence of educators involved in the change process will provide invaluable information for stakeholders on techniques and strategies vital to a successful transition.

To effectively meet the learning styles of Generation Z and those students following, educators need be able to adapt to quickly changing technology, be comfortable with students who multitask and be open to technology-rich teaching and learning environments.

This study filled a gap in the literature where little information on the humanistic challenges educators encounter when integrating technology into their learning environment providing insights into the values, beliefs and level of confidence of educators experiencing change.

This study aims to provide preliminary information to the investor by determining which indices co-movement, with the data mining method.

In this context, data sets containing daily opening and closing prices between 2001 and 2019 have been created for 11 stock market indexes in the world. The association rule algorithm, one of the data mining techniques, is used in the analysis of the data.

It is observed that the US stock market indices take part in the highest confidence levels between association rules. The XU100 stock index co-movement with both the European stock market indices and the US stock indices. In addition, the Hang Seng Index (HSI) (Hong Kong) takes part in the association rules of all stock market indices.

The important issue for data sets is that the opening/closing values of the same day or the previous day are taken into account according to the open or closed status of other stock market indices by taking the opening time of the stock exchange index to be created. Therefore, data sets are arranged for each stock market index, separately. As a result of this data set arranging process, it is possible to find out co-movements of the stock market indexes. It is proof that the world stock indices have co-movement, and this continues as a cycle.

The main purpose of this research is to investigate factors influencing rural women entrepreneurship development and sustainable rural livelihoods in Manicaland province of Zimbabwe.

A quantitative research was conducted in Manicaland province in Zimbabwe. Data were collected through structured questionnaires from 400 women entrepreneurs in various sectors. The participants were in vegetable vending, operating clothing flea markets and cross border trading. A self-administered structured questionnaire was used to collect data from respondents. Structural equation modeling in SmartPLS version 3 was used to test the research hypotheses.

The study established that women entrepreneurship is driven by financial factors, positive environmental factors, positive psychological factors as well as positive sociological factors for a sustainable rural livelihood.

It is clear that if the discovered challenges are not addressed, sustainability of women entrepreneurship will remain a dream.

The study came up with strategies for improving women entrepreneurship activities. Future research can be done in other areas of provinces to avoid generalization challenges.

Many challenges hinder the sustainability of women entrepreneurship. Major impediments to women entrepreneurship comprises inadequate support from government schemes, patriarchal societal structure of the community, lack of relevant entrepreneurial knowledge to manage businesses, lack of collateral security to access funding, time limitation or role conflict to balance family pressures and business.

The study recommends proper entrepreneurship education and training, supportive government schemes and access to network affiliation/connection to sustain women entrepreneurship.

This study aims to examine the influence of service quality, satisfaction, trust, value and commitment on hotel customers’ attitudinal and behavioural loyalty.

The purpose of this paper is to look into the mechanism in which customers involve themselves in omni-channel retail setting and use its advantages.

Via an empirical analysis through surveying customers, this paper assesses and confirms the drivers of omni-channel shopping intention within the context of fashion retailing sector in Danang.

The findings highlight the significance of customer perception of research shopping (including showrooming and webrooming) behaviours, compatibility and risk to their intention towards omni-channel shopping, implying profound understanding of designing effective omni-channel retailing strategy.

From a theoretical perspective, comprehending customer perception of the omni-channel concept has emerged as an important theme in recent literature as well as in practitioners' reports. Hence, the meaningful contribution of this study is the involvement in the attractive steam of study. From a managerial perspective, this study could offer guidance to retailers or managers about developing a successful omni-channel strategy from a customer point of view.

This research's main objective is to investigate the relationship between consumption expenditure and consumer confidence in the USA and to study their effects on US economic revivalism during and after the coronavirus disease 2019 (COVID-19) shock.

The authors use Michigan's monthly Consumer Sentiment Index and its five components from January 1978 to April 2020. The study is unique in quantifying the potential variations in US consumer confidence due to COVID-19 under different scenarios, by providing a projection until December 2021. It also estimates the time needed for recovery and offers guidance to policymakers on ways to contain the negative impacts of COVID-19 on the economy by restoring consumer confidence.

All scenarios show a gradual recovery of consumer confidence and consumption expenditure. This study recommends expansionary policies to encourage consumption expenditure to generate additional demand and boost economic growth and job creation.

Though this study is limited to the US consumer confidence index, it offers significant implications for marketers, customers and policymakers of other developed economies. The authors recommend expansionary economic policies to boost consumer confidence, raise economic growth and result in job creation.

The study is unique in quantifying the potential variations in US consumer confidence due to COVID-19 under different scenarios; by providing a projection until December 2021. It also estimates the time needed for recovery and guidance for policymakers on ways to contain the COVID-19 shock negative impacts on the economy by restoring consumer confidence.

The authors derive the necessary mathematics, provide computer simulations, provide links to free and user-friendly computer programs, and analyze real data sets.

Cohen's d, which indexes the difference in means in standard deviation units, is the most popular effect size measure in the social sciences and economics. Not surprisingly, researchers have developed statistical procedures for estimating sample sizes needed to have a desirable probability of rejecting the null hypothesis given assumed values for Cohen's d, or for estimating sample sizes needed to have a desirable probability of obtaining a confidence interval of a specified width. However, for researchers interested in using the sample Cohen's d to estimate the population value, these are insufficient. Therefore, it would be useful to have a procedure for obtaining sample sizes needed to be confident that the sample. Cohen's d to be obtained is close to the population parameter the researcher wishes to estimate, an expansion of the a priori procedure (APP). The authors derive the necessary mathematics, provide computer simulations and links to free and user-friendly computer programs, and analyze real data sets for illustration of our main results.

In this paper, the authors answered the following two questions: The precision question: How close do I want my sample Cohen's d to be to the population value? The confidence question: What probability do I want to have of being within the specified distance?

To the best of the authors’ knowledge, this is the first paper for estimating Cohen's effect size, using the APP method. It is convenient for researchers and practitioners to use the online computing packages.

The link between confidence and economic decisions has been widely covered in the economic literature, yet it is still an unexplored field in tourism. The purpose of this paper is to address this gap, and investigate benefits in forecast accuracy that can be achieved by combining the UNWTO Tourism Confidence Index (TCI) with statistical forecasts.

Research is conducted in a real-life setting, using UNWTO unique data sets of tourism indicators. UNWTO TCI is pooled with statistical forecasts using three distinct approaches. Forecasts efficiency is assessed in terms of accuracy gains and capability to predict turning points in alternative scenarios, including one of the hardest crises the tourism sector ever experienced.

Results suggest that the TCI provides meaningful indications about the sign of future growth in international tourist arrivals, and point to an improvement of forecast accuracy, when the index is used in combination with statistical forecasts. Still, accuracy gains vary greatly across regions and can hardly be generalised. Findings provide meaningful directions to tourism practitioners on the use opportunity cost to produce short-term forecasts using both approaches.

Empirical evidence suggests that a confidence index should not be collected as input to improve their forecasts. It remains a valuable instrument to supplement official statistics, over which it has the advantage of being more frequently compiled and more rapidly accessible. It is also of particular importance to predict changes in the business climate and capture turning points in a timely fashion, which makes it an extremely valuable input for operational and strategic decisions.

The use of sentiment indexes as input to forecasting is an unexplored field in the tourism literature.

The purpose of this single case study is to examine secondary-certificate-seeking preservice teachers' (PST) perceptions of their teaching practice.

This single case study used student responses to a two-part reflection assignment to examine what it revealed about PST self-efficacy.

The findings revealed: (1) PSTs were generally more confident when reflecting in a second reflection assignment, (2) there were points of tension between confidence and unease, (3) there were instances of PSTs with mixed confidence and (4) some PSTs crafted plans for their future teaching. The authors further discuss these findings by exploring how PSTs reflected on their teaching experiences, and the authors reflected on the role of teacher educators in modeling this reflective practice for PSTs.

This study has important implications for teacher preparation programs and teacher educators, particularly those who work with PSTs in clinical experiences.

This study aims at evaluating the effect of the COVID-19 pandemic on the export trade system for Mauritius during the first half of 2020 (January 2020–June 2020).

An initial analysis of the monthly export time series data proves that on the whole, the series have diverged from their actual trends after the outbreak of the COVID-19 pandemic: observed values are less than those predicted by the selected optimal forecast models. The authors subsequently employ the Bayesian structural time series (BSTS) framework for causal analysis to estimate the impact of the COVID-19 pandemic on the island's export system.

Overall, the findings show that the COVID-19 pandemic has a statistically significant and negative impact on the Mauritian export trade system, with the five main export trading partners and sectors the most affected. Despite that the impact in some cases is not apparent for the period of study, the results indicate that total exports will surely be affected by the pandemic in the long run. Nevertheless, this depends on the measures taken both locally and globally to mitigate the spread of the pandemic.

This study thus contributes to the growing literature on the economic impacts of the COVID-19 pandemic by focussing on a small island economy.