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The lecture method has been compared with teaching methods such as flip learning, cooperative learning and simulations to establish which holds the key to students' understanding of concepts. What is bereft in the education literature is its comparative efficiency with the culturo-techno contextual approach (CTCA) in the teaching of computer science education.

This study adopted the quasi-experimental design to determine the efficacy of the CTCA in breaking difficulties related to the study of spreadsheets as a difficult concept in the Nigerian computer science education curriculum. Junior high school students studying computer science education participated in the study. The control group had 30 students, with 35 students in the experimental group. The experimental group was taught using CTCA, while the control group used the lecture method. The spread sheet achievement test, which had 40 items on spreadsheet, was used to collect data.

The results showed that the experimental group significantly outperformed the control group [F (1,60) = 41.89; p < 0.05]. The findings showed the potential of CTCA in improving students' performance in spreadsheets in the computer science education curriculum.

The originality of this study is hinged on its ground-breaking test of the CTCA to the study of the spreadsheet. The findings of this study indicate its efficacy in improving students' understanding of spreadsheet and computer science education.

Extending the elaboration likelihood model (ELM), this study investigates the impact of social media influencers (SMI) on consumer well-being (CW) as well as the influence of CW on purchase intention.

A between-subjects experiment (macro- vs mega-influencer) was conducted to assess the proposed hypotheses. A total of 190 consumers participated in the experiment, and SmartPLS 3.3 was used for multigroup analyses.

Overall, argument quality (AQ), source's credibility (SC) and influencer's kindness positively predict CW, and CW predicts purchase intention. It was also found that SC is more important when information comes from a mega-influencer, whilst kindness is essential for a macro-influencer.

The results of this study imply that CW should be an essential component of influencer marketing strategy. Marketing managers should hire credible and kind influencers who can produce quality arguments. Additionally, the selection of SMI (macro- vs mega-influencer) should be aligned with the marketing objective and type of persuasion required.

This is one of the early attempts to extend ELM by introducing influencer kindness as a peripheral cue. Moreover, the study offers novelty by examining the effects of influencer characteristics (AQ, SC and kindness) on CW and comparing these effects across macro- and mega-influencers.

藉著擴展詳儘可能性模型, 本研究擬探討網絡紅人對消費者福祉的影響, 以及消費者福祉對購買意圖的影響。

研究人員進行被試間實驗 (中網紅對大型網紅) , 以對提出的假設進行評價。190名消費者參與實驗, 研究人員使用SmartPLS 3.3 進行多群組分析。

總的來說, 論點品質、來源可信度和網紅的仁慈體貼, 均能積極預測消費者福祉, 而消費者福祉亦可預測購買意圖。研究人員亦發現, 若資訊是來自大型網紅的話, 來源可信度則更形重要, 而對中網紅來說, 仁慈體貼則是不可或缺的。

研究結果暗示, 消費者福祉應是網紅市場營銷戰略的基本要素。市場經理應僱用可靠、仁慈體貼、並能提出優質論點的網紅。而且, 網絡紅人 (中網紅對大型網紅) 的挑選, 必須與營銷目標和說服的種類互相協調。

本研究為早期的嘗試, 利用引進網絡紅人的仁慈體貼作為周邊線索, 來擴展詳儘可能性模型。另外, 本研究探討網絡紅人的特徵 (論點品質、來源可信度和仁慈體貼) 會如何影響消費者福祉; 研究人員亦跨中網紅和大型網紅, 對這些影響進行比較, 就此而言, 本研究提供了創新的研究意念。

The purpose of this research is two-fold: first, the researchers will create a reliable and valid hospitality senior exit survey by conducting a Delphi panel of experts and second, the researchers will pilot-test the instrument with students from a four-year university. The data will be (a) assessed to determine the retention of knowledge by four-year students, (b) used by academicians to make changes to course material and (c) used to help with the accreditation assessment process.

A Delphi panel of hospitality educators was used to validate the items, and graduating hospitality students were used to calculate reliability.

By embracing the hospitality exit survey (HES), institutions can effectively evaluate and enhance their programs. With its ability to gauge students' knowledge retention, the study findings serve as a powerful tool for shaping the future of hospitality education.

The study's findings might be somewhat limited in representing a broader range of perspectives within hospitality programs. Another limitation stems from the structure of the survey itself. The survey included numerous items requiring two inputs for each item. This format has the potential to introduce certain biases among participants.

In a positive statement, organizations can use this information to discover why employees stay and then continue to develop goals/strategies to ensure this process stays up to date. Academia is no different. Academia also wants to produce the best product, and since the students are to become the next set of leaders, these programs need to know what is successful and what needs to be adjusted.

A strategic exit interview program should 1)Uncover issues relating to human resources/students; 2) understand employees'/students' perception of the work; 3) managers'/directors' leadership style and effectiveness; 4) human resource/college/departments benchmarks and 5) improve the organization.

This research holds significant importance as it focuses on developing the senior HES and its potential utilization within hospitality programs. The HES serves as a valuable tool for these programs to evaluate the knowledge levels of their graduating students and collect data necessary for assessment and accreditation purposes.

The purpose of this study is to provide insights and guidance for practitioners in terms of ensuring rigorous ethical and moral conduct in artificial intelligence (AI) hiring and implementation.

The research employed two experimental designs and one pilot study to investigate the ethical and moral implications of different levels of AI implementation in the hospitality industry, the intersection of self-congruency and ethical considerations when AI replaces human service providers and the impact of psychological distance associated with AI on individuals' ethical and moral considerations. These research methods included surveys and experimental manipulations to gather and analyze relevant data.

Findings provide valuable insights into the ethical and moral dimensions of AI implementation, the influence of self-congruency on ethical considerations and the role of psychological distance in individuals’ ethical evaluations. They contribute to the development of guidelines and practices for the responsible and ethical implementation of AI in various industries, including the hospitality sector.

The study highlights the importance of exercising rigorous ethical-moral AI hiring and implementation practices to ensure AI principles and enforcement operations in the restaurant industry. It provides practitioners with useful insights into how AI-robotization can improve ethical and moral standards.

The study contributes to the literature by providing insights into the ethical and moral implications of AI service robots in the hospitality industry. Additionally, the study explores the relationship between psychological distance and acceptance of AI-intervened service, which has not been extensively studied in the literature.

The study's objective is to estimate the association of specific perceived employer-provided benefits on employees' intention to leave in different age cohorts during coronavirus disease 2019 (COVID-19). Informed by the psychological theories of ageing, the authors propose three age-cohort-specific hypotheses in three motivational domains: security and health benefits, flexible work arrangement and education-related benefits.

The authors use a large survey of employees in Estonia (n = 7,209) conducted in 2020 and test the association of specific benefits and their interactions with age on employees' intention to leave.

The results show that older cohorts are generally less prone to leave their jobs. Benefits that employers could use during the COVID-19 crisis generally had negative associations with the intention to leave, but age-specific differences were negligible; only the perceived provision of flexible work arrangements reduced the younger cohort's intention to leave relatively more.

This study is one of the few that allows us to make inferences regarding the benefits preferences amongst the working population during an unprecedented health crisis.

To date, little research has been undertaken to test the effectiveness of team coaching, with past work focusing on models, frameworks and competencies. This study aimed to examine the effectiveness of team coaching within real world organizational teams and its impact on individual perceptions of team cohesion and psychological safety.

A randomized control trial (RCT) using the comparable interventions: (1) team coaching (intervention) and (2) team facilitation (control) was employed with multiple teams and multiple facilitators, measuring the impact on team cohesion and psychological safety.

The data indicate participants engaging in the team coaching intervention made greater gains in terms of their individual perceptions of psychological safety and team cohesion than individuals who received the team facilitation intervention (T1–T2).

Facilitators should apply a team coaching approach when seeking to address issues of cohesion and psychological safety within workplace teams.

This study provides the first evidence, using an RCT method, of the effectiveness of team coaching as a workplace intervention for enhancing individual perceptions of psychological safety and team cohesion.

This paper highlights an assignment in a combination upper-division undergraduate and graduate civic leadership class at a Midwestern state comprehensive university. The three-part assignment challenges students’ critical thinking skills and research capabilities while simultaneously necessitating the exploration of contrasting viewpoints on contentious issues.

Intentionally exposing students to diverse perspectives in a controlled environment.

We posit that the severity and frequency of these issues can be mitigated through focused efforts.

Students are better prepared to engage in civil debate on controversial topics, which continuously divide our communities, after completing a class using this pedagogical strategy.

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

Although proactive fault handling plans are widely spread, many unexpected data center outages still occurred. To rescue the jobs from faulty data centers, the authors propose a novel independent job rescheduling strategy for cloud resilience to reschedule the task from the faulty data center to other working-proper cloud data centers, by jointly considering job nature, timeline scenario and overall cloud performance.

A job parsing system and a priority assignment system are developed to identify the eligible time slots for the jobs and prioritize the jobs, respectively. A dynamic job rescheduling algorithm is proposed.

The simulation results show that our proposed approach has better cloud resiliency and load balancing performance than the HEFT series approaches.

This paper contributes to the cloud resilience by developing a novel job prioritizing, task rescheduling and timeline allocation method when facing faults.

The density peak clustering algorithm (DP) is proposed to identify cluster centers by two parameters, i.e. ρ value (local density) and δ value (the distance between a point and another point with a higher ρ value). According to the center-identifying principle of the DP, the potential cluster centers should have a higher ρ value and a higher δ value than other points. However, this principle may limit the DP from identifying some categories with multi-centers or the centers in lower-density regions. In addition, the improper assignment strategy of the DP could cause a wrong assignment result for the non-center points. This paper aims to address the aforementioned issues and improve the clustering performance of the DP.

First, to identify as many potential cluster centers as possible, the authors construct a point-domain by introducing the pinhole imaging strategy to extend the searching range of the potential cluster centers. Second, they design different novel calculation methods for calculating the domain distance, point-domain density and domain similarity. Third, they adopt domain similarity to achieve the domain merging process and optimize the final clustering results.

The experimental results on analyzing 12 synthetic data sets and 12 real-world data sets show that two-stage density peak clustering based on multi-strategy optimization (TMsDP) outperforms the DP and other state-of-the-art algorithms.

The authors propose a novel DP-based clustering method, i.e. TMsDP, and transform the relationship between points into that between domains to ultimately further optimize the clustering performance of the DP.