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The purpose of this systematic review is to critically analyze pharmaceutical industry case studies on the implementation of Lean 4.0 methodologies to synthesize key lessons, benefits and best practices. The goal is to inform decisions and guide investments in related technologies for enhancing quality, compliance, efficiency and responsiveness across production and supply chain processes.

The article utilized a systematic literature review (SLR) methodology following five phases: formulating research questions, locating relevant articles, selecting and evaluating articles, analyzing and synthesizing findings and reporting results. The SLR aimed to critically analyze pharmaceutical industry case studies on Lean 4.0 implementation to synthesize key lessons, benefits and best practices.

Key findings reveal recurrent efficiency gains, obstacles around legacy system integration and data governance as well as necessary operator training investments alongside technological upgrades. On average, quality assurance reliability improved by over 50%, while inventory waste declined by 57% based on quantified metrics across documented initiatives synthesizing robotics, sensors and analytics.

As a comprehensive literature review, findings depend on available documented implementations within the search period rather than direct case evaluations. Reporting bias may also skew toward more successful accounts.

Synthesized implementation patterns, performance outcomes and concealed pitfalls provide pharmaceutical leaders with an evidence-based reference guide aiding adoption strategy development, resource planning and workforce transitioning crucial for Lean 4.0 assimilation.

This systematic assessment of pharmaceutical Lean 4.0 adoption offers an unprecedented perspective into the real-world issues, dependencies and modifications necessary for successful integration, absent from conceptual projections or isolated case studies alone until now.

Online food delivery applications (OFDAs) provide an expedient platform, and consumers’ access to food has been drastically altered, especially during and after the COVID-19 pandemic. This study aimed to completely explore the attributes that influence consumers' purchase intention and how an app's aesthetics can evoke feelings that predict continuous usage intentions for OFDAs. The food industry, especially restaurants, heavily relies on mobile technology to facilitate critical online food delivery during the pandemic crisis.

The data for this study are gathered from 477 food consumers located in the federal capital territory (FCT) of Islamabad, Pakistan, through convenient sampling by developing a self-administrated online survey. SmartPLS is used for structural equation modeling to test the proposed research model and perform bootstrapping and algorithmic analysis.

Our findings revealed that perceived value positively predicted consumers’ purchase intentions. Moreover, perceived value mediates the association of information quality, familiarity, time-saving, usability and reputation with purchase intentions and fear of COVID-19 moderates the relationship between perceived value and purchase intention.

This research work has significant implications for researchers, web developers, app designers, delivery services, restaurants and other enterprises as it demonstrates the importance of aesthetically pleasing OFDAs in eliciting positive emotions and bolstering consumers’ intentions to continue using the app for efficient food delivery services.

This study expanded the application of the technology acceptance model (TAM) and attention, interest, desire and action (AIDA) by examining consumers’ purchase intentions in the context of OFDAs. Further, the successful utilization of TAM enhanced the understanding of consumer perceptions and behavioral intentions about the usage of OFDAs.

Augmented reality (AR) adoption has boomed globally in recent years. The prospective of AR to seamlessly integrate digital information into the actual environment has proven to be a challenge for academics and industry, as they endeavor to understand and predict the influence on users' perceptions, adoption intentions and usage. This study investigates the factors affecting consumers’ behavioral intention to adopt AR technology in shopping malls by offering the mobile technology acceptance model (MTAM).

This conceptual framework is based on mobile self-efficacy, rewards, social influence and enjoyment of existing MTAM constructs. A self-administered questionnaire, constructed by measuring questions modified from previous research, elicited 311 usable responses from mobile respondents who had recently used AR technology in shopping malls. This analysis was performed using SmartPLS3.0.

Grounded on the findings of the study, it was found that, aside from factors such as mobile usefulness, ease of use and social influence, the remaining independent variables had the most significant impact on adopting AR technologies. Considering the limitations of this study, the paper concludes by discussing the significant implications and insinuating avenues for future research.

To better investigate mobile AR app adoption in Pakistan’s shopping malls, the researchers modified the newly proposed MTAM model by incorporating mobile self-efficacy theory, social influence, rewards and perceived enjoyment. However, the extended model has not been extensively studied in previous research. This study is the first to examine the variables that affect an individual’s intention to accept mobile AR apps by using a novel extended MTAM.

Radical changes in consumer habits induced by the coronavirus disease (COVID-19) pandemic suggest that the usual demand forecasting techniques based on historical series are questionable. This is particularly true for hospitality demand, which has been dramatically affected by the pandemic. Accordingly, we investigate the suitability of tourists’ activity on Twitter as a predictor of hospitality demand in the Way of Saint James – an important pilgrimage tourism destination.

This study compares the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) time-series model with that of the SARIMA with an exogenous variables (SARIMAX) model to forecast hotel tourism demand. For this, 110,456 tweets posted on Twitter between January 2018 and September 2022 are used as exogenous variables.

The results confirm that the predictions of traditional time-series models for tourist demand can be significantly improved by including tourist activity on Twitter. Twitter data could be an effective tool for improving the forecasting accuracy of tourism demand in real-time, which has relevant implications for tourism management. This study also provides a better understanding of tourists’ digital footprints in pilgrimage tourism.

This study contributes to the scarce literature on the digitalisation of pilgrimage tourism and forecasting hotel demand using a new methodological framework based on Twitter user-generated content. This can enable hospitality industry practitioners to convert social media data into relevant information for hospitality management.

2019冠狀病毒病引致消費者習慣有根本的改變; 這些改變顯示，根據歷史序列而運作的慣常需求預測技巧未必是正確的。這不確性尤以受到大流行極大影響的酒店服務需求為甚。因此，我們擬探討、若把在推特網站上的旅遊活動視為聖雅各之路 (一個重要的朝聖旅遊聖地) 酒店服務需求的預測器，這會否是合適的呢?

本研究比較 SARIMA 時間序列模型與附有外生變數 (SARIMAX)模型兩者在預測旅遊及酒店服務需求方面的表現。為此，研究人員收集在推特網站上發佈的資訊，作為外生變數進行研究。這個樣本涵蓋於2018年1月至2022年9月期間110,456個發佈資訊。

研究結果確認了傳統的時間序列模型，若涵蓋推特網站上的旅遊活動，則其對旅遊需求方面的預測會得到顯著的改善。推特網站的數據，就改善預測實時旅遊需求的準確度，或許可成為有效的工具; 而這發現對旅遊管理會有一定的意義。本研究亦讓我們進一步瞭解朝聖旅遊方面旅客的數碼足跡。

現存文獻甚少探討朝聖旅遊的數字化，而本研究不但在這方面充實了有關的文獻，還使用了一個根據推特網站上使用者原創內容嶄新的方法框架，進行分析和探討。這會幫助酒店從業人員把社交媒體數據轉變為可供酒店管理之用的合宜資訊。

The ancient town of Lijiang is a representative place of ethnic minorities in China’s southwest border area jointly built by many ethnic groups. Its rich and diversified history, culture and architecture as well as its artistic and spiritual values need to be better retained and explored.

The protection and inheritance of Lijiang’s cultural heritage will be improved through the construction of digital memory resources. To guide Lijiang’s digital memory construction, this study explores strategies of digital memory construction by analyzing four case studies of well-known memory projects from China and America.

From the case studies analysis, factors of digital memory construction were identified and compared. Factors led to the discussion of strategies for constructing the digital memory of Lijiang within its design, construction and service phases.

The ancient town of Lijiang is a famous historical and cultural city in China, and it is also a representative place of ethnic minorities in the border area jointly built by many ethnic groups. The rich culture should be preserved and digitalized to offer better use for the whole nation.

The present article aims to examine the development of the theoretical framework surrounding collaborative consumption (CC) standards in recent years regarding European short-stay accommodation booking platforms. The sharing economy has significantly impacted the tourist accommodation market in recent years. Starting with the use of experimental data on CCs published on Eurostat in 2019, this article analyzes the correlation between choices of CCs for short-stay accommodation, employment and the economic crisis.

A vector autoregressive panel approach was applied to investigate the correlation between CC short-stay accommodation choices using panel organization data from 561 EU regions between 2018 and 2021.

Analyzing the connection between the main data panel variables, a positive correlation was found, followed by an increasing trend in CC use. A self-multiplying effect is generated; that is, the more people use CC, the more electronic captures occur. Consequently, the improvement of CC use and knowledge-intensive activities in short-stay accommodation is strongly linked with employment and GDP.

The originality of the investigation is to examine with a cross-sectional panel data overview the reasons that can push stakeholders to adopt CC and to clearly define a new perimeter of research in terms of the endpoint of CC in short-stay accommodation. Furthermore, the study seeks to assess the end-point congruence to utilize CC as a new gamble to accelerate digital knowledge in the hospitality sector.

This study aims to analyse the differences between professional interaction mediated by video conferencing and direct professional interaction. The research identifies diverging interests of office workers for the purpose of addressing work ethical and business ethical issues of professional collaboration, competition, and power in future hybrid work models.

Based on 28 qualitative interviews conducted between November 2020 and June 2021, and through the theoretical lens of phenomenology, the study develops explanatory hypotheses conceptualising four basic intentions of professional interaction and their corresponding preferences for video conferences and working on site.

The four intentions developed on the basis of the interviews are: the need for physical proximity; the challenge of collective creativity; the will to influence; and control of communication. This conceptual framework qualifies a moral ambivalence of professional interaction. The authors identify a connectivity paradox of professional interaction where the personal dimension remains unarticulated for the purpose of maintaining professionality. This tacit human connectivity is intertwined with latent power relations. This plasticity of both connectivity and power in direct interaction can be diminished by transferring the interaction to video conferencing.

The application of phenomenology to a collection of qualitative interviews has enabled the identification of underlying intention structures and the system in which they affect each other. This research identifies conflicts of interests between workers relative to their different self-perceived abilities to persevere in competitive professional interaction. It is therefore able to address consequences of future hybrid work models at an existential and societal level.

To enrich the research on the economic consequences of enterprise digital development from the perspective of capacity utilization.

Using a sample of listed firms from 2010 to 2020, this paper exploits text analysis of annual reports to construct a proxy for enterprise digital development.

Results show that enterprise digital development not only improves their own capacity utilization but also generates a positive spillover effect on the capacity utilization of peer firms and firms in the supply chain. Next, based on the incomplete information about market demand and potential competitors when making capacity-building decisions, the mechanism tests show that improving the accuracy of market forecasts and reducing investment surges are potential channels behind the baseline results. Cross-sectional tests show the baseline result is more pronounced when industries are highly homogeneous and when firms have access to less information.

This paper contributes to the research related to the economic consequences of digital development. With the development of the digital economy, the real effects of enterprise digital development have also triggered extensive interest and exploration. Existing studies mainly examine the impact on physical operations, such as specialization division of labor, innovation activities, business performance or total factor productivity (Huang, Yu, & Zhang, 2019; Yuan, Xiao, Geng, & Sheng, 2021; Wang, Kuang, & Shao, 2017; Li, Liu, & Shao, 2021; Zhao, Wang, & Li, 2021). These studies measure the economic benefits from the perspective of the supply (output) side but neglect the importance of the supply system to adapt to the actual market demand. In contrast, this paper focuses on capacity utilization, aimed at estimating the net economic effect of digital development by considering the supply-demand fit scenario. Thus, our findings enrich the relevant studies on the potential consequences of digital development.

We examined skill building techniques and changes over the course of a semester with pre and post-test data collection after implementing experiential learning assignments.

The Schutte Self-Report Emotional Intelligence Test (SEEIT) was used to measure emotional intelligence among students who interned for a 16-week period.

We found a significant difference using a paired samples t-test in SSEIT scores between the pre (M = 126.6, SD = 4.3) and the post-internship (M = 133.8, SD = 5.7) scores. t(5) = -5.61, p = 0.002. Students had an overall increase in mean scores over the course of one semester.

This was a pilot study that we completed to determine applicability of internship and increasing emotional intelligence. Overall, we saw an increase in EI in pre and post-test comparisons. This was a pilot study, so more research is needed on this topic.

Students were placed in situations during the internship process to help facilitate real world problems and were required to apply applicable textbook knowledge, develop theory-based activities and report their findings. Students worked with various age groups and learned how to work with a variety of populations including faculty, teachers, children and parents on a regular basis and this process contributed to their experience and potentially increased emotional intelligence over a 16-week period.

This research addresses the importance of emotional intelligence (EI) in career readiness and its role in potentially mitigating burnout in psychological professions.

This is important to those in the field of psychology and child development and family studies because it addresses concerns with the shortage of skilled and prepared workers.