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1 – 10 of 237
Article
Publication date: 19 February 2024

Ming-Chang Wang, Yu-Feng Hsu and Hsiang-Ying Chien

This study investigates the media activities of firms issuing private equity placements and seasoned equity offerings in Taiwan, as firms have incentives to manage media coverage…

Abstract

Purpose

This study investigates the media activities of firms issuing private equity placements and seasoned equity offerings in Taiwan, as firms have incentives to manage media coverage to influence their stock prices during private equity placement.

Design/methodology/approach

We collect a corpus of news stories and transform the news into term sets based on the part of speech. Then, we refer to Cecchini et al. (2010) to classify the news terms into positive, negative, and usual categories. Next, we employ the SVM algorithm to perform the classification tasks and the term frequency method to perform the text mining task. In last, we use a multiple regression model to verify the hypotheses.

Findings

We determine that issuing firms in a private placement have substantially more positive news stories and fewer negative news stories than those in public offerings. Furthermore, we evidence that the media management effects of postequity issues are more active than those of preequity issues. Finally, our results demonstrate that the timing and content of financial media coverage among different equity issuance methods may be biased by firm management. According to previous studies, they may attempt to manipulate stock prices to increase the number of highly profitable insider stakeholders.

Originality/value

To our knowledge, this is the first study to investigate that if private placement will associate with more active media management than the public offerings. According to our results of the difference-in-means test, the public offerings market may control news coverage; however, this result is inconsistent with that of the regression results. The private placements market may also exercise media management in the “before announcement day” and “after announcement day” periods by increasing positive news and reducing negative news.

Details

Journal of Accounting Literature, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0737-4607

Keywords

Article
Publication date: 26 September 2023

Wike Pertiwi, Sri Murni Setyawati and Ade Irma Anggraeni

The purpose of this paper is to examine the relationship between toxic workplace environments, negative workplace gossip and knowledge hiding, by exploring workplace spirituality…

Abstract

Purpose

The purpose of this paper is to examine the relationship between toxic workplace environments, negative workplace gossip and knowledge hiding, by exploring workplace spirituality as a moderating variable in this relationship.

Design/methodology/approach

This study focusses on private university lecturer in West Java, Indonesia. Data collection was carried out by distributing questionnaires to respondents offline and online via Google Forms. Data analysis was done by structural equation modeling (SEM).

Findings

The findings reveal that a toxic workplace environment and negative workplace gossip are positively related to knowledge hiding. In addition, it was found that workplace spirituality moderates the relationship between a toxic workplace environment and negative workplace gossip with knowledge hiding.

Research limitations/implications

This study extends the research model and research context of knowledge hiding in private universities. This research contributes to the social exchange theory literature by proving empirical support to confirm that there is a social exchange in interpersonal relations between academics.

Practical implications

This study extends the research model and research context of knowledge hiding in private universities, linking it to the conservation of resources theory. This research contributes to the social exchange theory literature by proving empirical support to confirm that there is a social exchange in interpersonal relations between lecturers.

Social implications

Leaders need to instill spirituality in lecturer so that they feel comfortable when working, and it indirectly reduces the effects of negative behavior such as negative gossip and a toxic environment that makes them willing to share knowledge.

Originality/value

To the authors’ understanding, this is the first study to examine workplace spirituality as a variable moderating the relationship between toxic workplace environment and negative workplace gossip with knowledge hiding in the college context.

Details

Journal of Applied Research in Higher Education, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-7003

Keywords

Article
Publication date: 22 August 2024

Yuexian Zhang and Xueying Wang

Although virtual anchors have emerged as potent marketing tools, their acceptance by consumers is controversial. Specifically, the relative efficacy of selecting an all-human-like…

Abstract

Purpose

Although virtual anchors have emerged as potent marketing tools, their acceptance by consumers is controversial. Specifically, the relative efficacy of selecting an all-human-like or animal-human-like virtual anchor is not well-defined. However, anthropomorphic visual cues are vital in enhancing live streaming. This study aims to analyze the disparate effects of an animal-human-like or all-human-like virtual anchor on purchase intention as well as evaluate the possible underlying influential mechanisms and boundary conditions.

Design/methodology/approach

In this research, three different studies were carried out to elucidate the impact of virtual anchors on purchase intention. Study 1 evaluated the core impact of an animal-human-like and all-human-like virtual anchor on purchase intention, as well as the mediating role of perceived warmth and competence. Studies 2 and 3 were then performed to investigate the moderating impacts of product type and certainty of consumer needs, respectively. Furthermore, research data for these studies was collected using the Credamo tool and analyzed via SPSS, using PROCESS for moderation and mediation analyses.

Findings

The research findings indicate that virtual anchors can trigger purchase intention, with perceived warmth and competence acting as mediating factors. Based on the utilitarian products and high certainty of consumer needs, the influence of perceived competence on purchase intention is augmented. Therefore, an all-human-like virtual anchor increases purchase intention. In contrast, the impact of perceived warmth on purchase intention is supplemented for hedonic products and low certainty of consumer needs. Thus, an animal-human-like virtual anchor increases purchase intention.

Originality/value

This research study evaluated consumer reactions to all-human-like and animal-human-like virtual anchors for different product types and the certainty of consumer needs to optimize the comprehension of a virtual anchor. Furthermore, the assessment of the mediating roles of perceived warmth and competence provided valuable insights into the influential mechanisms by which virtual anchors affect purchase intention. Moreover, this study provided managerial implications to guide retailers and brands on the strategic adoption of virtual anchors to enhance purchase intention based on the product type and the certainty of consumer needs.

Details

Journal of Product & Brand Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1061-0421

Keywords

Article
Publication date: 10 July 2024

Mehmet Bulent Durmusoglu and Canan Aglan

The inherent variability on process times and demand are the factors that prevent the efficient application of lean philosophy in multi-project product development (PD…

51

Abstract

Purpose

The inherent variability on process times and demand are the factors that prevent the efficient application of lean philosophy in multi-project product development (PD) environments. Considering this variability, a hybrid push–pull project control system is developed, and value stream costing (VSC) analysis is performed to reflect the relation between project lead time, capacity and project cost. The assessment of the push/pull project control on lead time improvement and long-term savings on capacity have been aimed with the proposed complete design structure.

Design/methodology/approach

In a team-based structure, formed through clustering, push control techniques for planning tasks within cross-functional teams and pull control techniques for planning tasks between cross-functional teams are developed. The final step evaluates the proposed structure through VSC and long-term savings have been pointed out, especially in terms of freed-up capacity. For the validation of the proposed methodology, an office furniture manufacturing firm’s PD department has been considered and the performance of the hybrid system has been observed through simulation experiments and based on the simulation results, the lean system is evaluated by VSC.

Findings

The results of simulation experiments show a superior performance of the proposed hybrid push/pull project control mechanism under different settings of cycle time between projects or shortly project cycle time, dispatching rules within teams and variability levels. The results of the Box-Score (tool to apply VSC) indicate increased capacity in the long term to add extra projects during the planning period with the same project lead time and without additional cost.

Research limitations/implications

Although extensive simulation experiments have been performed to quantify the effect of project control structure and positive results have been reported on lead time and cost, the proposed design structure has not been tested in all existing PD environments.

Originality/value

To the best of authors’ knowledge, the quantification of the effect of hybrid project control with VSC is the first attempt to be applied in lean PD projects.

Details

International Journal of Lean Six Sigma, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2040-4166

Keywords

Article
Publication date: 25 March 2024

Zhixue Liao, Xinyu Gou, Qiang Wei and Zhibin Xing

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that…

Abstract

Purpose

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

Design/methodology/approach

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

Findings

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

Originality/value

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

Details

Nankai Business Review International, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2040-8749

Keywords

Article
Publication date: 22 March 2024

Sanaz Khalaj Rahimi and Donya Rahmani

The study aims to optimize truck routes by minimizing social and economic costs. It introduces a strategy involving diverse drones and their potential for reusing at DNs based on…

74

Abstract

Purpose

The study aims to optimize truck routes by minimizing social and economic costs. It introduces a strategy involving diverse drones and their potential for reusing at DNs based on flight range. In HTDRP-DC, trucks can select and transport various drones to LDs to reduce deprivation time. This study estimates the nonlinear deprivation cost function using a linear two-piece-wise function, leading to MILP formulations. A heuristic-based Benders Decomposition approach is implemented to address medium and large instances. Valid inequalities and a heuristic method enhance convergence boundaries, ensuring an efficient solution methodology.

Design/methodology/approach

Research has yet to address critical factors in disaster logistics: minimizing the social and economic costs simultaneously and using drones in relief distribution; deprivation as a social cost measures the human suffering from a shortage of relief supplies. The proposed hybrid truck-drone routing problem minimizing deprivation cost (HTDRP-DC) involves distributing relief supplies to dispersed demand nodes with undamaged (LDs) or damaged (DNs) access roads, utilizing multiple trucks and diverse drones. A Benders Decomposition approach is enhanced by accelerating techniques.

Findings

Incorporating deprivation and economic costs results in selecting optimal routes, effectively reducing the time required to assist affected areas. Additionally, employing various drone types and their reuse in damaged nodes reduces deprivation time and associated deprivation costs. The study employs valid inequalities and the heuristic method to solve the master problem, substantially reducing computational time and iterations compared to GAMS and classical Benders Decomposition Algorithm. The proposed heuristic-based Benders Decomposition approach is applied to a disaster in Tehran, demonstrating efficient solutions for the HTDRP-DC regarding computational time and convergence rate.

Originality/value

Current research introduces an HTDRP-DC problem that addresses minimizing deprivation costs considering the vehicle’s arrival time as the deprivation time, offering a unique solution to optimize route selection in relief distribution. Furthermore, integrating heuristic methods and valid inequalities into the Benders Decomposition approach enhances its effectiveness in solving complex routing challenges in disaster scenarios.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 30 July 2024

Wei Zhang, Ning Ding, Rui Xue, Yilong Han and Chenyu Liu

In today’s digital era, talent recruitment can help address the growing shortage of skilled labor in the construction industry and promote sustainable growth. While existing…

Abstract

Purpose

In today’s digital era, talent recruitment can help address the growing shortage of skilled labor in the construction industry and promote sustainable growth. While existing research has explored the association between talent acquisition and local labor productivity or economic progress, the impact on construction growth deserves further study. This study aims to (1) explore the influence of talent recruitment on the growth of the construction industry and (2) analyze whether different regional characteristics shape the differential impact of talent acquisition on construction growth.

Design/methodology/approach

This research employs a quantitative approach, focusing on 35 major cities in China. A panel data regression model is utilized to analyze annual data from 2013 to 2018, considering variables like the construction talent recruitment index, value added in construction, gross regional product per capita and others. The study also examines regional heterogeneity and conducts robustness tests to validate the findings.

Findings

The results reveal a positive and significant correlation between talent recruitment and construction industry growth. This correlation is more pronounced in economically advanced and infrastructure-rich regions. The study also finds that factors like capital investment, educational attainment and housing prices significantly contribute to industry growth. Talent recruitment not only transforms local labor market dynamics but also drives demand for construction services, promoting industry growth through economies of scale.

Originality/value

This research constructs a new measurement for talent recruitment and provides new insights into the pivotal role of talent recruitment in the sustainable growth of the construction industry. It underscores the need for construction firms to tailor talent acquisition policies to their specific circumstances and regional developmental conditions. The findings offer practical guidance for driving regional growth within the sector, emphasizing the importance of talent recruitment as a key yet previously underappreciated factor in industry development.

Details

Engineering, Construction and Architectural Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0969-9988

Keywords

Open Access
Article
Publication date: 25 April 2024

Adrián Mendieta-Aragón, Julio Navío-Marco and Teresa Garín-Muñoz

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

研究目的

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?

研究設計/方法/理念

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

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。

研究的原創性

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。

Details

European Journal of Management and Business Economics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2444-8451

Keywords

Open Access
Article
Publication date: 28 February 2024

Jennie Åkesson, Angelina Sundström, Glenn Johansson, Koteshwar Chirumalla, Sten Grahn and Anders Berglund

Despite increasing focus among scholars and practitioners on the design of product-service systems (PSS), there exists no compilation of current knowledge on the role played by…

1436

Abstract

Purpose

Despite increasing focus among scholars and practitioners on the design of product-service systems (PSS), there exists no compilation of current knowledge on the role played by small and medium-sized enterprises (SMEs) in designing such systems. Thus, this paper sets out to identify and organise the existing research and suggest questions for future research.

Design/methodology/approach

A systematic literature review was performed to identify and provide in-depth details on key themes in the literature addressing the design of PSS in SMEs.

Findings

This paper identifies five themes in the literature on the design of PSS in SMEs: motives, challenges, SME characteristics, methods and digitalisation. The themes are interrelated, and SME characteristics seem to be at the core as they are related to all the other themes. Gaps in the current knowledge are identified, and questions for future research are suggested.

Originality/value

The suggestions for future research provide a starting point for expanding the research on PSS design and devising practical support for SMEs.

Details

Journal of Manufacturing Technology Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-038X

Keywords

Open Access
Article
Publication date: 13 January 2022

Dinda Thalia Andariesta and Meditya Wasesa

This research presents machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using multisource Internet data.

5536

Abstract

Purpose

This research presents machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using multisource Internet data.

Design/methodology/approach

To develop the prediction models, this research utilizes multisource Internet data from TripAdvisor travel forum and Google Trends. Temporal factors, posts and comments, search queries index and previous tourist arrivals records are set as predictors. Four sets of predictors and three distinct data compositions were utilized for training the machine learning models, namely artificial neural networks (ANNs), support vector regression (SVR) and random forest (RF). To evaluate the models, this research uses three accuracy metrics, namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).

Findings

Prediction models trained using multisource Internet data predictors have better accuracy than those trained using single-source Internet data or other predictors. In addition, using more training sets that cover the phenomenon of interest, such as COVID-19, will enhance the prediction model's learning process and accuracy. The experiments show that the RF models have better prediction accuracy than the ANN and SVR models.

Originality/value

First, this study pioneers the practice of a multisource Internet data approach in predicting tourist arrivals amid the unprecedented COVID-19 pandemic. Second, the use of multisource Internet data to improve prediction performance is validated with real empirical data. Finally, this is one of the few papers to provide perspectives on the current dynamics of Indonesia's tourism demand.

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