Search results

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

Crowdfunding is an increasingly popular channel for project fundraising for entrepreneurial ventures. Such efforts require fundraisers to develop and manage a crowdfunding campaign over a period of time and several stages. Thus, the authors aim to identify the stages fundraisers go through in their crowdfunding campaign process and how their engagement evolves throughout this process.

Following a multiple case study research design analysing six successful campaigns, the current study suggests a taxonomy of stages the fundraisers go through in their crowdfunding campaign management process while identifying the types of engagement displayed and their relative intensity at each of these stages.

The study proposes a five-stage process framework (pre-launch, launch, mid-campaign, conclusion and post-campaign), accompanied by a series of propositions outlining the relative intensity of different types of engagement throughout this process. The authors show that engagement levels appear with high intensity at pre-launch, and to a lesser degree also at the post-launch stage while showing low intensity at the stages in between them. More specifically, cognitive and behavioural engagement are most prominent at the pre- and post-launch stages. Emotional engagement is highest during the launch, mid-launch and conclusion stages. And social engagement maintains moderate levels of intensity throughout the process.

This study focuses on the campaign process using engagement theory, thus identifying the differing engagement patterns throughout the dynamic crowdfunding campaign management process, not just in one part.

This study aims to critically review the research on food eco-labels to identify research gaps and recommend future research directions in business and management.

A hybrid integrated review combines bibliometric analysis with an in-depth framework-based study of theory–context–characteristics–methodology (TCCM) and reviews quality research published over 21 years between 2002 and 2022.

We identify key research themes and the knowledge structure of food eco-label research. Future researchers should explore food eco-label dynamics and phenomena in areas related to consumer behaviour, information, consumer knowledge, environmental concerns, trust, packaging and willingness-to-pay as antecedent factors.

This review contributes to the business and management literature by quantifying existing research and consolidating its evolution. The articles were sourced from the established Web of Science (WoS) database. Other databases should be included in future reviews. We also focus on research published in business and management. Further studies could include research beyond such specialised domains.

Consumers need more information on the objectives and meaning of food eco-labels. The dissemination of knowledge on eco-labels, as well as effective communication and information on eco-labels, are relevant to future research issues.

The knowledge derived from this research holds significant potential in shaping policies and devising tools aimed at reducing the carbon footprint linked to food production and consumption. Considering the substantial impact of these activities on our planet’s carbon footprint, addressing food security and sustainability emerges as a crucial concern for humanity. Recognising the importance of eco-label communication and information becomes particularly pertinent for future generations, who stand to bear the most substantial impact of climate change and sustainable development. These generations are also more inclined toward embracing and implementing sustainable practices.

There are no comprehensive, integrated reviews exploring the methods, variables and constructs used in studies on food eco-labels based on all articles published in WoS journals in the business and management domains. This is the first comprehensive literature review using a hybrid approach (bibliometric review with TCCM framework) in the field of food eco-label research.

本研究擬以批判性的態度,審閱過去有關食物生態標籤的研究,以能確認研究缺口,並於商業和管理的範疇裡,建議今後的研究方向。

這是一個混合綜合審閱研究。研究結合了文獻計量分析方法和理論 -背景 - 特徵 - 方法論 (TCCM) 框架; 研究人員審閱於2002年與2022年間二十一年裡出版的高水平學術研究。

我們確認了關於食物生態標籤的研究裡主要的研究主題和知識結構。研究人員和學者今後應致力關於消費行為、資訊、顧客知識、環境關注、信任、包裝和支付意願等議題的研究範疇,以能在這些範疇內,把食物生態標籤的變革動力和現象,作為是先前因素進行探索。

本研究確定了現存有關的研究的數量,並幫助鞏固了這類研究的發展,就此,對商業和管理文獻作出了貢獻。審閱的學術論文均來自被認可的 Web of Science 資料庫。今後的研究應涵蓋其它資料庫; 而且,我們的焦點是放在關於商業和管裡的文獻上; 研究人員和學者今後或許也應致力於其它研究範疇。

消費者需要更多關於食物生態標籤目標和意義的資訊; 因此,今後的研究須關注生態標籤知識的傳播,以及生態標籤的有效溝通和資訊。

根據於 Web of Science 出版、以及在商業和管理領域內的學術論文,似乎沒有全面的或綜合的審閱,去探索有關食物生態標籤研究內採用的研究方法、變數和構築。本研究為在食物生態標籤研究領域裡,首個使用混合方式 (結合文獻計量分析方法和理論 -背景 - 特徵 - 方法論 (TCCM) 框架)的全面文獻審閱。

Engaging in webcare, i.e. responding to online reviews, can positively affect consumer attitudes, intentions and behavior. Research is often scarce or inconsistent regarding the effects of specific webcare strategies on business performance. Therefore, this study tests whether and how several webcare strategies affect hotel bookings.

We apply machine learning classifiers to secondary data (webcare messages) to classify webcare variables to be included in a regression analysis looking at the effect of these strategies on hotel bookings while controlling for possible confounds such as seasonality and hotel-specific effects.

The strategies that have a positive effect on bookings are directing reviewers to a private channel, being defensive, offering compensation and having managers sign the response. Webcare strategies to be avoided are apologies, merely asking for more information, inviting customers for another visit and adding informal non-verbal cues. Strategies that do not appear to affect future bookings are expressing gratitude, personalizing and having staff members (rather than managers) sign webcare.

These findings help managers optimize their webcare strategy for better business results and develop automated webcare.

We look into several commonly used and studied webcare strategies that affect actual business outcomes, being that most previous research studies are experimental or look into a very limited set of strategies.

This study explores the variance in investor responses to the corporate social responsibility (CSR) performance of firms, as influenced by information sources and investor types.

This study applies a short-term event study and cross-sectional analysis with unique CSR datasets obtained from newspaper articles and the Dow Jones Sustainability Index.

Investor reactions are significantly shaped by their sources of information. Individual investors are found to predominantly respond to accessible news announcements, whereas institutional investors show heightened sensitivity to adverse news from both scrutinized sources. Foreign investors, mirroring institutional investors' patterns, uniquely react positively to index additions.

Investors’ assessment of CSR activities varies due to the differing sources of information obtained; further, it is affected by the type of investor.

The findings guide public relation managers in strategizing CSR communication toward diverse investor types. This includes recommending targeted approaches for Japanese individual investors through newspapers and TV, exercising caution in disseminating adverse news to Japanese institutions, and promoting and justifying CSR actions to foreign investors. It underscores the need for a strategic investor relations frameworks that considers accessibility, literacy, and investors' interests.

This study examines the relationship between sources of information for CSR activities and investors’ responses, an area under-represented in the literature. The author uses CSR announcement data, collected from newspapers to make the results more accurate and relevant.

This study investigates the relationship between board gender diversity (BGD) and the time to Initial Public Offering (IPO), which stands as an entrepreneurially risky choice, particularly challenging in family firms. We also investigate the moderating role of family ownership dispersion (FOD).

We draw on an integrated theoretical framework bringing together the upper echelons theory and the socio-emotional wealth (SEW) perspective and on hand-collected data on a sample of Italian family IPOs that occurred in the period 2000–2020. We employ ordinary least squares (OLS) regression and alternative model estimations to test our hypotheses.

BGD positively affects the time to IPO, thus, it increases the time required to go public. FOD negatively moderates this relationship. Our findings remain robust with different measures for BGD, FOD, and family business definition as well as with different econometric models.

The article develops literature on family firms and IPO and it enriches the academic debate about gender and IPOs in family firms. It adds to studies addressing the determinants of the time to IPO by incorporating gender diversity and the FOD into the discussion. Finally, it contributes to research on women and outcomes in family firms.

Food waste is a severe problem affecting the supply chain due to its significant adverse social and environmental effects. Even if the topic is hotly debated in the literature, there is a lack of research about the success factors influencing food waste prevention initiatives retailers undertake.

The research analyzes how several variables (i.e. product-related variables and technology-enabling variables) might impact the success of the sales of products close to the expiration date that is sold at a discounted price. Data from 390.000 products sold at a discounted price in 2020 and 2021 by a large Italian food retailer were examined with a regression analysis.

The results highlight that both product-related and technology-enabling variables influence the success of food prevention initiatives aimed at selling products close to the expiration date at a discounted price. In particular, the authors stress the importance of digital technologies in supporting food waste prevention initiatives.

The study offers several practical implications for managers in structuring a waste prevention initiative. The introduction of digital technologies, the monitoring of specific variables or the ability to find synergies with other food waste prevention initiatives are discussed to support retailers in reducing food losses.

The paper is focused on the retailer perspective, which is barely investigated due to the difficulty in finding data.

This paper aims to provide an overview of the application of blockchain to agri-food supply chains, including key issues and trends. It examines the state of the art and conceptual structure of the field and proposes an agenda to guide future research.

This article performs a bibliometric analysis using VOSviewer software on a sample of 205 articles from the WoS database to identify research trend topics.

The number of publications in this area has increased since 2020, which shows a growing research interest. The research hotspots are related to the integration of blockchain technology in the agri-food supply chain for traceability, coordination between all actors involved, transparency of operations and improvement of food safety. Furthermore, this is linked to sustainability and the achievement of the sustainable development gtoals (SDGs), while addressing key challenges in the implementation of blockchain-based technologies in the agri-food supply chain.

The application of blockchain in the agri-food supply chain may consider four key aspects. Firstly, the implementation of blockchain can improve the traceability of food products. Secondly, this technology supports sustainability issues and could avoid disruptions in the agri-food supply chain. Third, blockchain improves food quality and safety control throughout the supply chain. Fourthly, the findings show that regulation is needed to improve trust between stakeholders.

The paper provides a comprehensive overview of the blockchain phenomenon in the agri-food supply chain by optimising the search criteria. Moreover, it serves to bridge to future research by identifying gaps in the field.

All entrepreneurs interact simultaneously with multiple entrepreneurial contexts throughout their entrepreneurial journey. This conceptual paper has two central aims: (1) it synthesises the current literature on gender and entrepreneurship, and (2) it increases our understanding of how gender norms, contextual embeddedness and (in)equality mechanisms interact within contexts. Illustrative contexts that are discussed include entrepreneurship education, business networks and finance.

This conceptual paper draws upon extant literature to develop its proposed conceptual framework. It provides suggestions for systemic policy interventions as well as pointing to promising paths for future research.

A literature-generated conceptual framework is developed to explain and address the systemic barriers faced by opportunity-driven women as they engage in entrepreneurial contexts. This conceptual framework visualises the interplay between gender norms, contextual embeddedness and inequality mechanisms to explain systemic disparities. An extra dimension is integrated in the framework to account for the power of agency within women and with others, whereby agency, either individually or collectively, may disrupt and subvert the current interplay with inequality mechanisms.

This work advances understanding of the underrepresentation of women entrepreneurs. The paper offers a conceptual framework that provides policymakers with a useful tool to understand how to intervene and increase contextual embeddedness for all entrepreneurs. Additionally, this paper suggests moving beyond “fixing” women entrepreneurs and points towards disrupting systemic disparities to accomplish this contextual embeddedness for all entrepreneurs. By doing so, this research adds to academic knowledge on the construction and reconstruction of gender in the field of entrepreneurship.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.