Search results

1 – 9 of 9
Per page
102050
Citations:
Loading...
Access Restricted. View access options
Article
Publication date: 16 February 2022

Siyu Gong, Li Wang, Peter Peverelli and Danni Suo

Products that espouse environmental ethical principles have received increasing attention in recent years. However, one key barrier against sustainable consumption is that green…

1392

Abstract

Purpose

Products that espouse environmental ethical principles have received increasing attention in recent years. However, one key barrier against sustainable consumption is that green attributes could result in consumer’s expectation of decreased product physical performance. This study aims to investigate how green attributes existing in different product categories affect consumer purchase intention.

Design/methodology/approach

Two experimental studies were conducted to test the hypotheses. Study 1 provides initial evidence of the interaction effects between green attributes and product category on consumer purchase intention. Study 2 replicates the findings of Study 1 and further tests a benefits-based mechanism in the relationship between green attributes and consumer purchase intention.

Findings

The findings show that in the utilitarian product category, products with green peripheral attributes result in a higher purchase intention than those with green core attributes, whereas, in the hedonic product category, products with green core attributes result in a higher purchase intention than those with green peripheral attributes. Furthermore, the authors demonstrate that green attributes, as universal sustainability cues predominantly affect consumers’ perceptions of utilitarian environmental benefits and self-expression benefits, which further enhance their purchase intention towards utilitarian products and hedonic products, respectively.

Originality/value

This study responds to the calls for more empirical studies into discussing the role of green attributes in consumer purchase intention. Furthermore, it uncovers a benefits-based mechanism that explains how green attributes existing in utilitarian product categories and hedonic product categories trigger consumers’ analysis of benefits, leading to positive consumer purchase intention.

Details

Journal of Product & Brand Management, vol. 31 no. 6
Type: Research Article
ISSN: 1061-0421

Keywords

Access Restricted. View access options
Article
Publication date: 24 September 2020

Siyu Gong, Guanghua Sheng, Peter Peverelli and Jialin Dai

This study aims to develop a comprehensive conceptual framework to investigate how green brand positioning strategies positively impact consumer response. It focusses on…

3291

Abstract

Purpose

This study aims to develop a comprehensive conceptual framework to investigate how green brand positioning strategies positively impact consumer response. It focusses on uncovering the causal mechanism in which such effect is mediated by brand stereotypes. Additionally, it outlines the moderating role of construal level in this formation process.

Design/methodology/approach

Three experimental studies were conducted to examine the hypotheses. Study 1 tests the positive influence of green brand positioning on consumer response. Study 2 tests the dual mediating effect of warmth and competence in the relationship between green brand positioning and consumer response. Study 3 further examines the moderating role of construal level in the effects of green brand positioning on brand stereotypes.

Findings

The findings reveal that green emotional positioning strategies are predominantly stereotyped as warm while green functional positioning strategies are predominantly stereotyped as competent. Both warm and competent mediate the effects of green brand positioning on consumer response. Furthermore, a congruency between green emotional positioning and high-level construal, as well as the match between green functional positioning and low-level construal, leads to more warmth and competence perception.

Originality/value

This study contributes to green brand management literature by proposing a brand stereotype-based mechanism to explain how green brand positioning strategies trigger consumers’ stereotyping process, leading to positive consumer response. This study also identifies the construal level as a moderating variable that impacts consumers’ warmth and competence perceptions towards two kinds of green brand positioning strategies. Managerially, the findings of this study provide managerial ideas for developing green branding strategies.

Details

Journal of Product & Brand Management, vol. 30 no. 7
Type: Research Article
ISSN: 1061-0421

Keywords

Access Restricted. View access options
Article
Publication date: 16 December 2024

Siyu Gong, Danni Suo and Jiatong Dai

This study aims to investigate the effectiveness of exclusive promotions in the form of targeted m-coupons and to uncover the potential psychological mechanisms underpinning…

24

Abstract

Purpose

This study aims to investigate the effectiveness of exclusive promotions in the form of targeted m-coupons and to uncover the potential psychological mechanisms underpinning consumer redemption behaviour.

Design/methodology/approach

Three experimental studies were conducted to elucidate the mechanisms of psychological ownership as well as perceived intrusiveness and to examine the moderating effect of the timing of delivery in these relationships.

Findings

The findings suggest that consumers demonstrate a higher intention to redeem targeted m-coupons than for untargeted m-coupons. Psychological ownership and perceived intrusiveness act as dual mediators in this relationship. However, delivering m-coupons on special dates helps mitigate the discrepancies in consumer responses between targeted and untargeted m-coupons.

Originality/value

This research contributes to existing literature on targeted promotion by comparing the effectiveness of two types of m-coupons and elucidating the dual mechanisms of psychological ownership and perceived intrusiveness. Furthermore, this study identifies a boundary condition that modifies the positive effects of targeted m-coupons.

Details

International Journal of Retail & Distribution Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0959-0552

Keywords

Access Restricted. View access options
Article
Publication date: 2 October 2017

Guangyou Liu and Siyu Liu

This paper aims to answer the following two research questions: Do corruption cases present different features before and since the new administration in China? How are criminal…

1091

Abstract

Purpose

This paper aims to answer the following two research questions: Do corruption cases present different features before and since the new administration in China? How are criminal penalties affected by these corruption features?

Design/methodology/approach

The investigation is based on the online disclosure of 269 state corruption audits and their consequences, which have been made public by China’s National Audit Office since 2011. By manual coding, these official reports were analyzed, and an appropriate-sized sample of corruption cases was chosen. The authors then adopted Welch’s t-test and regression model methods to test the research hypotheses relevant to the two research questions.

Findings

The authors find that larger embezzlement or bribery amounts and more organizational corruption cases have been detected and punished since the anti-corruption campaign was launched by the new administration. They also conclude that significantly tougher criminal penalties were given to corruption cases involving large monetary amounts, that bribery cases were more harshly punished compared to other occupational crimes and that individual perpetrators received tougher criminal penalties than organizational criminals. In addition, the authors observe a trend that criminal penalties for corruption have been increasingly harsher in recent years.

Research limitations/implications

The limitations of this study are quite clear as the Chinese corruption cases in this sample only include state corruption audit cases and does not refer to high-profile corruption cases investigated by the Central Commission of Discipline Inspection. However, this study suggests that state corruption audit results are a good research sample, which can be used to extend empirical tests to archival data acquired from state audit practices and can encourage more studies on public sector auditing and occupational financial crime.

Practical implications

State corruption audits can be an effective approach to successful anti-corruption campaigns, and the conclusions can be useful to policy makers and legislators in China and other developing countries.

Originality/value

This paper bridges some gaps in the existing financial crime literature. First, this study on corruption features is located within the context of a political administrative change; second, the state audit is highlighted as a supervising agency in the anti-corruption campaign; and third, the authors’ contribution adds to the empirical testing of data sets of state corruption audits within the existing financial crime literature.

Details

Journal of Financial Crime, vol. 24 no. 4
Type: Research Article
ISSN: 1359-0790

Keywords

Access Restricted. View access options
Article
Publication date: 10 January 2023

Jianhua Zhu, Luxin Wan, Huijuan Zhao, Longzhen Yu and Siyu Xiao

The purpose of this paper is to provide scientific guidance for the integration of industrialization and information (TIOII). In recent years, TIOII has promoted the development…

571

Abstract

Purpose

The purpose of this paper is to provide scientific guidance for the integration of industrialization and information (TIOII). In recent years, TIOII has promoted the development of intelligent manufacturing in China. However, many enterprises blindly invest in TIOII, which affects their normal production and operation.

Design/methodology/approach

This study establishes an efficiency evaluation model for TIOII. In this paper, entropy analytic hierarchy process (AHP) constraint cone and cross-efficiency are added based on traditional data envelopment analysis (DEA) model, and entropy AHP–cross-efficiency DEA model is proposed. Then, statistical analysis is carried out on the integration efficiency of enterprises in Guangzhou using cross-sectional data, and the traditional DEA model and entropy AHP–cross-efficiency DEA model are used to analyze the integration efficiency of enterprises.

Findings

The data show that the efficiency of enterprise integration is at a medium level in Guangzhou. The efficiency of enterprise integration has no significant relationship with enterprise size and production type but has a low negative correlation with the development level of enterprise integration. In addition, the improved DEA model can better reflect the real integration efficiency of enterprises and obtain complete ranking results.

Originality/value

By adding the entropy AHP constraint cone and cross-efficiency, the traditional DEA model is improved. The improved DEA model can better reflect the real efficiency of TIOII and obtain complete ranking results.

Details

Chinese Management Studies, vol. 18 no. 1
Type: Research Article
ISSN: 1750-614X

Keywords

Access Restricted. View access options
Article
Publication date: 9 August 2023

Siyu Su, Youchao Sun, Chong Peng and Yuanyuan Guo

The purpose of this paper is to identify the key influencing factors of aviation accidents and to predict the aviation accidents caused by the factors.

181

Abstract

Purpose

The purpose of this paper is to identify the key influencing factors of aviation accidents and to predict the aviation accidents caused by the factors.

Design/methodology/approach

This paper proposes an improved gray correlation analysis (IGCA) theory to make the relational analysis of aviation accidents and influencing factors and find out the critical causes of aviation accidents. The optimal varying weight combination model (OVW-CM) is constructed based on gradient boosted regression tree (GBRT), extreme gradient boosting (XGBoost) and support vector regression (SVR) to predict aviation accidents due to critical factors.

Findings

The global aviation accident data from 1919 to 2020 is selected as the experimental data. The airplane, takeoff/landing and unexpected results are the leading causes of the aviation accidents based on IGCA. Then GBRT, XGBoost, SVR, equal-weight combination model (EQ-CM), variance-covariance combination model (VCW-CM) and OVW-CM are used to predict aviation accidents caused by airplane, takeoff/landing and unexpected results, respectively. The experimental results show that OVW-CM has a better prediction effect, and the prediction accuracy and stability are higher than other models.

Originality/value

Unlike the traditional gray correlation analysis (GCA), IGCA weights the sample by distance analysis to more objectively reflect the degree of influence of different factors on aviation accidents. OVW-CM is built by minimizing the combined prediction error at sample points and assigns different weights to different individual models at different moments, which can make full use of the advantages of each model and has higher prediction accuracy. And the model parameters of GBRT, XGBoost and SVR are optimized by the particle swarm algorithm. The study can guide the analysis and prediction of aviation accidents and provide a scientific basis for aviation safety management.

Details

Engineering Computations, vol. 40 no. 7/8
Type: Research Article
ISSN: 0264-4401

Keywords

Access Restricted. View access options
Article
Publication date: 20 August 2024

Siyu Zhang, Ze Lin and Wii-Joo Yhang

This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN)…

124

Abstract

Purpose

This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques and diverse data sets, the research seeks to enhance the accuracy and reliability of tourism demand predictions, contributing significantly to both theoretical implications and practical applications in the field of hospitality and tourism.

Design/methodology/approach

This study introduces an innovative approach to forecasting international tourist arrivals by leveraging LSTM networks. This advanced methodology addresses complex managerial issues in tourism management by providing more accurate forecasts. The methodology comprises four key steps: collecting data sets; preprocessing the data; training the LSTM network; and forecasting future international tourist arrivals. The rest of this study is structured as follows: the subsequent sections detail the proposed LSTM model, present the empirical results and discuss the findings, conclusions and the theoretical and practical implications of the study in the field of hospitality and tourism.

Findings

This research pioneers the simultaneous use of big data encompassing five factors – international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases – for daily forecasting. The study reveals that integrating exchange rates, oil prices, stock market data and COVID-19 cases significantly enhances LSTM network forecasting precision. It addresses the narrow scope of existing research on predicting international tourist arrivals at ICN with these factors. Moreover, the study demonstrates LSTM networks’ capability to effectively handle multivariable time series prediction problems, providing a robust basis for their application in hospitality and tourism management.

Originality/value

This research pioneers the integration of international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases for forecasting daily international tourist arrivals. It bridges the gap in existing literature by proposing a comprehensive approach that considers multiple predictors simultaneously. Furthermore, it demonstrates the effectiveness of LSTM networks in handling multivariable time series forecasting problems, offering practical insights for enhancing tourism demand predictions. By addressing these critical factors and leveraging advanced deep learning techniques, this study contributes significantly to the advancement of forecasting methodologies in the tourism industry, aiding decision-makers in effective planning and resource allocation.

研究目的

本研究旨在开发一种基于LSTM的强大预测模型, 用于预测仁川国际机场的日常国际游客抵达量, 结合多种预测因素, 包括汇率、WTI原油价格、韩国综合股价指数 (KOSPI) 数据和新冠疫情病例。通过利用深度学习技术和多样化数据集, 研究旨在提升旅游需求预测的准确性和可靠性, 对酒店与旅游领域的理论和实际应用有重要贡献。

研究方法

本研究通过利用长短期记忆(LSTM)网络引入创新方法, 预测国际游客抵达量。这一先进方法解决了旅游管理中的复杂管理问题, 提供了更精确的预测。方法论包括四个关键步骤: (1) 收集数据集; (2) 数据预处理; (3) 训练LSTM网络; 以及 (4) 预测未来的国际游客抵达量。本文的其余部分结构如下:后续部分详细介绍了提出的LSTM模型, 呈现了实证结果, 并讨论了研究的发现、结论以及在酒店与旅游领域的理论和实际意义。

研究发现

本研究首次同时使用包括国际游客抵达量、汇率、原油价格、股市数据和新冠疫情病例在内的大数据进行日常预测。研究显示, 整合汇率、原油价格、股市数据和新冠疫情病例显著增强了LSTM网络的预测精度。研究填补了现有研究在使用这些因素预测仁川国际机场国际游客抵达量的狭窄范围。此外, 研究证明了LSTM网络在处理多变量时间序列预测问题上的能力, 为其在酒店与旅游管理中的应用提供了坚实基础。

研究创新

本研究首次将国际游客抵达量、汇率、WTI原油价格、KOSPI数据和新冠疫情病例整合到日常国际游客抵达量的预测中。它通过提出同时考虑多个预测因素的全面方法, 弥合了现有文献的差距。此外, 研究展示了LSTM网络在处理多变量时间序列预测问题方面的有效性, 为增强旅游需求预测提供了实用见解。通过处理这些关键因素并利用先进的深度学习技术, 本研究在旅游业预测方法的进步中做出了重要贡献, 帮助决策者进行有效的规划和资源配置。

Access Restricted. View access options
Article
Publication date: 12 July 2024

Bilu Cheng and Siyu Hou

The purpose of this study is to investigate the influence of brand equity on corporate financial performance across various institutional factors in China, encompassing macro…

208

Abstract

Purpose

The purpose of this study is to investigate the influence of brand equity on corporate financial performance across various institutional factors in China, encompassing macro (regional economic development and product market development), meso (industry uncertainty), and micro (CEO overseas experience) levels.

Design/methodology/approach

Using archival data related to Chinese listed companies, this study employs standard error combined with fixed effect regression for model estimation to empirically evaluate the impact of brand equity on financial performance (Tobin’s q) and its boundary effects.

Findings

This study reveals that in China, the influence of brand equity on Tobin’s q isn’t significant. However, when considering institutional factors across various levels, its impact becomes significant. Specifically, the positive effect of brand equity on Tobin’s q in China is more pronounced in regions with higher economic or product market development, industries with high uncertainty, or when the CEO has overseas experience.

Research limitations/implications

This study enriches the brand-related marketing literature in China and highlights the potential underperformance of brand equity within this context. Furthermore, this study advances the integration of resource-based view with institutional theory by combining brand equity with institutional factors at the macro-, meso-, and micro-level in China.

Originality/value

This study focuses on brand performance in China, the largest emerging market, emphasizing the importance of integrating brand equity with diverse institutional factors to amplify its beneficial influence on financial performance.

Details

Marketing Intelligence & Planning, vol. 42 no. 8
Type: Research Article
ISSN: 0263-4503

Keywords

Access Restricted. View access options
Article
Publication date: 25 May 2022

Yee Sye Lee, Ali Rashidi, Amin Talei, Mehrdad Arashpour and Farzad Pour Rahimian

In recent years, deep learning and extended reality (XR) technologies have gained popularity in the built environment, especially in construction engineering and management. A…

814

Abstract

Purpose

In recent years, deep learning and extended reality (XR) technologies have gained popularity in the built environment, especially in construction engineering and management. A significant amount of research efforts has been thus dedicated to the automation of construction-related activities and visualization of the construction process. The purpose of this study is to investigate potential research opportunities in the integration of deep learning and XR technologies in construction engineering and management.

Design/methodology/approach

This study presents a literature review of 164 research articles published in Scopus from 2006 to 2021, based on strict data acquisition criteria. A mixed review method, consisting of a scientometric analysis and systematic review, is conducted in this study to identify research gaps and propose future research directions.

Findings

The proposed research directions can be categorized into four areas, including realism of training simulations; integration of visual and audio-based classification; automated hazard detection in head-mounted displays (HMDs); and context awareness in HMDs.

Originality/value

This study contributes to the body of knowledge by identifying the necessity of integrating deep learning and XR technologies in facilitating the construction engineering and management process.

1 – 9 of 9
Per page
102050