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Article
Publication date: 25 September 2009

Mireia Ribera, Merce Porras, Marc Boldu, Miquel Termens, Andreu Sule and Pilar Paris

The purpose of this paper is to explain the changes in the Web Content Accessibility Guidelines (WCAG) 2.0 compared with WCAG 1.0 within the context of its historical development.

1990

Abstract

Purpose

The purpose of this paper is to explain the changes in the Web Content Accessibility Guidelines (WCAG) 2.0 compared with WCAG 1.0 within the context of its historical development.

Design/methodology/approach

In order to compare WCAG 2.0 with WCAG 1.0 a diachronic analysis of the evolution of these standards is done. Known authors and publications in the field, the Web Accessibility Initiative (WAI) web pages, WebAIM and the blogosphere were also monitored for comments and third‐party analyses. The analysis of the main changes from WCAG 1.0 to WCAG 2.0 was based on personal experience with WAI guidelines, experimentation with some of the new guidelines, and a selection of best practice online services in the application of the WCAG, including WAI documentation.

Findings

WCAG 2.0 is more educational and is applied to more technologies than WCAG 1.0. The limitations of WCAG 1.0 are mostly due to its origin. In changing from one to the other, new priorities and new elements must be taken into account. The paper concludes that though these guidelines are a useful tool for governments, they are only the first step towards accessibility, which can only be achieved through user‐centred design.

Originality/value

This paper explains the significance and limitations of the WCAG and gives a short guide to adapting web sites to the new regulations.

Details

Program, vol. 43 no. 4
Type: Research Article
ISSN: 0033-0337

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Article
Publication date: 25 April 2023

Mahima Shukla, Richa Misra and Rahul Gupta

This study aims to use empowerment theory to examine the relationship between a user's engagement type (active or passive) and psychological empowerment (intrapersonal and…

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Abstract

Purpose

This study aims to use empowerment theory to examine the relationship between a user's engagement type (active or passive) and psychological empowerment (intrapersonal and interactional) in the context of a social media brand community (SMBC). This study also looks at the impact of psychological empowerment on brand community commitment (CC) and brand loyalty.

Design/methodology/approach

Convenience and snowball sampling were used to select respondents from mobile phone brand communities in India. The conceptual model was tested using structure equation modelling.

Findings

According to the study findings, active user involvement in SMBC is strongly associated to both intrapersonal and interactional empowerment (IE), but passive user engagement is weakly related to IE. Furthermore, customer empowerment and CC have a strong impact on brand CC and brand loyalty.

Practical implications

SMBC is now a significant point of contact for building strong consumer–brand relationships. SMBC members who are actively involved in the community have greater emotional bonding, trust and commitment to the brand. Therefore, social media marketers should encourage their customers to get involved in a brand community and empower them by involving them in brand related decision, etc. This will help the community grow and thrive.

Originality/value

This study addresses a research gap by examining how active and passive members of an SMBC facilitate both focal points of psychological empowerment (intrapersonal and interactional), which increase the brand community's commitment and brand loyalty.

Details

Journal of Consumer Marketing, vol. 40 no. 6
Type: Research Article
ISSN: 0736-3761

Keywords

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Article
Publication date: 16 August 2022

Liyao Huang, Cheng Li and Weimin Zheng

Given the importance of spatial effects in improving the accuracy of hotel demand forecasting, this study aims to introduce price and online rating, two critical factors…

770

Abstract

Purpose

Given the importance of spatial effects in improving the accuracy of hotel demand forecasting, this study aims to introduce price and online rating, two critical factors influencing hotel demand, as external variables into the model, and capture the spatial and temporal correlation of hotel demand within the region.

Design/methodology/approach

For high practical implications, the authors conduct the case study in Xiamen, China, where the hotel industry is prosperous. Based on the daily demand data of 118 hotels before and during the COVID-19 period (from January to June 2019 and from January to June 2021), the authors evaluate the prediction performance of the proposed innovative model, that is, a deep learning-based model, incorporating graph convolutional networks (GCN) and gated recurrent units.

Findings

The proposed model simultaneously predicts the daily demand of multiple hotels. It effectively captures the spatial-temporal characteristics of hotel demand. In addition, the features, price and online rating of competing hotels can further improve predictive performance. Meanwhile, the robustness of the model is verified by comparing the forecasting results for different periods (during and before the COVID-19 period).

Practical implications

From a long-term management perspective, long-term observation of market competitors’ rankings and price changes can facilitate timely adjustment of corresponding management measures, especially attention to extremely critical factors affecting forecast demand, such as price. While from a short-term operational perspective, short-term demand forecasting can greatly improve hotel operational efficiency, such as optimizing resource allocation and dynamically adjusting prices. The proposed model not only achieves short-term demand forecasting, but also greatly improves the forecasting accuracy by considering factors related to competitors in the same region.

Originality/value

The originalities of the study are as follows. First, this study represents a pioneering attempt to incorporate demand, price and online rating of other hotels into the forecasting model. Second, integrated deep learning models based on GCN and gated recurrent unit complement existing predictive models using historical data in a methodological sense.

Details

International Journal of Contemporary Hospitality Management, vol. 35 no. 1
Type: Research Article
ISSN: 0959-6119

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