Search results

The purpose of this study is to understand the status quo of the use of Web analytics tools by European destination management organizations (DMOs) and to provide guidelines in using these metrics for business intelligence and tourism design. In addition, the goal is to improve destination management at the city level using Web analytics data.

In this exploratory study, the authors analyze how European DMOs view Web analytics data through the lens of the “data to knowledge to results” framework. The authors analyze the use of Web analytics tools by DMOs through the theory of affordances and “data-to-knowledge framework” developed by Davenport et al., which incorporates several factors that contribute to a successful transformation of data available to an organization to knowledge, desirable results and ultimately to building an analytical capability.

The results show that European DMOs mainly use Web analytics data for website quality assurance, but that some are also using them to drive marketing programs. The study concludes by providing several suggestions for ways in which DMOs might optimize the use of Web analytics data, which will also improve the management of destinations.

Web analytics tools are used by many organizations such as DMOs to collect traffic data, to evaluate and optimize websites. However, these metrics can also be combined with other data such as bednights numbers and used for forecasting or other managerial decisions for destination management at the city level. There is a research gap in this area that focuses on using Web analytics data for business intelligence in the tourism industry and this research aims to fill this gap.

The purpose of this paper is to develop an analytical hierarchy process (AHP)‐based selection model for choosing a web analytics product/service that meets organizational needs.

The research objective is achieved through modeling and empirical validation.

While more criteria could be added, the proposed selection model provides a feasible approach to choosing a web analytics product/service. Cost‐ and risk‐related criteria are weighed heavier than those of technical capabilities. Tools based on the page tagging method are more popular than those based on transaction log file analysis. The level of technology savvy might play a role in the application of the selection model.

The development of web analytics products/service is still evolving. Thus, as the use of web analytics increases, more criteria might be identified and added to the model. The model is validated by groups for different sectors. In the future, it is suggested to conduct a similar study with one sector by different groups.

The selection model provides a process in which practitioners can systematically evaluate pros and cons of web analytics products/services. The selection model includes a comprehensive list of criteria that vendors of web analytics products/services can use to benchmark their products. Following this model, an organization contemplating the use of web analytics will more likely find one product/service that accommodates organizational and technological characteristics.

A sufficiently comprehensive list of qualitative and quantitative criteria for evaluating web analytics products/services was developed. Practitioners will be able to use the model to select a proper tool. In academia, the article fills a gap in literature that might bring academics' interests in this area.

This paper is designed to illustrate how clickstream data, collected from a B2B web site and then analyzed using web analytics software, can be used to evaluate and improve B2B web site performance. A number of issues in the application of clickstream data and web analytics software are to be identified and discussed.

A case study approach is used to present some of the technical issues in the field of web analytics and to demonstrate their value in B2B web site management. Three field experiments, focusing on incorporating ways to discourage shopping‐cart abandonment and the use of two different free‐shipping promotions, were used as the basic research method for collecting the data. Web traffic conversion funnels are used to conduct the analysis and present the findings.

The analysis of clickstream data using web analytics procedures serves as a useful tool in the enhancement of a B2B web site by investigating how visitors move through the web site conversion process and complete their purchase. Improved sales result from each of the three field experiments.

Researchers may use the paper as evidence that web analytics methods can be applied successfully in a B2B application for a technology‐oriented company.

The paper illustrates the use of clickstream data to measure the progression of web site visitors through the conversion process toward purchase.

Insight is provided into the usefulness of web analytics as a framework for performance measurement that is used to drive success for B2B web sites.

This study aims to investigate and explore the impact of big data analytics (BDA) as a mechanism that could develop the ability to measure customers’ performance. To accomplish the research aim, the theoretical discussion was developed through the combination of the diffusion of innovation theory with the technology acceptance model (TAM) that is less developed for the research field of this study.

Empirical data was obtained using Web-based quasi-experiments with 104 Egyptian accounting professionals. Further, the Wilcoxon signed-rank test and the chi-square goodness-of-fit test were used to analyze data.

The empirical results indicate that measuring customers’ performance based on BDA increase the organizations’ ability to analyze the customers’ unstructured data, decrease the cost of customers’ unstructured data analysis, increase the ability to handle the customers’ problems quickly, minimize the time spent to analyze the customers’ data and obtaining the customers’ performance reports and control managers’ bias when they measure customer satisfaction. The study findings supported the accounting professionals’ acceptance of BDA through the TAM elements: the intention to use (R), perceived usefulness (U) and the perceived ease of use (E).

This study has several limitations that could be addressed in future research. First, this study focuses on customers’ performance measurement (CPM) only and ignores other performance measurements such as employees’ performance measurement and financial performance measurement. Future research can examine these areas. Second, this study conducts a Web-based experiment with Master of Business Administration students as a study’s participants, researchers could conduct a laboratory experiment and report if there are differences. Third, owing to the novelty of the topic, there was a lack of theoretical evidence in developing the study’s hypotheses.

This study succeeds to provide the much-needed empirical evidence for BDA positive impact in improving CPM efficiency through the proposed framework (i.e. CPM and BDA framework). Furthermore, this study contributes to the improvement of the performance measurement process, thus, the decision-making process with meaningful and proper insights through the capability of collecting and analyzing the customers’ unstructured data. On a practical level, the company could eventually use this study’s results and the new insights to make better decisions and develop its policies.

This study holds significance as it provides the much-needed empirical evidence for BDA positive impact in improving CPM efficiency. The study findings will contribute to the enhancement of the performance measurement process through the ability of gathering and analyzing the customers’ unstructured data.

The purpose of this study is to assess the use of digital collections created via the large-scale digitization of archival collections. The large-scale digitization method specifically examined is the reuse of archival description from finding aids to create digital collections that consist mainly of compound digital objects, equivalent to a folder of items, minimally described at the aggregate level. This paper compares Web analytics data for two large-scale digital collections and one digital collection with rich, item-level description.

This study analyzed one year of Web analytics for three digital collections. The main research question of this study is: Are digital collections of minimally described compound objects used less than digital collections of richly described single objects?

This study found that the large-scale digital collections analyzed received less use than the traditional item-level collection, when examined at the item level. At the object level, the large-scale collections did not always receive less use than the traditional item-level collection.

This study is limited to three different digital collections from one institution. Web analytics also represent a limited interpretation of “use.”

This study presents a method for other institutions to assess their own large-scale digitization efforts and contributes to the profession’s understanding of the impact of large-scale digitization.

This paper is unique because it uses Web analytics to compare the use of large-scale digital collections to the use of traditional boutique digital collections.

This research attempts to highlight the concept of big data analytics in predictive maintenance for maintenance management of government buildings in Malaysia.

This study uses several empirical analyses such as vector autoregression (VAR), vector error correction model (VECM), ARMA model and Granger causality to analyse predictive maintenance by using big data analytics concept.

The results indicate that there are strong correlations among these variables, which indicate reciprocal predictive maintenance of maintenance management job function. The findings also showed that there are significant needs of application of big data analytics for maintenance management in Putrajaya, Malaysia, to ensure the efficient maintenance of government buildings.

The conducted case study has demonstrated the empirical perspective which streamlines with the big data analytics' concept in maintenance, especially for analytics' support with appropriate empirical methodology

Introduction: The Internet has tremendously transformed the computer and networking world. Information reaches our fingertips and adds data to our repository within a second. Big data was initially defined as three Vs, where data come with greater variety, increasing volumes and extra velocity. Big data is a collection of structured, unstructured and semi-structured data gathered from different sources and applications. It has become the most powerful buzzword in almost all the business sectors. The real success of any industry can be counted based on how the big data is analysed, potential knowledge is discovered and productive business decisions are made. New technologies such as artificial intelligence and machine learning have added more efficiency to storing and analysing data. This big data analytics (BDA) becomes more valuable to those companies, focusing on getting insight into customer behaviour, trends and patterns. This popularity of big data has inspired insurance companies to utilise big data at their core systems and advance the financial operations, improve customer service, construct a personalised environment and take all possible measures to increase revenue and profits.

Purpose: This study aims to recognise what big data stands for in the insurance sector and how the application of BDA has opened the door for new and innovative changes in the insurance industry.

Methodology: This study describes the field of BDA in the insurance sector, discusses the benefits, outlines tools, architectural framework, the method, describes applications in general and specific and briefly discusses the opportunities and challenges.

Findings: The study concludes that BDA in insurance is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however, there remain challenges to overcome.

This paper aims to investigate emerging trends in data analytics and knowledge management (KM) job market by using the knowledge, skills and abilities (KSA) framework. The findings from the study provide insights into curriculum development and academic program design.

This study traced and retrieved job ads on LinkedIn to understand how data analytics and KM interplay in terms of job functions, knowledge, skills and abilities required for jobs, as well as career progression. Conducting content analysis using text analytics and multiple correspondence analysis, this paper extends the framework of KSA proposed by Cegielski and Jones‐Farmer to the field of data analytics and KM.

Using content analysis, the study analyzes the requisite KSA that connect analytics to KM from the job demand perspective. While Kruskal–Wallis tests assist in examining the relationships between different types of KSA and company’s characteristics, multiple correspondence analysis (MCA) aids in reducing dimensions and representing the KSA data points in two-dimensional space to identify potential associations between levels of categorical variables. The results from the Kruskal–Wallis tests indicate a significant relationship between job experience levels and KSA. The MCA diagrams illustrate key distinctions between hard and soft skills in data across different experience levels.

The practical implications of the study are two-fold. First, the extended KSA framework can guide KM professionals with their career planning toward data analytics. Second, the findings can inform academic institutions with regard to broadening and refining their data analytics or KM curricula.

This paper is one of the first studies to investigate the connection between data analytics and KM from the job demand perspective. It contributes to the ongoing discussion and provides insights into curriculum development and academic program design.

The purpose of this paper is to describe the benefits of integrating personalization within a library web site and presents methodology for achieving this goal within an academic setting.

The project documented in this study explores the use of student course enrollment data as the basis for creating a personalized library web site. Off-the-shelf, open source applications are used in conjunction with existing university data to deliver a final product that offers an enhanced user experience for the university community.

Adaptive personalization is increasingly commonplace on the web. Academic libraries have a unique source of existing data that offers the potential of adding personalization to the library web site. At present, the personalization of library online services remains largely unexplored. This project illustrates one relatively low-cost method to help libraries interested in creating personalized web sites.

This paper provides a guide for libraries interested in the implementation of personalization within their web sites.

The project described in this case study is highly unique within libraries. The paper outlines the feasibility and technical requirements associated with using course enrollment data to add personalized content to a library web site.

The purpose of this paper is to investigate the effects of Big Data analytics, data security and service supply chain innovation capabilities on services supply chain performance.

The paper draws on the relational view of resource-based theory to propose a theoretical model. The data were collected through survey of 145 service firms.

The results of this study found that the Big Data analytics has a positive and significant relationship with a firm’s ability to manage data security and a positive impact on service supply chain innovation capabilities and service supply chain performance. This study also found that most service firms participating in this study used Big Data analytics to execute existing algorithms faster with larger data sets.

A main recommendation of this study is that service firms empower a chief data officer to establish the data needed and design the governance of data in the company to eliminate any security issues. Data security was a concern if a firm did not have ample data governance and protection as the information was shared among members of service supply chain networks.

Big Data analytics are a useful technology tool to forecast market preference based on open source, structured and unstructured data.