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This viewpoint study aims to make the case that the field of knowledge management (KM) must respond to the significant changes that big data/analytics is bringing to operationalizing the production of organizational data and information.

This study expresses the opinions of the guest editors of “Does Big Data Mean Big Knowledge? Knowledge Management Perspectives on Big Data and Analytics”.

A Big Data/Analytics-Knowledge Management (BDA-KM) model is proposed that illustrates the centrality of knowledge as the guiding principle in the use of big data/analytics in organizations.

This is an opinion piece, and the proposed model still needs to be empirically verified.

It is suggested that academics and practitioners in KM must be capable of controlling the application of big data/analytics, and calls for further research investigating how KM can conceptually and operationally use and integrate big data/analytics to foster organizational knowledge for better decision-making and organizational value creation.

The BDA-KM model is one of the early models placing knowledge as the primary consideration in the successful organizational use of big data/analytics.

This paper aims to investigate the value of big data investments by examining the market reaction to company announcements of big data investments and tests the effect for firms that are either knowledge intensive or not.

This study is based on an event study using data from two stock markets in China.

The stock market sees an overall index increase in stock prices when announcements of big data investments are revealed by grouping all the listed firms included in the sample. Increased stock prices are also the case for non-knowledge intensive firms. However, the stock market does not seem to react to big data investment announcements by testing the knowledge intensive firms along.

This study contributes to the literature on assessing the economic value of big data investments from the perspective of big data information value chain by taking an unexpected change in stock price as the measure of the financial performance of the investment and by comparing market reactions between knowledge intensive firms and non-knowledge intensive firms. Findings of this study can be used to refine practitioners’ understanding of the economic value of big data investments to different firms and provide guidance to their future investments in knowledge management to maximize the benefits along the big data information value chain. However, findings of study should be interpreted carefully when applying them to companies that are not publicly traded on the stock market or listed on other financial markets.

Based on the concept of big data information value chain, this study advances research on the economic value of big data investments. Taking the perspective of stock market investors, this study investigates how the stock market reacts to big data investments by comparing the reactions to knowledge-intensive firms and non-knowledge-intensive firms. The results may be particularly interesting to those publicly traded companies that have not previously invested in knowledge management systems. The findings imply that stock investors tend to believe that big data investment could possibly increase the future returns for non-knowledge-intensive firms.

Online shopping has continued to grow in popularity, and the advance of internet technology has enhanced customers’ experiences. One technology online retailers have been using to increase sales is virtual try-on (VTO). The purpose of this paper is to investigate how such technology affects online consumers’ purchase decision process towards purchase intention, especially from an integration of utilitarian, hedonic and risk perspectives, by using advanced partial least square (PLS) approaches.

This study applied a web-based survey approach for data collection from online apparel retailing websites. The survey instrument was developed by adapting previously validated measurement items. The valid data collected were analysed using PLS with multi-group analyses. Advanced PLS techniques such as examination of discriminant validity using heterotrait-monotrait ratio, tests of out-of-sample prediction performance, and measurement invariance of composite models were applied.

The results of examining the proposed model reveal that customers’ attitude towards VTO technology can affect their intention to purchase a garment online, which is affected by perceived usefulness, perceived enjoyment and perceived privacy risk. Perceived ease of use is found to affect perceived usefulness and perceived helpfulness. The results also show no significant differences among age groups and genders in terms of the role of VTO technology in the full decision process towards online purchase intention.

This study enhances the understanding of the roles that VTO technology plays in consumers’ online purchase intention by providing an integrative view of its utilitarian value, hedonic value and risk. This study demonstrates the feasibility of applying advanced PLS techniques to investigate online consumer behaviour, particularly in the field of VTO application in online retailing. Implications for online retailers and designers of VTO technology are also derived from the findings.

This research proposes a decision framework for using non-financial measures to define a replenishment policy for perishable health products. These products are perishable and substitutable by nature and create complexities for managing inventory. Instead of a financial measure, numerous measures should be considered and balanced to meet business objectives and enhance inventory management.

This research applies a multi-methodological approach and develops a framework that integrates discrete event simulation (DES), analytic hierarchy process (AHP) and data envelopment analysis (DEA) techniques to define the most favourable replenishment policy using non-financial measures.

The integration framework performs well as illustrated in the numerical example; outcomes from the framework are comparable to those generated using a traditional, financial measures-based, approach. This research demonstrates that it is feasible to adopt non-financial performance measures to define a replenishment policy and evaluate performance.

The framework, thus, prioritises non-financial measures and addresses issues of lacking information sharing and employee involvement to enhance hospitals' performance while minimising costs. The non-financial measures improve cross-functional communication while supporting simpler transformations from high-level strategies to daily operational targets.

This study aims to use financial stability and health facilities of countries, to cluster them for making a more consensus environment for manifesting the status of Covid-19 in a justifiable manner. The scarcity of the categorisation of the countries of the world in a common platform, and the requirement of manifesting the pandemic status such as Covid-19 in a justifiable manner create the demanding requirement. This study mainly focusses on assisting to generate a liable manifesto to criticise the span of viral infection of the severe acute respiratory syndrome coronavirus-2 over the globe.

Data for this study has been gathered from official websites of the World Bank, and the world in data. The Louvain clustering method has been used to cluster the countries based on their financial strength and health facilities. The resulted clusters are visualised using Silhouette plots. The anomalies of the clusters had been used to quantify the pandemic situation. The status of Covid-19 has been manifested with the time series analysis through python programming.

The countries of the world have been clustered into seven, where developed countries divided into three clusters and the countries with transition economies and developing clustered together into four clusters. The time series analysis of recognised anomalies of the clusters assist to monitor the government responses and analyse the efficiency of used safety measures against the pandemic.

This study’s resulted clusters are highly valuable as a division of countries of the whole world for evaluating the health systems and for the regional levels. Further, the results of time series analysis are beneficial in monitoring the government responses and analysing the efficiency of used safety measures against the pandemic.

The purpose of this paper is to explain how enterprise resource planning (ERP) implementation evolves by cloud computing in different industries with different delivery models of cloud ERP. This paper also investigates infrastructure as a service (IaaS) as a delivery approach for cloud ERP. Case research on IaaS is rarely found in the literature. In addition, this paper intends to reveal how this transformation from on-premises to the cloud would influence the ERP implementation process.

A multiple-case study is conducted to identify the different deployed models of cloud ERP systems in the implementation projects. The influences of emerging cloud computing technology on ERP implementation are investigated by interviewing consultants related to the projects.

The findings illustrate that not only software as a service (SaaS) but also IaaS and platform as a service cloud computing services are widely applied in cloud ERP implementation. This study also indicates that certain technical limitations of cloud ERP might have a positive effect on the outcome of ERP implementation.

This study investigates how cloud computing influences ERP implementation from different aspects. The result identifies both SaaS and IaaS as two different approaches widely adopted in cloud ERP implementation. Besides, this study has discussed in-depth and analyzed these two cloud ERP paradigms in five factors, including functionality, performance, portability, security, cost and customization. The classification and suggestions are original to the literature.

The purpose of this study is to investigate the motivational process underlying users’ intention to provide feedback on user-contributed knowledge in professional online communities. User feedback can serve as a means of indicating the credibility of the online content, which can help community members in their knowledge-seeking process. Adopting such a user feedback mechanism is beneficial for users to identify relevant and credible content efficiently and for an online community to sustain itself.

Drawing on self-determination theory, an integrated model is proposed. In this model, behavioural intention is defined as the consequence of motivational orientations whose antecedences include various social factors. The model is empirically tested using survey data collected online and the structural equation modelling techniques.

The results show that users’ intention to provide feedback is primarily influenced by autonomous motivation. Autonomous motivation is in turn affected by social factors, including reciprocity, online reputation, trust in the user involvement mechanisms and affective and normative community commitments.

This study adds value to prior studies by stressing the significance and feasibility of user feedback in helping members of professional online communities with their knowledge-seeking process. It also contributes to the literature on user participation in these communities by showing the efficacy of a motivational process perspective and the role of motivational orientations, in particular, in explaining users’ behavioural intention.