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This paper identifies opportunities for potential theoretical and practical improvements in employees' awareness of cybersecurity and their motivational behavior to protect themselves and their organizations from cyberattacks using the protection motivation theory. In addition, it contributes to the literature by examining additional variables and mediators besides the core constructs of the Protection Motivation Model (PMT). This article uses empirical data and structural equation modeling to test the antecedents and mediators of employees' cybersecurity motivational behavior. The study offers theoretical and pragmatic guidance for cybersecurity programs. First, the model developed in this study can partially explain how people may change their cybersecurity protection behavior about security threats and coping actions. Secondly, the result of the study indicates that security coping factors are reliable predictors in projecting individual intention to take protective measures. Third, organizational effort in combatting cyber threats and increasing employee awareness is significantly associated with the use of cyber threat coping processes. Additionally, several practical prescriptions are suggested based on gender, generations, and types of organizations. For example, government organizations have taken well-designed cybersecurity measures and developed detailed protocols to enhance employees' motivational behavior. Finally, future cybersecurity training materials should adapt to the unique traits of different generations, especially the Gen Edge group and digital natives for all cybersecurity subjects.

PurposeThe paper aims to help enterprises gain valuable knowledge about big data implementation in practice and improve their information management ability, as they accumulate experience, to reuse or adapt the proposed method to achieve a sustainable competitive advantage.Design/methodology/approachGuided by the theory of technological frames of reference (TFR) and transaction cost theory (TCT), this paper describes a real-world case study in the banking industry to explain how to help enterprises leverage big data analytics for changes. Through close integration with bank's daily operations and strategic planning, the case study shows how the analytics team frame the challenge and analyze the data with two analytic models – customer segmentation (unsupervised) and product affinity prediction (supervised), to initiate the adoption of big data analytics in precise marketing.FindingsThe study reported relevant findings from a longitudinal data analysis and identified some key success factors. First, non-technical factors, for example intuitive analytics results, appropriate evaluation baseline, multiple-wave implementation and selection of marketing channels critically influence big data implementation progress in organizations. Second, a successful campaign also relies on technical factors. For example, the clustering analytics could promote customers' response rates, and the product affinity prediction model could boost efficient transaction and lower time costs.Originality/valueFor theoretical contribution, this paper verified that the outstanding characteristics of online mutual fund platforms brought up by Nagle, Seamans and Tadelis (2010) could not guarantee organizations' competitive advantages from the aspect of TCT.

Purpose
Few academic studies specifically investigate how businesses can use social media to innovate customer loyalty programs. The purpose of this paper is to present an in-depth case study of the Shop Your Way (SYW) program, which is regarded as one of the most successful customer loyalty programs with social media.


Design/methodology/approach
This paper uses case study research as the methodology to uncover innovative features associated with the SYW customer loyalty program. The authors collected the data from SYW's social media forums and tweets. The data set was analyzed using social media analytics tools including the R package and Lexicon.


Findings
Based on the research results, the authors summarize innovative social media features identified from SYW. The authors also provide insights and recommendations for businesses that are seeking to innovate their customer loyalty programs using social media technologies.


Originality/value
The results of this case study set a good example for businesses which want to innovate and improve their customer loyalty programs using social media technologies. This is the first in-depth case study on the SYW program, one of the most successful customer loyalty programs with social media. The results shed light on how social media can innovate customer loyalty programs in both theory and practice.

Cloud manufacturing has recently become a hot research topic in the manufacturing industry. One of the key bottlenecks that hinders the development and application of cloud manufacturing is security. As the adoption and use of manufacturing cloud depends on its security mechanism to a large extent, we propose a new resource security method to enhance the security of cloud manufacturing services by providing resources exclusive access to cloud virtual machine and restricting cloud access from unauthorized users. To enable authorized users in the right location, right time and right network to access resources, a GeoAuthentication model that maps geographic location, access time, and subnetwork information into a secret key is proposed. We also propose conflict firewall in manufacturing cloud to separate users with conflict of interest.

PurposeRisks resulted from asymmetric information have become crucial barriers for commercial banks to implement supply chain finance (SCF) – mainly the inventory pledge financing (IPF). At the same time, online financial service providers (OFSPs) are emerging as strong competitors in the SCF market. As a result, commercial banks need to update their traditional SCF business models and alleviate their over-dependence on OFSPs.Design/methodology/approachThe authors employ a multi-case-study method to investigate how the Internet of things (IoT) and blockchain technologies can be jointly leveraged to mitigate SCF risks. In-depth interviews were conducted to depict the business models and their novel ecosystem to reinforce traditional banks' ability in SCF services.FindingsFrom the perspective of information asymmetry, the authors categorize IPF risks into three groups based on the principal-agent theory: collateral, warehousing and liquidity risk. The findings suggest that IoT can primarily improve traditional banks' information acquisition ability, and blockchain can facilitate credible information transformation, enabling banks to acquire knowledge from collaterals. Besides, the e-platform in the new architecture increases banks' involvement in the supply chain and builds a fair network to curtail warehousing risks. The employment of smart contracts and collaborative mechanism ensure process and outcome control in mitigating liquidity risks.Originality/valueThe research contributes to the literature by confirming the role of emerging technologies in reducing information asymmetry risks. Besides, the findings provide valuable insights for practitioners to promote effective practices and approaches in IPF.

AbstractTraditional case‐based reasoning (CBR) research has focused on the theories and heuristics of case representation, retrieval, reuse, revision and retention, while little attention has been paid to the users of CBR systems. In an effort to facilitate and improve the literature related to the CBR cycle as it relates to users, this paper proposes an extended CBR framework which allows users to play a more active role. The extended framework integrates the classical CBR R4‐Cycle approach with Web 2.0 technologies such as blogs and RSS (Really Simple Syndication) feeds. The purpose of the extended CBR 2.0 framework is to encourage and enable users to contribute to case retrieval, case revision, case reuse and case retention. This paper also introduces some ideas to deploy Web 2.0 technologies along with CBR systems, with a discussion of the integration of Web 2.0 and CBR systems in systems perspectives. Copyright © 2009 John Wiley & Sons, Ltd.

PurposeThis study aims to examine how different hospitals utilize social media to communicate risk information about COVID-19 with the communities they serve, and how hospitals' social media messaging (firm-generated content and their local community's responses (user-generated content) evolved with the COVID-19 outbreak progression.Design/methodology/approachThis research proposes a healthcare-specific social media analytics framework and studied 68,136 tweets posted from November 2019 to November 2020 from a geographically diverse set of ten leading hospitals' social media messaging on COVID-19 and the public responses by using social media analytics techniques and the health belief model (HBM).FindingsThe study found correlations between some of the HBM variables and COVID-19 outbreak progression. The findings provide actionable insight for hospitals regarding risk communication, decision making, pandemic awareness and education campaigns and social media messaging strategy during a pandemic and help the public to be more prepared for information seeking in the case of future pandemics.Practical implicationsFor hospitals, the results provide valuable insights for risk communication practitioners and inform the way hospitals or health agencies manage crisis communication during the pandemic For patients and local community members, they are recommended to check out local hospital's social media sites for updates and advice.Originality/valueThe study demonstrates the role of social media analytics and health behavior models, such as the HBM, in identifying important and useful data and knowledge for public health risk communication, emergency responses and planning during a pandemic.