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This study aims to understand how informational factors influence online purchase intention when considering secondhand books.

A conceptual model linking book condition, description, delivery cost, picture, sellers’ rating and delivery date to purchase intention was developed and tested by using structural equation modeling. Survey data from 234 respondents was used to analyze both direct and mediating relationships.

The examination demonstrates how book condition, delivery cost and sellers’ rating influence consumers’ purchase intention. Book condition directly and indirectly influenced purchase intention, while delivery cost and sellers’ rating were significant only when including the mediating variable delivery date.

This work clarifies where resources should be allocated when offering secondhand books online. Sellers should dedicate time to include informational factors such as book condition, delivery cost and sellers’ rating. Contra wise, there is little value expounding on the book’s description or providing a high-quality picture when selling online.

The COVID-19 pandemic has disrupted the ‘real’ world and substantially impacted the virtual world and thus the Internet ecosystem. It has caused a significant exogenous shock that offers a wealth of natural experiments and produced new data about broadband, clouds, and the Internet in times of crisis. In this chapter, we characterise and evaluate the evolving impact of the global COVID-19 crisis on traffic patterns and loads and the impact of those on Internet performance from multiple perspectives. While we place a particular focus on deriving insights into how we can better respond to crises and better plan for the post-COVID-19 ‘new normal’, we analyse the impact on and the responses by different actors of the Internet ecosystem across different jurisdictions. With a focus on the USA and Europe, we examine the responses of both public and private actors, with the latter including content and cloud providers, content delivery networks, and Internet service providers (ISPs). This chapter makes two contributions: first, we derive lessons learned for a future post-COVID-19 world to inform non-networking spheres and policy-making; second, the insights gained assist the networking community in better planning for the future.

The study examines health care inequities in viral load testing among hepatitis C (HCV) antibody-positive patients. The analysis predicts whether individual and census tract sociodemographic characteristics impact the likelihood of viral load testing.

This a study of 26,218 HCV antibody-positive patients in Orange County, California, from 2010 to 2020. The case data were matched with the 2017 American Community Survey to help understand the role of neighborhood socioeconomic characteristics in testing for viral load. Multivariable logistic regression was used to predict the probability of ever testing for HCV viral load.

Thirty-six percent of antibody-positive persons were never viral load tested. The results show inequalities in viral load testing by sociodemographic factors. The following groups were less likely to ever test for viral load than their counterparts: (1) individuals under 65 years old, (2) females, (3) residents of census tracts with lower levels of health insurance enrollment, (4) residents of census tracts with lower levels of government health insurance, and (5) residents of census tracts with a higher proportion of non-white residents.

This is a secondary database from public health department reports. Using census tract data raises the issue of the ecological fallacy. Detailed medical records were not available. The results of this study emphasize the social inequality in viral load testing for HCV. These groups are less likely to be treated and cured, and may spread the disease to others.

This chapter is unique as it combines routinely collected public health department data with census tract level data to examine social inequities associated with lower rates of HCV viral load testing.

Jarring events, be they global crises such as COVID-19 or technological events such as the Cambridge Analytica data incident, have bullwhip effects on billions of people's daily lives. Such “shocks” vary in their characteristics. While some shocks cause, for example, widespread adoption of information systems (IS) as diverse as Netflix and Teams, others lead users to stop using IS, such as Facebook. To offer insights into the multifaceted ways shocks influence user behavior, this study aims to assess the status quo of shock-related literature in the IS discipline and develop a taxonomy that paves the path for future IS research on shocks.

This study conducted a literature review (N = 70) to assess the status quo of shock-related research in the IS discipline. Through a qualitative study based on users who experienced shocks (N = 39), it confirmed the findings of previous literature in an illustrative IS research context. Integrating the findings of the literature review and qualitative study, this study informs a taxonomy of shocks impacting IS use.

This study identifies different ways that shocks influence user behavior. The taxonomy reveals that IS research could profit from considering environmental, private and work shocks and shedding light on positive shocks. IS research could also benefit from examining the urgency of shocks, as there are indications that this influences how and when individuals react to a specific shock.

Findings complement previous rational explanations for user behavior by showing technology use can be influenced by shocks. This study offers a foundation for forward-looking research that connects jarring events to patterns of technology use.

Modular integrated construction (MiC) is an innovative and effective manufacturing-based method of construction that has become the mainstream development direction of projects in Hong Kong (HK). However, large-scale promotion of MiC practice still needs efforts. A pressing concern is that the impact of relevant policies on stakeholders during project implementation is rarely explored in depth. Therefore, to fill the research gap, this study aims to investigate the influence of policies on stakeholders to drive the successful implementation of MiC in HK.

This study uses a strategy of multiple methods. First, a comprehensively literature review and survey were adopted to identify critical policies and stakeholders. Second, semi-structured interviews with 28 experts were conducted to quantify their relationships. Third, three policy–stakeholder networks at initiation, planning and design and construction stages were established using social network analysis.

Environmental protection policy, COVID-19 pandemic policy and environmental protection policy and quality acceptance standard for project completion are found to be the most important policies of the three stages, respectively. The HK government and developers are highlighted as prominent stakeholders influencing policy implementation at all three stages. The dynamics of the influence stakeholders receive from critical policies at different stages of MiC are discussed. Valuable recommendations are accordingly proposed to enhance the successful implementation of MiC projects from the perspective of various stakeholders.

This study contributes to the body of knowledge by considering the mediating influence of stakeholders during policy implementation in the MiC uptake, and is valuable in helping policymakers to deeply understand the influence of policies to further forward successful MiC implementation and practicality in HK.

We studied the relationship between job engagement and systematic problem solving (SPS) among shop-floor employees and how lean production (LP) and Internet of Things (IoT) systems moderate this relationship.

We collected data from a sample of 440 shop floor workers in 101 manufacturing work units across 33 plants. Because our data is nested, we employed a series of multilevel regression models to test the hypotheses. The application of IoT systems within work units was evaluated by our research team through direct observations from on-site visits.

Our findings indicate a positive association between job engagement and SPS. Additionally, we found that the adoption of lean bundles positively moderates this relationship, while, surprisingly, the adoption of IoT systems negatively moderates this relationship. Interestingly, we found that, when the adoption of IoT systems is complemented by a lean management system, workers tend to experience a higher effect on the SPS of their engagement.

One limitation of this research is the reliance on the self-reported data collected from both workers (job engagement, SPS and control variables) and supervisors (lean bundles). Furthermore, our study was conducted in a specific country, Italy, which might have limitations on the generalizability of the results since cross-cultural differences in job engagement and SPS have been documented.

Our findings highlight that employees’ strong engagement in SPS behaviors is shaped by the managerial and technological systems implemented on the shop floor. Specifically, we point out that implementing IoT systems without the appropriate managerial practices can pose challenges to fostering employee engagement and SPS.

This paper provides new insights on how lean and new technologies contribute to the development of learning-to-learn capabilities at the individual level by empirically analyzing the moderating effects of IoT systems and LP on the relationship between job engagement and SPS.