Search results

The purpose of this article is to increase the understanding of how improvements can be facilitated in a public service containing multiple actors in terms of identifying, aligning and prerequisites for the improvements.

The research utilizes an interactive research approach where data were gathered though a conference, workshop and a survey. The study alternately combines quality management methods such as affinity and interrelationship diagrams with computer aided text mining and latent semantic analysis.

The research shows that practitioners must consider interconnectedness between improvements and benefits that are crossing organizational levels of the public service system as well as professional borders. In public service systems, the complex reality can be better understood when improvements and benefits are classified into different organizational layers and an interconnectedness and sequence of improvement areas are acknowledged.

The research is set in the Swedish public service of the tax-paid sick leave insurance. Future research would benefit by investigating similar cases in other nations and other services.

The used methodology can be applied by practitioners to enhance a unified understanding of the system required to improve. The study also guides practitioners for how to support, relive hinders and prioritize improvements.

The research fills a gap of understanding of improvements in public services with multiple actors. As this area is difficult to improve, a novel combination of qualitative and quantitative methods paved the way for deeper and more unified understanding of the system.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

The increasing capabilities of artificial intelligence (AI) are changing the way organizations operate and interact with users both internally and externally. The insurance sector is currently using AI in several ways but its potential to disrupt insurance is not clear. This research evaluated the implementation of AI-led automation in 20 insurance companies. The findings indicate four business models (BM) emerging: In the first model the insurer takes a smaller part of the value chain allowing others with superior AI and data to take a larger part. In the second model the insurer keeps the same model and value chain but uses AI to improve effectiveness. In the third model the insurer adapts their model to fully utilize AI and seek new sources of data and customers. Lastly in the fourth model a technology focused company uses their existing AI prowess, superior data and extensive customer base, and adds insurance provision.

Social enterprises (SEs) face tensions when combining financial and social missions, and this is particularly evident in the scaling process. Although extant research mainly focuses on SEs that integrate their social and financial missions, this study aims to unpack social impact scaling strategies in differentiated hybrid organizations (DHOs) through the case of African SEs.

The study entails an inductive multiple case study approach based on four case SEs: work integration social enterprises (WISEs) and fair trade producer social enterprises (FTPSEs) in Uganda and Kenya. A total of 24 semi-structured interviews were collected together with multiple secondary data sources and then coded and analyzed through the rigorous Gioia et al. (2013) methodology to build a theoretical model.

The results indicate that SEs, as differentiated hybrids, implement four types of social impact scaling strategies toward beneficiaries and benefits (penetration, bundling, spreading and diversification) and unveil different dual mission tensions generated by each scaling strategy. The study also shows mutually reinforcing mechanisms named cross-bracing actions, which are paradoxical actions connected to one another for navigating tensions and ensuring dual mission during scaling.

This study provides evidence of four strategies for scaling social impact, with associated challenges and response mechanisms based on the cross-bracing effect between social and financial missions. Thus, the research provides a clear framework (social impact scaling matrix) for investigating differentiation in hybridity at scaling and provides new directions on how SEs scale their impact, with implications for social entrepreneurship and dual mission management literature.

The model offers a practical tool for decision-makers in SEs, such as managers and social entrepreneurs, providing insights into what scaling pathways to implement (one or multiples) and, more importantly, the implications and possible solutions. Response mechanisms are also useful for tackling specific tensions, thereby contributing to addressing the challenges of vulnerable, marginalized and low-income individuals. The study also offers implications for policymakers, governments and other ecosystem actors such as nongovernmental organizations (NGOs) and social investors.

Despite the growing body of literature on scaling social impact, only a few studies have focused on differentiated hybrids, and no evidence has been provided on how they scale only the social impact (without considering commercial scaling). This study brings a new perspective to paradox theory and hybridity, showing paradoxes come into view at scaling, and documenting how from a differentiation approach to hybridity, DHOs also implemented cross-bracing actions, which are reinforcement mechanisms, thus suggesting connections and synergies among the actions in social and financial mission, where such knowledge is required to better comprehend how SEs can achieve a virtuous cycle of profits and reinvestments in social impact.

Drawing on Bandura’s social learning theory (SLT), the purpose of this paper is to investigate, analytically, the impact that after-training skills level (i.e. perceived skill match) has on change self-efficacy. Moreover, this research also aims to identify which specific skills sets (if any) act as a protective factor during organizational change, supporting the change confidence (CC) level of the people involved.

Quantitative research was carried out on a sample of 200 workers in the bank sector.

Findings of this study suggest that skill match has a significant impact on the CC level. Furthermore, this study demonstrates that, even in front-office jobs, a perceived skill match of soft skills does not have a significant impact on staff CC, unlike that suggested by common sense and by literature.

Future research should investigate if and how a training process enables change self-efficacy over time or instead shows its utility only when it is relating to a specific and limited period.

This study suggests that in designing training, top and middle management should pay specific attention to change recipients’ needs by adopting a bottom-up approach. Moreover, to improve training effectiveness, it would be advisable to also train change recipients’ supervisors.

This study has social implications in suggesting how to foster the adaptive capabilities of change recipients in current turbulent times. In doing so, it suggests how to prevent some undesirable change consequences such as anxiety, intention to quit, work-related stress and change cynicism.

This paper shows that, from a methodological point of view, it is necessary to evaluate training effectiveness at the level of a specific skill area and not simply by comparing the trained/not trained people, as typically practiced until now.

Algorithm bias refers to repetitive computer program errors that give some users more weight than others. The aim of this article is to provide a deeper insight of algorithm bias in AI-enabled ERP software customization. Although algorithmic bias in machine learning models has uneven, unfair and unjust impacts, research on it is mostly anecdotal and scattered.

As guided by the previous research (Akter et al., 2022), this study presents the possible design bias (model, data and method) one may experience with enterprise resource planning (ERP) software customization algorithm. This study then presents the artificial intelligence (AI) version of ERP customization algorithm using k-nearest neighbours algorithm.

This study illustrates the possible bias when the prioritized requirements customization estimation (PRCE) algorithm available in the ERP literature is executed without any AI. Then, the authors present their newly developed AI version of the PRCE algorithm that uses ML techniques. The authors then discuss its adjoining algorithmic bias with an illustration. Further, the authors also draw a roadmap for managing algorithmic bias during ERP customization in practice.

To the best of the authors’ knowledge, no prior research has attempted to understand the algorithmic bias that occurs during the execution of the ERP customization algorithm (with or without AI).

This study aims to understand consumer behaviour in the context of online food delivery (OFD), especially given the mandatory lockdown imposed in some countries that have modified the behaviour of consumers. Using model goal-directed behaviour (MGB), this study was conducted to investigate consumer perceived risk on the use of OFD services.

Responses of food delivery services users were collected online throughout April 2020 to understand their risk profile and behaviour. A total of 339 responses were collected and subsequently analysed using partial least square (PLS). Both measurement and structural model were evaluated to ensure that the structural equation modelling (SEM) is valid.

The results revealed that attitude (ATT), subjective norm (SN), positive anticipated emotion (PAE) and negative anticipated emotion (NAE) and perceived behavioural control (PBC) significantly influenced users' desire. It was also found that PBC significantly influenced users' intention. The empirical result suggests that performance, privacy, financial, physical and the risk of contracting COVID-19 negatively influenced users' desire. In contrast, only physical and the risk of contracting COVID-19 negatively influenced users' intention to use OFD services.

These findings provide OFD service providers and scholars with significant insights into what compels urbanites to adopt OFD services amid a health pandemic. It also allows OFD companies to realign their operation in addressing these concerns and changes in consumer behaviour.

Against the backdrop of the pandemic, this study provides insights for OFD providers in developing new strategies and approaches for business development and consumer retention in a post-pandemic world.

本研究擬瞭解與網上訂餐相關的消費行為；尤其當有些國家推行了改變消費者行為的強制性封鎖政策的情況下，這類研究更具意義。透過應用目標導向行為模型，本研究擬探討消費者在使用網上訂餐服務時所意識到的風險。

研究人員於 2020年4月網上收集使用訂餐服務人士的意見，以瞭解其風險狀況和行為。研究共收集了339位人士的意見，並以偏最小二乘法進行分析。測量和結構模型均加以評估，以確保結構方程模型是站得住腳的。

研究結果顯示，態度、主觀規範、預期的正面和負面情緒、以及感知的行為控制，顯著地影響了用戶的慾望。研究結果亦發現，感知的行為控制顯著地影響了用戶的意圖。研究的經驗性結果暗示了表現、私隱、金錢上的爭議、可能會導致的身體損傷和感染2019冠狀病毒病的風險，負面地影響用戶的慾望。相比之下，影響著用戶使用網上訂餐服務的意圖的因素就只有可能會導致的身體損傷和感染2019冠狀病毒病的風險。

研究的結果，為提供網上訂餐服務的營運者和研究學者提供了重要的啟示，使他們更瞭解是什麼因素會迫使都市人在與健康息息相關的大流行病期間使用網上訂餐服務。而且，提供網上訂餐服務的公司亦可藉此重新調整其營運，以能處理客戶的憂慮和應付消費行為的變化。

在大流行病肆虐的背景下，本研究為網上訂餐服務提供者給予了啟示，以便他們能在後疫情時代制定新的策略和營運方法，以拓展其業務和留住消費者。

Research has examined how new ventures strengthen local economic outcomes; however, limited research examines health-oriented ventures and their impact on social outcomes, including health outcomes. Increased VC investment in healthcare service start-ups signals more activity toward this end, and the need for further academic inquiry. We examine the relationship between these start-ups and county-level health outcomes, health factors, and hospital utilization.

Data on start-ups funded via institutional venture capital from PitchBook were merged with US county-level outcomes from the County Health Rankings and Area Health Resources Files for 2010 to 2019. We investigated how the number of VC-funded healthcare service start-ups, as well as a subset defined as innovative, were associated with county-level health measures. We used panel models with two-way fixed effects and Propensity Score Matched (PSM), controlling for demographics and socioeconomic factors.

Each additional VC-funded healthcare service start-up was related to a significant 0.01 percentage point decrease in diabetes prevalence (p < 0.01), a decrease of 1.54 HIV cases per 100,000 population (p < 0.1), a 0.02 percentage point decrease in obesity rates (p < 0.01), and a 0.03 percentage point decrease in binge drinking (p < 0.01). VC-funded healthcare service start-ups were not related to hospital utilization.

This work expands our understanding of how industry-specific start-ups, in this case healthcare start-ups, relate to positive social outcomes. The results underscore the importance of evidence-based evaluation, the need for expanded outcome measures for VC investment, and the possibilities for integration of healthcare services and entrepreneurship ecosystems.