THE IMPACT OF POWER BOUNDARY MANAGEMENT ON THE DESIGN OF COMPANY-INITIATED OPEN INNOVATION PLATFORM

Open innovation recognizes potential opportunities and advantages gained from leveraging knowledge and innovations found outside an organization‟s formal boundaries. With the intensive use of Internet-based tools, organizations are actively involved in using Open Innovation Platform (OIP) to attract external knowledge. However, developing a company-initiated OIP is a challenging task because usage of OIP depends on the voluntary participation of external users, which makes companies cannot follow the protocol of developing traditional IS. Furthermore, a company\u27s institutional properties may also impact the design company-initiated OIP. In this research, we focus on one type of organizational property, namely power boundary, and explore its impact on the design of a company-initiated OIP over time. From qualitative analysis of two versions of OIP in a single company, we develop a theoretical model depicting how the changes of power boundary of a firm influence the design of a company-initiated OIP over time. This result generates theoretical and empirical insights into the OIP design and power boundary and thus has important implications for both scholars and practitioners

Tumor-Intrinsic Sirpa Promotes Sensitivity to Checkpoint Inhibition Immunotherapy in Melanoma

Checkpoint inhibition immunotherapy has revolutionized cancer treatment, but many patients show resistance. Here we perform integrative transcriptomic and proteomic analyses on emerging immuno-oncology targets across multiple clinical cohorts of melanoma under anti-PD-1 treatment, on both bulk and single-cell levels. We reveal a surprising role of tumor-intrinsic SIRPA in enhancing antitumor immunity, in contrast to its well-established role as a major inhibitory immune modulator in macrophages. The loss of SIRPA expression is a marker of melanoma dedifferentiation, a key phenotype linked to immunotherapy efficacy. Inhibition of SIRPA in melanoma cells abrogates tumor killing by activated CD

Can Large Language Models Understand Real-World Complex Instructions?

Large language models (LLMs) can understand human instructions, showing their potential for pragmatic applications beyond traditional NLP tasks. However, they still struggle with complex instructions, which can be either complex task descriptions that require multiple tasks and constraints, or complex input that contains long context, noise, heterogeneous information and multi-turn format. Due to these features, LLMs often ignore semantic constraints from task descriptions, generate incorrect formats, violate length or sample count constraints, and be unfaithful to the input text. Existing benchmarks are insufficient to assess LLMs' ability to understand complex instructions, as they are close-ended and simple. To bridge this gap, we propose CELLO, a benchmark for evaluating LLMs' ability to follow complex instructions systematically. We design eight features for complex instructions and construct a comprehensive evaluation dataset from real-world scenarios. We also establish four criteria and develop corresponding metrics, as current ones are inadequate, biased or too strict and coarse-grained. We compare the performance of representative Chinese-oriented and English-oriented models in following complex instructions through extensive experiments. Resources of CELLO are publicly available at https://github.com/Abbey4799/CELLO

The value of synthetic MRI in detecting the brain changes and hearing impairment of children with sensorineural hearing loss

IntroductionSensorineural hearing loss (SNHL) can arise from a diverse range of congenital and acquired factors. Detecting it early is pivotal for nurturing speech, language, and cognitive development in children with SNHL. In our study, we utilized synthetic magnetic resonance imaging (SyMRI) to assess alterations in both gray and white matter within the brains of children affected by SNHL.MethodsThe study encompassed both children diagnosed with SNHL and a control group of children with normal hearing {1.5-month-olds (n = 52) and 3-month-olds (n = 78)}. Participants were categorized based on their auditory brainstem response (ABR) threshold, delineated into normal, mild, moderate, and severe subgroups.Clinical parameters were included and assessed the correlation with SNHL. Quantitative analysis of brain morphology was conducted using SyMRI scans, yielding data on brain segmentation and relaxation time.Through both univariate and multivariate analyses, independent factors predictive of SNHL were identified. The efficacy of the prediction model was evaluated using receiver operating characteristic (ROC) curves, with visualization facilitated through the utilization of a nomogram. It's important to note that due to the constraints of our research, we worked with a relatively small sample size.ResultsNeonatal hyperbilirubinemia (NH) and children with inner ear malformation (IEM) were associated with the onset of SNHL both at 1.5 and 3-month groups. At 3-month group, the moderate and severe subgroups exhibited elevated quantitative T1 values in the inferior colliculus (IC), lateral lemniscus (LL), and middle cerebellar peduncle (MCP) compared to the normal group. Additionally, WMV, WMF, MYF, and MYV were significantly reduced relative to the normal group. Additionally, SNHL-children with IEM had high T1 values in IC, and LL and reduced WMV, WMF, MYV and MYF values as compared with SNHL-children without IEM at 3-month group. LL-T1 and WMF were independent risk factors associated with SNHL. Consequently, a prediction model was devised based on LL-T1 and WMF. ROC for training set, validation set and external set were 0.865, 0.806, and 0.736, respectively.ConclusionThe integration of T1 quantitative values and brain volume segmentation offers a valuable tool for tracking brain development in children affected by SNHL and assessing the progression of the condition's severity

Understanding the Complex Adoption Behavior of Cloud Services by SMEs Based on Complexity Theory: A Fuzzy Sets Qualitative Comparative Analysis (fsQCA)

To survive in a competitive environment, small and medium enterprises (SMEs) have had to adapt to the digital environment in order to adjust to customer needs globally, particularly in the post-COVID-19 world. The advantages of cloud computing (e.g., flexibility, scalability, and low entry cost) provide opportunities for SMEs with a restricted budget and limited resources. To understand how SMEs adopt cloud computing in a complex digital environment, this study examines how antecedents combine with each other to explain the high adoption of cloud computing. From the perspectives of holism and set theory, we draw on complexity and configuration theories, present a conceptual model including seven antecedents based on the technology-organization-environment framework, and conduct an asymmetric fuzzy-set qualitative comparative analysis. Through an empirical study with 123 Chinese companies, we identify nine combinations (configurations) of determinant antecedents that lead to the high adoption of cloud computing. The results show that none of the factors are indispensable to explain a high adoption on their own; instead, they are insufficient but necessary parts of the causal combinations that explain a high adoption. This study contributes to the literature on cloud computing adoption by extending current knowledge on how antecedents combine to increase the adoption and identify specific patterns of SMEs for whom these factors are essential and greatly influence their adoption

An Empirical Analysis of Open Government Data Platform and Enterprise Innovation Performance

Many countries regard open government data (OGD) as an engine to stimulate innovation and economic value, and establish an effective OGD platform (OGDP) for public to access and utilize data. Many literature discuss OGD-based and data- driven business model, type of data application, new product development and other commercial value creation process, however, there are still lacks of reliable empirical study and lacks of in-depth analysis of impact mechanism of OGDP on enterprise innovation. This paper will use difference-in-difference method to empirically examine causal effect of OGDP on enterprise innovation performance in different areas, and reveal effect mechanism of OGDP on enterprise innovation. The empirical results not only expand the application field of open innovation theory and data-driven research, but also fill the gap about lacks of empirical study on OGDP impact. Meanwhile, the confirmation of the impact relationship and mechanism of OGDP on enterprise innovation will promote the development of OGDP and guide governments to implement OGDP in most countries

Net and configurational effects of determinants on cloud computing adoption by SMEs under cloud promotion policy using PLS-SEM and fsQCA

A deeper understanding of cloud computing is required to accelerate its adoption and leverage its cost, performance, reliability, and security. However, information about the combined effect of factors influencing cloud computing adoption using traditional statistical methods is limited. Based on a literature review of firms’ adoption of cloud computing, we identified 12 determinants to explore how antecedent factors influence cloud computing adoption by small and medium-sized enterprises (SMEs). We used symmetric and asymmetric techniques to analyze data from 203 Chinese SMEs. The partial least squares structural equation modeling (PLS-SEM) assessed the net impact of each antecedent, and the fuzzy-set qualitative comparative analysis (fsQCA) provided a supplementary analysis by highlighting the configurations of the causal conditions associated with cloud computing adoption. The PLS-SEM results show that security concerns, top management support, IT competence, competitive pressure, trading partner pressure, and provider support influence SMEs’ decisions to adopt cloud computing. Interestingly, fsQCA provides a deeper understanding of the complex causality that PLS-SEM does not capture. That is, fsQCA revealed seven configurations resulting in high-level cloud computing adoption and eight causal paths leading to the negation of cloud computing adoption. These findings indicate that several conditions with no significant influence in PLS-SEM were adequate when combined with other conditions in the configurations. The results of the complementary analysis provide theoretical and practical insights

User Acceptance of Internet of Vehicles Services: Empirical Findings of Partial Least Square Structural Equation Modeling (PLS-SEM) and Fuzzy Sets Qualitative Comparative Analysis (fsQCA)

Recently, IoV-based services and vehicles have come to the forefront as part of the growing market for the automobile industry. Since IoV-based services and vehicles were introduced, they have been expected to grow rapidly. However, contrary to optimistic expectations for future market growth, the IoV-based services and vehicles market has appeared to hit a roadblock and remains at an early market stage. Therefore, research of the determinants leading to consumers’ intention to accept and purchase IoV-based services and vehicles is significant for either academics or practitioners. Drawing upon the extended unified theory of acceptance and use of technology acceptance model (UTAUT2), the perceived risk theory, and the initial trust model, we developed an integrated conceptual model and explored what and how various determinant antecedent conditions fit together on consumer intention to accept IoV-based services and vehicles. The proposed model and hypotheses were assessed by both symmetric (partial least square structural equation modeling, PLS-SEM) and asymmetric (fsQCA) approaches using online survey datasets with 362 Chinese consumers. The findings suggest that PLS-SEM and fsQCA are complementary analytical techniques providing comparable results. PLS-SEM results indicate that performance expectancy, price value, habit, and initial trust have significant effects on behavioral intention to accept IoV services. Despite other determinants, e.g., effort expectancy, social influence, facilitating conditions, hedonic motivation, and perceived risk, have no significant effect. FsQCA results reveal twelve different configurations of determinants resulting in a high level of behavioral intention to accept IoV services, and eight causal paths equifinally leading to the negation of behavioral intention to accept IoV services. These findings suggest that several conditions that were not significant in PLS-SEM are sufficient conditions when combined with other conditions. This study enriches relevant research studies on IoV-based services acceptance and provides relevant insights and marketing suggestions for incentivizing consumers to accept the IoV-based services

Users' willingness to adopt metaverse drawing on flow theory: An empirical study using PLS-SEM and FsQCA

As a rapidly developing information technology in recent years, the metaverse has significantly transformed how we live, learn, and work. In order to accelerate the use of metaverse technology and promote users' acceptance of the metaverse, this study constructs an integrated model based on flow theory and use and satisfaction theory, to further explore the factors affecting users' acceptance of the metaverse. A total of 265 valid questionnaires were obtained through a situational questionnaire survey. Considering the limitations of a single analysis technique, we use two methods to analyze the data. Among them, the symmetric PLS-SEM method is mainly used to analyze the effects of single variables, while the asymmetric fsQCA method is used to analyze the combined effects of variables. The PLS-SEM results manifest that flow experience, perceived risk, and personal innovation directly influence users' acceptance of the metaverse, while perceived cost has no effect. Simultaneously, interactivity, presence, and social presence indirectly affect users' acceptance of the metaverse, while informativeness and enjoyment have no indirect effect. Significantly, fsQCA unveiled five configurations resulting in a high user acceptance of the metaverse, as well as six configurations leading to a negative acceptance. The complementary findings from PLS-SEM and fsQCA offer valuable insights for both theoretical understanding and practical implementation

Adoption of Mobile Government Cloud from the Perspective of Public Sector

Mobile cloud computing (MCC) has been widely used in every aspect of our society, bringing both advantages and challenges. However, the adoption of MCC technology is still at an early stage of implementation in the governments. To promote the adoption and diffusion of MCC in the government area, exploring the determinants and influence mechanisms of mobile cloud computing-based government (m-Gov cloud) adoption has become the focus in academic and industry. Based on the technology-organization-environment framework and trust theory at the organizational level, an integrated model including the determinants on the adoption of m-Gov cloud is proposed, and 93 survey samples from China are used to analyzed by partial least squares structural equation modeling (PLS-SEM). The results show that provider competence, organizational readiness, external pressure, and trust of m-Gov cloud have significant effects on m-Gov cloud adoption. Perceived benefit, perceived risk, and provider competence have significant effects on m-Gov cloud trust. The m-Gov cloud trust plays an indirect-only (full) mediation and a complementary (partial) mediation effect between perceived benefit, provider competence, and m-Gov cloud adoption, respectively, while perceived risk has no significant direct and indirect effect on m-Gov cloud adoption. The findings provide a new research perspective and practice insights to promote the implementation of solutions based on the idea of mobile cloud computing