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This study aims to develop a process model to facilitate enterprises’ co-creating value with consumers through social media.

Drawing on the concepts of internet-based co-creation and collective action theory, this study outlines a five-stage model (Interact-Engage-Propose-Act-Realize, IEPAR) of utilizing social media to co-create with consumers, enriches the model through in-depth interviews with industry experts and briefly illustrates how it can be applied in practice using a service firm case.

This study clarifies the co-creation process in the social media environment. For each of the process’s five stages, the objectives to be accomplished by the social media operator and the means to complete the objectives are illustrated.

This study illustrated the proposed model with a representative service firm. Future study may refine the model by gathering additional data from real implementations to improve its effectiveness in practice.

This study suggests how an enterprise can construct a consumer co-creation platform from a managerial perspective. The proposed model can serve as a reference that enterprises can implement to increase customer value through co-creation using social media.

Enterprises have begun to notice the power of serving as a platform for co-creating value with consumers. However, it is seldom related to literature. The proposed model of the co-creation process in the social media environment can supplement past research.

An important issue for researchers and managers of organizations is the understanding of user-perceived values of collective intelligence (UPVoCI) in online social networks (OSNs) with the purpose of helping organizations identify the values that cause internet users and members of OSNs to share information and knowledge during they participate in collective intelligence (co-intelligence) activities. However, the development of measurement instruments and predictive models and rules for predicting UPVoCI are inadequate. The paper aims to discuss these issues.

A novel measurement scale was developed to measure UPVoCI using a user-oriented research strategy that is based on qualitative and quantitative research methods. This work also identified critical indicators and constructed predictive models and rules for forecasting UPVoCI by multivariate statistical methods and data mining.

A 17-item scale of UPVoCI was developed and 17 measurement items were associated with two major dimensions, which are the user-perceived social value of co-intelligence and the user-perceived problem-solving value of co-intelligence. Ten critical indicators of UPVoCI that are important in predicting UPVoCI and 12 rules for predicting UPVoCI were identified and a refined model for predicting UPVoCI was constructed.

The results in this work allow organizations to determine the perceived value of members of OSNs and the benefits of their participating in co-intelligence activities, as a basis for adjusting user-oriented online co-intelligence and service strategies with the goal of improving collaborative innovation performance.

This work systematically developed a novel scale for measuring UPVoCI in OSNs and constructed new models and rules for predicting UPVoCI in OSNs.

Blog is a web-based social activity that has become mainstream media. The purpose of this paper is to develope a measurement instrument for assessing blog service innovation, which social media services providers and bloggers can use to promote blog functions and to ensure high quality blog services.

The study adopted both qualitative and quantitative research methods and performed four steps of scale development, including item generation and item pooling, pretest and initial purification, scale refinement, and scale validation and critical item analysis.

From a user-oriented service perspective, the major findings of the study were the 18 measurement items for blog service innovation scale (BLOG-S-INNO scale), which was derived from the innovative blog cognition of blog users. One critical factor in the BLOG-S-INNO scale was further identified to effectively predict outcomes of blog service innovation in social media services.

Management at social media services providers can apply the BLOG-S-INNO scale as a diagnostic tool to assess organizational innovation capabilities in relation to blog services, and to link their innovation strategies with the innovation experiences of bloggers improving bloggers’ affection. The findings of this study also make it possible to offer recommendations to help bloggers improve service innovation to increase the experience and preference of blog browsers.

The study used qualitative research methods to construct a pool of items for measuring blog service innovation. Furthermore, the paper conducted quantitative research methods to develop a new blog service innovation scale and analyzed the key indicators of blog service innovation.

The purpose of this study is to explore the feasibility of applying an internet‐based information system (IBIS) to facilitate business alliance activities, especially for small and median enterprises whose business performance is highly dependent on their strategic alliance partnerships.

A case study of six firms was conducted to understand current business alliance practices in Taiwan and to investigate the demand for applying IBIS systems in business alliance activities. A prototype of the IBIS system was also developed and evaluated.

We found that communication and information sharing are the most appropriate activities in business alliances for the application of an IBIS system and that the decision to adopt an IBIS system is dependent on the allied partners' support and the technological capabilities they possess. However, the lack of trust in internet security is one of the key factors that may hinder enterprises from applying the IBIS system to business alliance activities.

The prototype IBIS system was tested by two case firms. Though the test result was positive, a larger test group might be helpful in discovering ways of improving the system for practical use.

The positive result of the system evaluation supports the feasibility of applying the IBIS system to facilitate business alliance activities.

Both the IBIS system and strategic alliance are important research issues in business. However, integrative research of the two issues is seldom undertaken. This study validates the feasibility of applying the IBIS system to increase the operational efficiency of a strategic alliance.

This research intends to establish a model integrating the related theories in strategy management and competency in the HRM field, and to develop a systematic tool that can help a company quickly and precisely identify its core competency.

An internal value activity chain is obtained through analyzing customers' needs. The priority of the activities is then determined via examining external factors. The critical competency needed by the activity is abstracted from a process and operation analysis. Standardized procedures and tools for applying the Process Oriented Core Competency Identification (POCCI) model are sequentially deployed.

An empirical case was illustrated that the POCCI model not only helps a company identify the core competency that prevails over alternative approaches but also draws more recognition from the raters. The linkage between individual competency and organizational competitive advantage was solidified by the POCCI model and evidenced by an empirical case.

Misunderstanding over competency items might impede the consensus formation. A handbook of standardized procedures with unified definition is strongly suggested to facilitate the progression of competency identification.

The model, procedures and tools proposed in this paper can help most companies quickly and precisely identify their specific core competencies.

The POCCI model that emphasizes interdisciplinary integration and practical usage has never been thoroughly investigated in the previous literature and could serve as a prototype for further explorations.

To investigate the relationship between organizational characteristics and enterprise information portal (EIP) adoption, and the relationship between EIP implementation and e‐business performance.

This study uses questionnaire survey method to collect data for statistical analysis.

The result of analysis shows that: between organizations that have and those that have not adopted EIP, significant differences exist in the maturity and familiarity of information technologies, and firm size; the implementation of EIP in terms of application degree, implementation type, integration ability, and users of EIP, will significantly influence e‐business performance; the relationship between the application degree of EIP and e‐business performance will be enhanced by higher e‐business implementation; the relationship between the implementation type of EIP and e‐business performance will be intervened by higher e‐business implementation.

Corporations may create great business value by establishing EIP project.

This paper provides a model to understand the relationship between EIP implementation and e‐business performance and helps corporations evaluate EIP project.

The purpose of this paper is to understand how perceptions of fair treatment impact voluntary behaviors of information technology (IT) professionals beneficial to the achievement of organizational goals. Specifically, social exchange and psychological contract theory provide a framework to consider whether equitable treatment of employees and preservation of implied contracts are indicators of beneficial, extra‐role behaviors.

The study employs a questionnaire targeting IT professionals to test a model derived from theory and prior IT personnel literature.

IT employees reciprocate demonstrations of equity and maintenance of implied contracts by added beneficial behaviors not explicit to their job requirements. This relationship is partially mediated by their trust held in the organization.

This study considered two related but distinct social exchange concepts in the same model of IT personnel behavior that had previously been considered independently indicating the need to consider both in future models. Generalizations of the observed result are limited by the use of cross‐sectional data from a single culture.

Managers of IT personnel must design and implement procedures that guarantee equitable distribution of resources and rewards. Management honoring contacts that are merely implicit and derived internally by the IT employee is crucial in promoting beneficial behaviors that fall outside explicit job requirements.

The paper highlights how IT employees reciprocate in the technology work environment. In exchange for an organization honoring contracts that are merely perceived and providing an equitable structure, IT professionals are willing to go beyond required job descriptions to achieve goals.

Customer satisfaction in the banking industry has long been measured as a function of service quality by using a variation of the SERVQUAL instrument. The purpose of this paper is to build a broader understanding of the determinants of customer satisfaction throughout the financial services industry by incorporating the perceptions of fairness in service delivery (FAIRSERV) and outlining why and how FAIRSERV is important to customer satisfaction.

The authors conduct a cross‐sectional questionnaire survey, including samples of 420 customers from the financial services industry in Taiwan. PLS‐Graph is used to evaluate the measures of reliability as well as validities, and to test the hypotheses.

The results show that fair service not only has a significant impact on customer satisfaction, but also plays a role equivalent to service quality in determining customers’ trust and perceived value, which in turn lead to customer satisfaction.

The impact of FAIRSERV on customer satisfaction should be emphasized. Future studies examining the impact of service quality on customer satisfaction should incorporate the concept or instruments of fair service as a major contributor.

The results imply that financial institutions must carefully implement policies and practices to ensure that perceptions of fairness are propagated throughout the organization.

The paper proposes a complementary component to service quality in determining the perceived value and satisfaction. The paper emphasizes the significance of fairness on service, and provides additional insights into the impacts of FAIRSERV on customer satisfaction.

Abundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.

As the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.

The empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.

With these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.

To address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.