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Despite considerable research highlighting the significance of relational governance in inter-organizational relationships, few have involved the connections between relational governance and innovation ecosystems. This study explores this issue to discover the influential mechanisms of relational governance in innovation ecosystem co-evolution. Building an evolutionary game model, we embody trust and reciprocity (two dominance of relational governance) into co-evolutionary relationships of an innovation ecosystem composed of focal firms, research institutes, customers, and governments, and discuss how relational governance affects innovation strategies of actors. Moreover, the impacts of benefit distribution are also examined. We reveal that (1) focal firms and governments prefer cooperative strategies; (2) reciprocity and trust foster cooperation; increasing benefit distribution drives all actors to collaborate except research institutes; (3) governments finitely encourage cooperation through regulation; and (4) the power of relational governance is restricted due to the below-the-average strategies of customers and research institutes and the neutralizing effects of benefits. Our findings offer a complementary and novel framework for relational governance and extend a deeper understanding of innovation ecosystem studies.

PurposeIt is very complicated to keep the business processes under control since the business processes change rapidly and thus flexibility is an important attribute which businesses should possess in order to respond to rapid changes in the business environment. The purpose of this paper is to divide the companies' customers into different priority groups to be served according to their payment history and feedback in order to increase the companies' performance and profit and save the time of customers within high priority class which may lead to increase their satisfaction.Design/methodology/approachThe paper proposes a requirements engineering‐based approach for business process modelling to assist businesses maintain their performance in such an environment. The paper proposes a new numerical model to improve customer satisfaction in relation to delivery or service waiting time according to their priority class, particularly customers in the high priority class. A call centre at the selected telecommunication company is used as a case study to validate the proposed numerical model.FindingsThe customers' satisfaction in the area of the time to be served according to their priority group classes can be improved using the proposed model.Research limitations/implicationsThe paper has some limitations as the paper only tested the numerical model on one real business organisation and one business process service.Originality/valueTo date, no research has been conducted in the area of separating customers into different priority groups to provide services according to their required delivery time, payment history and feedback which will increase the company's performance and profit and provide prompt service to customers in the high priority class which in turn, will increase their satisfaction.

A very promising approach to discovering services and context information in ad-hoc networks is based on the use of Attenuated Bloom filters. In this paper we analyze the impact of changes in the connectivity of an ad-hoc network on this approach. We evaluate the performance of the discovery protocol while nodes appear, disappear, and move, through analytical and simulative analysis. The analytical results are shown to be accurate when node density is high. We show that an almost linear relation exists between the density of the network and the number of update messages to be exchanged. Further, in case of nodes moving, the number of messages exchanged does not increase with the speed of movement.

Purpose
– The purpose of this paper is to investigate the impact of dividing the companies' customers into different priority groups to be served according to their payment history and feedback on the business performance areas: service quality (SQ), business process time (BPT), business process cost (BPC) and customer satisfaction (CS).


Design/methodology/approach
– A new numerical model to improve CS service waiting time according to their priority queue class, particularly customers in the high priority queue class will be proposed. To validate the proposed numerical model, a call centre at the selected telecommunication company is used as a case study. An empirical analysis based on data from 130 business and IT managers is used to evaluate and investigate if it has an impact on business process (BP) performance. Bivariate correlation analysis was used to test four hypotheses. The results were subjected to reliability and validity analyses.


Findings
– The results show that managing customer power is positively associated with BP performance. Furthermore, the results indicate that by using the proposed numerical model, the customers' satisfaction can be improved.


Research limitations/implications
– The paper has some limitations as it is only tested on one real business organizations and one BP service. Furthermore, the study was conducted only in telecommunication companies. The questionnaires were answered only by IT and business managers in Saudi Arabian telecommunication companies. Therefore, the results cannot be used as a standard and might not be directly transferrable to any sized firm and any other country. Moreover, the results may be affected by common method variance as the authors collected the data from participants by using the same survey and at the same time.


Social implications
– The results of this research provide important evidence for business managers and business analysts that managing customers power can enhance the business performance.


Originality/value
– To date, there is only a few researches have been conducted in the area of separating customers into different priority groups to provide services according to their required delivery time, payment history and feedback. However, most of them have not been evaluated in the business environment. Moreover, no previous study has attempted to empirically demonstrate the relationship between creating a BP model which can manage customer power, SQ, BPT, BPC and CS.