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The purpose of this study is to investigate the need for an evolving business model that accounts for social, as well as business-related risks/rewards considerations, that is, for owner-managers with lifestyle as opposed to growth-oriented objectives.

The methodological approach undertaken involved in-depth interviews with the firm’s owner-managers, supplemental interviews with members of staff, observation, plus examining documents from secondary sources. Data gathering involved a period of three years to account for an evolving business model over time.

The findings from an instrumental case study demonstrate the need to adapt a firm’s business model in the light of changing circumstances. Additionally, in the context of owner-managers with lifestyle as opposed to growth-oriented objectives, to account for social in addition to business-related considerations in planning activities.

The originality of the study is to incorporate a longitudinal case study in to the entrepreneurial marketing literature. Specifically, this offers implications for business support organisations that advise prospective owner-managers; that is, in respect of the need for effective planning in formulating an evolving and enduring business model. Implications also highlight in a business sense, that turnaround of a poorly performing firm may be possible, for example, to overcome initial inadequate marketing planning. However, for owner-managers with lifestyle as opposed to growth-oriented objectives, a combination of both business and social factors need consideration to maintain a work/life balance. A venture that relies on personal and business relationships may not be viable if the partners cannot work together, no matter if the venture is performing well.

Generalization ability of genetic programming (GP) models relies highly on the choice of parameter settings chosen and the fitness function used. The purpose of this paper is to conduct critical survey followed by quantitative analysis to determine the appropriate parameter settings and fitness function responsible for evolving the GP models with higher generalization ability.

For having a better understanding about the parameter settings, the present work examines the notion, applications, abilities and the issues of GP in the modelling of machining processes. A gamut of model selection criteria have been used in fitness functions of GP, but, the choice of an appropriate one is unclear. In this work, GP is applied to model the turning process to study the effect of fitness functions on its performance.

The results show that the fitness function, structural risk minimization (SRM) gives better generalization ability of the models than those of other fitness functions.

This study is of its first kind where two main contributions are listed addressing the need of evolving GP models with higher generalization ability. First is the survey study conducted to determine the parameter settings and second, the quantitative analysis for unearthing the best fitness function.

The purpose of this paper is to propose a complex network model which can study the specified objects in a complex system within which the number of agents is fixed while the interactions and the outside environments are evolving with time.

The complex network model is analyzed by the master equation method and the rigorous four‐step statistical test is applied to test whether the degree distribution in the real world fits power law or not.

By theoretical analysis, the vertex degrees of the model follow power law distribution p(k)∼k⁻² which is different from that of the Barabási‐Albert model. By empirical research, the result shows that the citations of papers published in 2001 on the small‐world networks follow a power law distribution which is tested by the statistical test.

The small sample and short evolving time may cause some deviation from the theoretical expectation.

This evolving complex network model with fixed number of vertices and the statistical test process for power‐law will have a great significance for the theoretical and empirical study on complex networks.

This paper presents a new model of evolving complex networks which can be used to analyze the specified objects in a dynamic system and a quantitative method for power law test.

This study aims to investigate the question involving what factors affect angel investors’ decision-making in funding new start-ups with specific reference to their evolving business models. Without funding and access to networks and experience, certain entrepreneurs will not get their business model through the start-up phase.

Data arise from 20 semi-structured interviews with angel investors in New Zealand plus supplementary interviews with business incubator managers and textual data.

The findings suggest a degree of causation-based decision-making, in that certain linear thinking was evident. The implication is that, without the ability of the entrepreneurs to convince the investors about key criteria in their decision-making, investment is unlikely. Nevertheless, a degree of effectuation-based decision-making was also evident, the implication being investors having to balance risk/reward decisions in the context of their own perceptions of affordable losses against an evolving business model. However, angel investors may take on co-investment, including from overseas, that takes decision-making away from management teams.

The study draws attention to the need to consider entrepreneurial ecosystems in angel investor’s decision-making and especially those with a small domestic market that may require management teams to look for scalability internationally. Furthermore, an effectuation lens contributes to knowledge in respect of predictive and control criteria, in particular, assessing risks and rewards against affordable losses involving an evolving business model.

The purpose of this paper is to develop a method to model entire structures on a large scale, at the same time taking into account localized non-linear phenomena of the discrete microstructure of cohesive-frictional materials.

Finite element (FEM) based continuum methods are generally considered appropriate as long as solutions are smooth. However, when discontinuities like cracks and fragmentation appear and evolve, application of models that take into account (evolving) microstructures may be advantageous. One popular model to simulate behavior of cohesive-frictional materials is the discrete element method (DEM). However, even if the microscale is close to the macroscale, DEMs are computationally expensive and can only be applied to relatively small specimen sizes and time intervals. Hence, a method is desirable that combines efficiency of FEM with accuracy of DEM by adaptively switching from the continuous to the discrete model where necessary.

An existing method which allows smooth transition between discrete and continuous models is the quasicontinuum method, developed in the field of atomistic simulations. It is taken as a starting point and its concepts are extended to applications in structural mechanics in this paper. The kinematics in the method presented herein is obtained from FEM whereas DEM yields the constitutive behavior. With respect to the constitutive law, three levels of resolution – continuous, intermediate and discrete – are introduced.

The overall concept combines model adaptation with adaptive mesh refinement with the aim to obtain a most efficient and accurate solution.

This research aims to aid product development managers to estimate the expected cost associated with the development of cost-intensive physical prototypes considering transitions associated with pertinent states of quality of the prototype and corresponding decision policies under the Markovian setting.

The authors evolve two types of optimization-based mathematical models under both deterministic and randomized policies. Under the deterministic policy, the product development managers take certain decisions such as “Do nothing,” “Overhaul,” or “Replace” corresponding to different quality states of prototype such as “Good as new,” “Functional with minor deterioration,” “Functional with major deterioration” and “Non-functional.” Under the randomized policy, the product development managers ascertain the probability distribution associated with these decisions corresponding to various states of quality. In both types of mathematical models, i.e. related to deterministic and randomized settings, minimization of the expected cost of the prototype remains the objective function.

Employing an illustrative case of the operator cabin from the construction equipment domain, the authors ascertain that randomized policy provides us with better decision interventions such that the expected cost of the prototype remains lower than that associated with the deterministic policy. The authors also ascertain the steady-state probabilities associated with a prototype remaining in a particular quality state. These findings have implications for product development budget, time to market, product quality, etc.

The authors’ work contributes toward the development of optimization-driven mathematical models that can encapsulate the nuances related to the uncertainty of transition of quality states of a prototype, decision policies at each quality state of the prototype while considering such facets for all constituent subsystems of the prototype. As opposed to a typical prescriptive study, their study captures the inherent uncertainties associated with states of quality in the context of prototype testing, etc.

How do entrepreneurs use simple rules to build their business models? Based on an inductive study of three Chinese Internet and technology firms, the authors find that business models emerge from simple rules that entrepreneurs learn from their experience. Simple rules also guide entrepreneurs to actualize and exploit opportunities in the marketplace, and they can help business models evolve through market feedback, especially in internationalization. This paper aims to delve into the black box of entrepreneurial decision-making and offer a better depiction of the business model development process in uncertain and fast-changing environments and thus provide guidance for future entrepreneurs.

Following the case method (Eisenhardt, 1989; Yin, 2003), the authors first present a thick description of characteristics of three companies and the dynamics of their business models. They then code these descriptions into first-order measures. Finally, they aggregate these measures into abstract constructs. They constantly compare the theoretical constructs and the emerging theory with the existing literature on business models.

The authors generate three key insights from the findings: business models emerge from simple rules learned from entrepreneurs’ experience, simple rules help entrepreneurs exploit and actualize opportunities in the marketplace and simple rules help businesses expand and evolve business models through market feedback, especially in internationalization.

This paper falls into the intersection of strategy and entrepreneurship – an emerging new field of strategic entrepreneurship – and is concerned with how businesses create and sustain a competitive advantage while simultaneously identifying and exploiting new opportunities. The authors bring people – the individual decision-makers for businesses – back in strategy research and depict a more realistic picture of the behavior of successful entrepreneurs and their business model development process.

Optimization of resources related to man, money, manpower and those related to organization is critical in context of after-sales supply chains. Many times, organizational objectives in terms of resource optimization and providing superior customer experience might be conflicting, however.

One such instance is when customers expect near 100% service level in which case the organizational costs to meet such high service level goes up significantly. To this end, in this research a novel bi-objective optimization model has been evolved for a typical after-sales service supply chain network constituted of the manufacturer, the retailer and the customer. The first objective function pertains to maximization of the manufacturer's and the retailer's profit. The second objective function is related to the minimization of tardiness of order fulfilment (by the retailer) for the customer.

Employing a small problem instance, the authors generate a number of findings related to service level and information asymmetry. In particular, the authors observe that achieving best possible manufacturer-retailer profit and at the same time 100% service level is a mathematical impossibility. Furthermore, reducing information asymmetry between the customer and the retailer (as opposed to reducing information asymmetry between the retailer and the manufacturer) actually yields higher profits for the manufacturer-retailer pair.

This research describes the mathematical structure of a three-tier after-sales supply chain wherein information quality and service level requirements are key constraints. Furthermore, the study evolves the bi-objective optimization model as a formulation that can drive the operational decisions of manufacturers and retailers who are part of such after-sales service supply chains.

The purpose of this paper is to clarify how a competitive company develops its own method to create innovation by utilizing imitation and learning.

The paper examines the case of Fast Retailing from the viewpoint of imitation strategy.

Fast Retailing constantly explores and imports business ideas, evolves its business model through trial and error and finally creates innovation.

The paper emphasizes the importance of imitation strategy that flexibly accepts and extends business ideas through learning, creates new values by evolving a business model and combines them with corporate identity and brand.

The case study of Fast Retailing suggests that the successful imitation is enabled by flexible corporate culture and redefining its corporate identity and brand through the process of evolving its business model.

The community college model is evolving in the Socialist Republic of Viet Nam. During June 2009, 11 of the 12 existing higher education institutions with “community college” in their official name were examined utilizing a qualitative multiple-case study approach. Data were collected in the field from June 1 to 23, 2009, while making visits to each of the 11 colleges, the Ministry of Education and Training (MOET), and the Vietnam Association of Community Colleges (VACC). Five data collection techniques were utilized to examine the bounded system: (a) semi-structured interviews, (b) survey of the college rectors, VACC informant and MOET informant, (c) participant observation with field notes, (d) document review, and (e) photographs taken during each site visit. After the field study stage, e-mail communication with the study informants between August 2009 and March 2010 clarified questions and developed a deeper level of understanding of the Vietnamese community college model. Results of the study (Epperson, 2010) indicate that although legislation does not exist to permanently establish the higher education institutions named community colleges, a community college model does exist and is in a state of evolution. The model can be defined by a set of core characteristics which emerged from the data. Five themes were particularly prominent: (a) public higher education institutions with community ownership at the provincial or city level, in conjunction with MOET oversight for academic matters; (b) multidisciplinary programs designed to meet the unique needs of the community; (c) multi-level certificates and diplomas conferred up through the college level (three year) of higher education and articulation agreements with universities enable students to earn a university bachelor's degree; (d) domestic and international partnerships are actively sought to develop social capital; (e) scientific and technological research based on community needs as required in the 2005 Education Law.