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The purpose of the paper is to present an approach to detect and isolate the aircraft sensor and control surface/actuator failures affecting the mean of the Kalman filter innovation sequence.

The extended Kalman filter (EKF) is developed for nonlinear flight dynamic estimation of an F‐16 fighter and the effects of the sensor and control surface/actuator failures in the innovation sequence of the designed EKF are investigated. A robust Kalman filter (RKF) is very useful to isolate the control surface/actuator failures and sensor failures. The technique for control surface detection and identification is applied to an unstable multi‐input multi‐output model of a nonlinear AFTI/F‐16 fighter. The fighter is stabilized by means of a linear quadratic optimal controller. The control gain brings all the eigenvalues that are outside the unit circle, inside the unit circle. It also keeps the mechanical limits on the deflections of control surfaces. The fighter has nine state variables and six control inputs.

In the simulations, the longitudinal and lateral dynamics of an F‐16 aircraft dynamic model are considered, and the sensor and control surface/actuator failures are detected and isolated.

A real‐time detection of sensor and control surface/actuator failures affecting the mean of the innovation process applied to the linearized F‐16 fighter flight dynamic is examined and an effective approach to isolate the sensor and control surface/actuator failures is proposed. The nonlinear F‐16 model is linearized. Failures affecting the covariance of the innovation sequence is not considered in the paper.

An approach has been proposed to detect and isolate the aircraft sensor and control surface/actuator failures occurred in the aircraft control system. An extended Kalman filter has been developed for the nonlinear flight dynamic estimation of an F‐16 fighter. Failures in the sensors and control surfaces/actuators affect the characteristics of the innovation sequence of the EKF. The failures that affect the mean of the innovation sequence have been considered. When the EKF is used, the decision statistics changes regardless the fault is in the sensors or in the control surfaces/actuators, while a RKF is used, it is easy to distinguish the sensor and control surface/actuator faults.

Cumulative capability models are dominating frameworks explaining how manufacturing organizations gain their performance capabilities, such as quality, delivery, flexibility and cost. When innovation capabilities are excluded from the framework, the models are incapable of explaining how companies sustain substantive capabilities in a changing environment. Responding to this gap, the purpose of this paper is to propose and test a “sand cone” cumulative capability model that includes the innovation competitive performance alongside the competitive performance of quality, delivery flexibility and cost.

Two competing cumulative models were proposed. The extended cumulative capability model hypothesizes the development of innovation in sequence with other competitive performance dimensions. The affected with innovation cumulative model hypothesizes innovation performance as a predecessor of other performance dimensions. The models were tested using a multimethod approach on a representative sample of 500 manufacturing companies. An analysis of correlations among competitive performance, frequencies of plants following prescribed sequences, fit statistics of covariance-based structural equation modeling and analysis of strength and statistical significance of path coefficients enabled us to select a model that best represents the collected data.

The findings reveal that innovation competitive performance operates as a predecessor of quality, delivery, flexibility and cost and is developed in relation to these performance dimensions. The modified model also provides a theoretical explanation of how innovation performance helps to sustain reliable production systems that can perform consistently over time within a tolerable range of quality, delivery, flexibility and cost performance.

The results are significant for practitioners, especially for companies that are operating in volatile environments because the results provide insight on how to develop innovation competitive performance in relation to quality, delivery, flexibility and cost performance.

This study extends the cumulative capability models with innovation competitive performance. It advances the contingency approach on cumulative capability models.

This paper aims to examine how distinct sequences of organizational learning types (experiential and vicarious) underpin processes of exploratory versus exploitative innovation.

Data collection consists of 16 interviews conducted with senior personnel at two firms in the biopharmaceutical sector, with sequences of organizational learning types derived from the associated innovation projects. These sequences and their differential emphases on experiential or vicarious learning are used to construct a conceptual model. Propositions describe the structural differentiation and integration mechanisms useful to foster organizational ambidexterity.

Technological brokering emerges as a key means by which organizations can reconcile the learning sequences underlying exploration and exploitation. For exploration, a structure incorporating cross-industry technology brokerage during the initiation and development phases of innovation is posited. For exploitation, a structure harnessing intra-industry technology brokerage during the development phase of innovation is suggested. Integration of these projects can be accomplished through cross-unit interfaces incorporating both types of brokerage roles, with emphasis on their use during implementation.

This paper considers the ways in which organizations focus on separate types of organizational learning at different stages of the innovation process. Insights are provided into how firms mobilize internal and external knowledge to advance these projects independently, as well as to link these efforts and thereby facilitate ambidexterity.

This chapter outlines a method for developing simulation code from case-based data using narrative sequence analysis. This analytical method allows researchers to systematically specify the ‘real-world’ behaviours and causal mechanisms that describe the research problem and translate this mechanism into simulation code. An illustrative example of the process used for code development from case-based data is detailed using a well-documented case of photovoltaic innovation. Narrative sequence analysis is used to analyse case data. Micro-sequences are identified and simplified. Each micro-sequence is presented first in pseudo-code and then in simulation code. This chapter demonstrates the coding process using Netlogo code. Narrative sequence analysis provides a rigorous and systematic approach to identifying the underlying mechanisms to be described when building simulation models. This analytical technique also provides necessary and sufficient information to write simulation code. This chapter addresses a current gap in the methodology literature by including case data within agent-based model building processes. It benefits B2B marketing researchers by outlining guiding processes and principles in the use of case-based data to build simulation models.

A survey by Gary Hamel's company (Strategos) identified that over 80 percent of senior managers agreed that innovation creates a strong source of competitive advantage, and 90 percent indicated that innovation is highly valued. Yet these same companies rated themselves poorly at innovation. This paper sets out to consider behaviors and traits that will help organizations to successfully innovate.

Recent articles have attempted to use the concept of scientific DNA as a metaphor to describe characteristics of an organization. Many of these are descriptive and refer to basic core activities that managers need to concern themselves with. This article presents an analogy of DNA for the business perspective. There are certain behaviors and traits – call them innovation genes – that are foundational to innovation. It is believed that the sequence presented is this paper represents the basic building blocks for organizational innovation.

The paper finds that the innovation DNA sequence includes employee centric traits of knowledge management, cluster management, value management, and alignment. The context shaping innovation includes employee constituency and empowerment. The outcomes include strategic architecture to support innovation, innovation mapping of strategic initiatives, and value creation. There are competitive and positioning advantages of innovation DNA that promote a sustainable competitive advantage.

Embedding innovation DNA into the organization's fabric elevates organizations to being innovative in everything they do ‐ from knowledge management to value creation, and execution. Its application is universal as it elevates the least common denominator respecting how employees think and act; behaviors which lend life to innovation. As a result, the innovation imperative will only be as good as the organization's lowest common denominator in this respect.

Companies are increasingly leveraging digital technologies toward innovation strategies that deliver novel features to customers sequentially through successive new product generations (i.e., successive innovation). Extant literature examining successive innovation is both limited and fragmented across marketing and management literatures. Our goal is to synthesize literature on concepts related to successive innovation (such as versioning and upgrades) to identify the core dimensions of successive innovation and provide a cohesive framework to guide future research in this domain.

Given the equivocality in understanding the conceptual domain of successive innovation, we review and synthesize literature across three disciplinary domains: marketing, management, and information and decision sciences. Based on the emerging patterns from the literature review, we develop a conceptual framework of successive innovation with the aim of moving the discussion toward greater theoretical clarity.

Based on the literature review and synthesis, we identify three core-dimensions that define successive innovation and compare these across digital and physical product realms: coexistence, embeddedness, and adoption controllability.

Our proposed conceptual dimensions of successive innovation, and discussion of differences across physical and digital product domains, offer important directions for future research and a common vocabulary.

As physical and digital successive innovations can differ in coexistence, embeddedness, and adoption controllability, firms need to consider relevant barriers to adoption of successive product generations and select appropriate strategies to promote and communicate successive innovation. Our proposed successive innovation conceptual dimensions help managers comprehend the complexity of arranging such innovation in business and consumer segments.

Our contribution to the emerging literature on successive innovation is threefold. First, by conducting a comprehensive literature review, we integrate insights from different fields of inquiry (i.e., marketing, management, and information and decision sciences). Second, based on the synthesis of the literature, we offer a conceptual framework of successive innovation, which aims to move the discussion toward greater theoretical clarity. Third, based on our review and conceptual framework, we discuss a set of future research directions to guide academic research efforts.

The purpose of this paper is to conduct the particle swarm optimization (PSO)‐assisted adaptive Kalman filter (AKF) for global positioning systems (GPS) navigation processing. Performance evaluation for the PSO‐assisted Kalman filter (KF) as compared to the conventional KF is provided.

The position‐velocity also knows as constant velocity process model can be applied to the GPS KF adequately when navigating a vehicle with constant speed. However, when an abrupt acceleration motion occurs, the filtering solution becomes very poor or even diverges. To avoid the limitation of the KF, the PSO can be incorporated into the filtering mechanism as dynamic model corrector. The PSO is utilized as the noise‐adaptive mechanism to tune the covariance matrix of process noise and overcome the deficiency of KF. In other words, PSO‐assisted KF approach is employed for tuning the covariance of the GPS KF so as to reduce the estimation error during substantial maneuvering.

The paper provides an alternative approach for designing an AKF and provides an example in the application to GPS.

The proposed scheme enhances the improvement in estimation accuracy. Application of the PSO to the GPS navigation filter design is discussed. The method takes advantage of both the adaptation capability and the robustness of numerical stability.

The PSO are employed for assisting the AKF. The use of optimization such as PSO for AKF has seldom been seen in the open literature.

Based on theoretical input from punctuated equilibrium theory and the exploration vs exploitation paradigm, a set of innovation drivers and propositions is developed. The purpose of this paper is to present a starting point for a more comprehensive investigation of companies’ innovation trajectories towards balance.

The paper builds on a thorough analysis of the literature and suggests the notion of “strategic inflection points” (SIPs). These points are supposed to mark the transgression between innovation types as reflected in respective performance enhancements.

The propositions developed in this paper serve as a basis for further understanding and for subsequent empirical research.

Managerial implications relate to the understanding that companies need to balance innovation types in order to increase performance.

This paper discusses a variety of innovation types and suggests the notion of SIPs.

“Unless a company accepts R & D as an essential element in the business, it is better not to spend money on it, for the money will be wasted.”

A novel facet of the construction industry's (CI) digital transformation relates to the rise of smart contracts, and the contribution of blockchain technology in this domain appears to be nascent but rapidly gaining traction. Although the benefits of digitalisation for technologically less enthusiastic CI are irrefutable, the adoption of smart contracts has been found to be low pertaining to industry professionals' behavioural factors stimulated by technological perception. The challenge undertook by this study, therefore, is to develop a knowledge framework for blockchain-enabled smart contract adoption in the CI.

From a methodological perspective, this study employed a qualitative approach that involved semi-structured interviews with ten (10) highly experienced CI practitioners involved in digital innovations for data collection. Directed content analysis was performed using NVivo 12 software, which enabled the creation of preliminary open codes. Subsequently, these open codes were grouped into similar categories to develop axial codes. Finally, the study presented final themes along with their corresponding descriptions.

Notably, research findings expanded the current body of knowledge on perceived attributes and their measurement items to determine the perception of innovation adoption in CI, where a total of nine (9) perceived attributes were associated with thirty-two (32) measurement items.

The measurement items were seen as having an extensive impact on the CI professionals' decision to adopt blockchain-enabled smart contracts. With ensuing implications, this study represents one of the first to present a knowledge framework exclusively customised for blockchain-enabled smart contracts, laying the groundwork for effective technological adoption by CI professionals.