Search results

This paper aims to investigate the role of predictive models in the learning and decision-making processes of strategic management. The rapid advancement of digitalisation has contributed to increasing the complexity of the worldwide economy and led to various new competitive dynamics.

To achieve this purpose, a literature review has been carried out and a predictive model based on Watson, an IBM supercomputer, is presented as a qualitative process model.

Specific insights derived from a review of the literature highlight organisations' need to modify their decision- and strategy-making processes, which are increasing in speed and frequency, thus also leading to the formulation of emergent and trigger event strategies based on the identification of conditions that require the revision of all or part of the firm's strategy. Predictive models, acting as filters, transform data into informative knowledge that decision-makers can interpret based on individual domain knowledge.

From a theoretical point of view, this paper contributes to the field of digital transformation by proposing the economics of complexity as a paradigm through which to observe and study the issue of predictive models in strategic management. Additionally, the authors analyse the phenomenon from a cognitive perspective, defining the new learning dynamics of digital transformation and the social learning cycle triggered by big data and predictive models. From a managerial and policy-making point of view, this suggests the need to re-shape traditional education contents and dynamics and foster skills that are multi-disciplinary, multi-domain, multi-empathic, multi-interaction and multi-communication between people and things.

Predictive analytics is the science of decision-making that eliminates guesswork out of the decision-making process and applies proven scientific procedures to find right solutions. Predictive analytics provides ideas on the occurrences of future downtimes and rejections thereby aids in taking preventive actions before abnormalities occur. Considering these advantages, predictive analytics is adopted in various diverse fields such as health care, finance, education, marketing, automotive, etc. Predictive analytics tools can be used to predict various behaviors and patterns, thereby saving the time and money of its users. Many open-source predictive analysis tools namely R, scikit-learn, Konstanz Information Miner (KNIME), Orange, RapidMiner, Waikato Environment for Knowledge Analysis (WEKA), etc. are freely available for the users. This chapter aims to reveal the best accurate tools and techniques for the classification task that aid in decision-making. Our experimental results show that no specific tool provides the best results in all scenarios; rather it depends upon the datasets and the classifier.

This paper aims to introduce a new thought process and associated predictive tool to assist designers of infrastructure for unfamiliar rural societies in understanding the specific non-engineering influences that can lead to greater effectiveness of engineered infrastructure.

Using sociological principles, a simple checklist tool has been designed to assess specific societal conditions that may influence intervention outcomes. The tool, when weighted by regional predispositions, allows the designer to incorporate five non-engineering influences into technical design of engineered infrastructure.

Early deployment of the tool indicates that the predictive process helps to adapt technical designs to societal contexts. It also enhances consultant understanding of the client’s values and needs to achieve a collaborative technical solution.

Long-term outcomes have not been assessed, so additional time is needed to confirm the value of context in design performance. Further evaluations will refine the technical guideline process as well.

Merging sociological understanding with technical design allows engineers to assimilate client values and indigenous beliefs into an infrastructure, preventing rejection due to incompatibility with local context.

The concept of contextual engineering, which melds technical approach with societal influences, is the original contribution of the author, as is the predictive tool.

Outlines the main findings of a study of a process in a major organization (involved in the servicing of capital equipment) which seeks to predict customer demand for service calls and match these against engineering resources. The process is called the engineer availability process (EAP) and has been examined in two geographical areas of the host company. Among the findings is that, no matter what means are used to predict demand, there is always a need for a flexible revision of supply. This revision can be met between formal and informal clustering of engineers. The former delivers a consistent output of jobs per day across a clustered team whereas with informal clustering, the distribution across teams is less consistent and puts greater pressure on individual teams to match supply and demand.

The purpose of this paper is to explore the prospect of using neurophenomenology to understand, design and test phygital consumer experiences. It aims to clarify interpretivist approaches to consumer neuroscience, wherein theoretical models of individual phenomenology can be combined with modern neuroimaging techniques to detect and interpret the first-person accounts of phygital experiences.

The argument is conceptual in nature, building its position through synthesizing insights from phenomenology, phygital marketing, theoretical neuroscience and other related fields.

Ultimately, the paper presents the argument that interpretivist neuroscience in general, and neurophenomenology specifically, provides a valuable new perspective on phygital marketing experiences. In particular, we argue that the approach to studying first-personal experiences within the phygital domain can be significantly refined by adopting this perspective.

One of the primary goals of this paper is to stimulate a novel approach to interpretivist phygital research, and in doing so, provide a foundation by which the impact of phygital interventions can be empirically tested through neuroscience, and through which future research into this topic can be developed. As such, the success of such an approach is yet untested.

Phygital marketing is distinguished by its focus on the quality of subjective first-personal consumer experiences, but few papers to date have explored how neuroscience can be used as a tool for exploring these inner landscapes. This paper addresses this lacuna by providing a novel perspective on “interpretivist neuroscience” and proposes ways that current neuroscientific models can be used as a practical methodology for addressing these questions.

The business operations of today's enterprises are heavily influenced by numerous of internal and external business events. With the Event Driven Architecture and particularly the Complex Event Processing (CEP), the technology required for identifying complex correlations in these large amounts of event data right after its appearance has already emerged. The resulting gain in operational transparency builds the foundation for (near) real-time reactions. This motivated extensive research activities especially in the field of Business Process Management (BPM), which essentially coined the term Event-Driven BPM (EDBPM). Now, several years after the advent of this new concept, the purpose of this paper is to shed light to the question: where are we now on our way towards a sophisticated adoption of the CEP technology within BPM?

The research methodology of this paper is a structured literature analysis. It basically follows the procedure proposed by vom Brocke et al. (2009). This verified five-step process – entitled “Reconstructing the giant” – allowed a rigorous study. As a result, various research clusters were derived, whose state-of-the-art exposed existing research gaps within EDBPM.

First of all, the paper provides a concise conceptual basis on different application possibilities of EDBPM. Afterwards, it synthesizes current research into six clusters and highlights most significant work within them. Finally, a research agenda is proposed to tackle existing research gaps to pave the way towards fully realizing the potentials of the paradigm.

So far, a comparable study of the current state-of-the-art within EDBPM is non-existent. The findings of this paper, e.g. the proposed research agenda, help scholars to focus their research efforts on specific aspects that need to be considered in order to advance the adoption of the CEP technology within BPM.

This study aims to understand trust meanings, determinants and manifestations in supply chains (SCs) operating in an emerging market context. It also aims to improve our knowledge about the role of trust and the mechanisms by which it operates in establishing and maintaining relationships between firms in SCs.

This study adopts an explanatory approach. In‐depth interviews with 30 key informants were conducted. Informants were chief executive officers or marketing managers in firms operating in different economic sectors. Firms varied in size and ranged from small businesses to large companies.

The study results showed that trust could evolve through four building processes: calculative‐based process, predictive‐based process, intention‐based process, and identification‐based process and that trust meanings and determinants vary with the trust form. Moreover, the study revealed that determinants related to the trustor also have an influence on the trust form and its evolving process. On the other hand, it was found that risk taking, preference for the partner, fewer formalized controls, offers of assistance and psychological security are the main manifestations of trust. This supports the point of view of the twofold facets of trust: perceived trustworthiness and trusting behaviors.

Because of the complexity of the trust phenomenon, and the research approach adopted, the findings may not be generally applicable. Further quantitative studies are needed to test the proposed framework.

Given the globalisation of markets and the widespread increase in international collaborative partnerships, the study sheds some light on how Tunisian managers conceive trust, which factors they perceive most important to develop trust, and how they behave to signal their trust towards a partner. These insights can be very helpful for foreign investors who are willing to invest in this emerging market and to implement a supply chain management approach with Tunisian partners.

This paper fulfils an identified need, not only to better understand the phenomenon of trust in SCs, but also to carry out more studies in situ. Indeed, the rapid development of the global economy has made it more important than ever before for managers from different cultures to understand how their business partners conceive and manage the interpersonal aspects of business relationships.

The nature and amount of data that public organizations have to monitor to counteract corruption lead to a phenomenon called “KPI overload”, consisting of the business analyst feeling overwhelmed by the amount of information and resulting in the absence of appropriate control. The purpose of this study is to develop a solution based on Artificial Intelligence technology to avoid data overloading and, at the same time, under-controlling in business process monitoring.

The authors adopted a design science research approach. The authors started by observing a specific problem in a real context (a healthcare organization); then conceptualized, designed and implemented a solution to the problem with the goal to develop knowledge that can be used to design solutions for similar problems. The proposed solution for business process monitoring integrates databases and self-service business intelligence for outlier detection and artificial intelligence for classification analysis.

The authors found the solution powerful to solve problems related to KPI overload in process monitoring. In the specific case study, the authors found that the combination of Business Intelligence and Artificial Intelligence can provide a significant contribution to the detection of fraud, corruption and/or policy misalignment in public organizations.

The authors provide a big-data-based solution to the problem of data overload in business process monitoring that does not sacrifice any monitored Key Performance Indicators and that also reduces the workload of the business analyst. The authors also developed and implemented this automated solution in a context where data sensitivity and privacy are critical issues.

The purpose of this paper is to propose an empirical investigation targeting companies operating in Northern Italy and focusing on some main topics of maintenance.

Through a combined application of factor analysis and cluster analysis, the paper first identified the main approaches implemented by Italian companies with the purpose of preventing failures and reducing their consequences. Then, the paper grouped companies on the basis of such approaches, and derived the profile of each cluster, in terms of both the company's characteristics and some key maintenance elements, including the maintenance policy or the use of advanced techniques to make maintenance interventions more effective.

Results of this study suggest that the approach to maintenance management could be related to the implementation of specific maintenance policies, as well as to other topics, such as the company characteristics, the use of advanced techniques for enhancing maintenance effectiveness and the criteria used for selecting the maintenance policy.

The sample of companies investigated in this study is quite limited, and prevented the possibility of providing statistical evidence of all outcomes.

The outcomes of this study can be useful for practitioners to understand the positioning of companies towards the implementation of maintenance policies, as a function of their approach to maintenance management.

This study complements previous empirical works on maintenance by exploring the relationships between maintenance management, maintenance policies, the application of different tools and techniques for maintenance management, and the criteria adopted for the maintenance policy selection.

The purpose of this paper is to develop an extensive economic production quantity (EPQ) model on the basis of previous research. Considering an imperfect three‐state production process, this paper makes contributions to an integrated model combining conceptions of quality loss and design of control chart based on EPQ model. The objective is to minimize the total production cost with the determination of EPQ and design parameters of control chart subjected to quality loss and other process costs.

In this paper, imperfect process is defined as a three‐state process, and the quality cost corresponding to each state contributes to the eventual total expected cost formulation. Control chart is used to monitor the shift from the target value within whole process and its control limits are set to be related to the quality cost.

The proposed integrated model conforms more closely to the real situation of production process considering the process shift as a random variable.

Numerical computation and sensitivity analysis through a case study are presented to demonstrate the applications of the model.

Few research efforts investigate an integrated model considering EPQ, control chart and quality loss simultaneously. In particular, compared with the former researches, the process shift, due to which the quality cost incurs, is considered as a random variable in this paper.