Analysis of Business Process Variants in Apromore

In this paper we illustrate a set of features of the Apromore process model repository for analyzing business process variants. Two types of analysis are provided: one is static and based on differences on the process control flow, the other is dynamic and based on differences in the process behavior between the variants. These features combine techniques for the management of large process model collections with those for mining process knowledge from process execution logs. The tool demonstration will be useful for researchers and practitioners working on large process model collections and process execution logs, and specifically for those with an interest in understanding, managing and consolidating business process variants both within and across organizational boundaries.

Transforming beyond self: fluidity of parent identity in family decision-making

When a household welcomes a new infant a transformation occurs whereby household routines, values and decisions change. This research explores how decision-making is influenced by fluctuating identity subjectivities. We explore longitudinally, using a family identity framework, how the transitioning between self, couple and family self-identities influences the decisions made regarding social issues, in this case infant feeding. Results indicate that decision-making during a period of transformation is not straightforward, relying on a multiplicity of identities that are constantly renegotiated and dependent on other influences. Decisions made conform to the identity-construct-of-the-moment, but are fluid and subject to change, such that pinpointing causal pathways is inappropriate. Implications for influencing the consumption of social behaviors for consumer researchers are one size does not fit all and require an in-depth understanding of the fluidity of decision-making. Consequently, social marketing strategies need to be tailored to constructed identities and flexible across time to remain influential.

Business Process Management: 13th International Conference, BPM 2015, Innsbruck, Austria, August 31 -- September 3, 2015, Proceedings

BPM 2015 was the 13th International Conference on Business Process Management. It provided a global forum for researchers to meet and exchange views over research topics and outcomes in business process management. BPM 2015 was hosted by the University of Innsbruck and took place August 31 to September 3.

Business model publicness and socialness of the social enterprise

Social enterprise is important. Yet, there has been diverse understanding of the phenomenon in the literature. This paper attempts to make sense of the social enterprise phenomenon in the literature from a two-layer framework of two-by-two matrices. The first layer juxtaposes social enterprise against other organizations (a typology of organizations) and the second layer classifies different types of social enterprises (a typology of social enterprise). This framework may provide researchers with tools to develop a clear and comprehensive definition of social enterprise. For practitioners, the ability to recognize structures of different types of social enterprises may offer them guideline to design the appropriate business model to serve their purposes.

ACE Research Vignette 047: What is the hard evidence on innovation and business productivity

This series of research vignettes is aimed at sharing current and interesting research findings from our team of international Entrepreneurship researchers. This vignette, written by Professor Beth Webster at Swinburne University of Technology, examines how innovation in small and medium size businesses affect their productivity.

Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes

This paper addresses the problem of predicting the outcome of an ongoing case of a business process based on event logs. In this setting, the outcome of a case may refer for example to the achievement of a performance objective or the fulfillment of a compliance rule upon completion of the case. Given a log consisting of traces of completed cases, given a trace of an ongoing case, and given two or more possible out- comes (e.g., a positive and a negative outcome), the paper addresses the problem of determining the most likely outcome for the case in question. Previous approaches to this problem are largely based on simple symbolic sequence classification, meaning that they extract features from traces seen as sequences of event labels, and use these features to construct a classifier for runtime prediction. In doing so, these approaches ignore the data payload associated to each event. This paper approaches the problem from a different angle by treating traces as complex symbolic sequences, that is, sequences of events each carrying a data payload. In this context, the paper outlines different feature encodings of complex symbolic sequences and compares their predictive accuracy on real-life business process event logs.

A candidate gene association study for growth performance in an improved Giant Freshwater Prawn (Macrobrachium rosenbergii) culture line

A candidate gene approach using type I single nucleotide polymorphism (SNP) markers can provide an effective method for detecting genes and gene regions that underlie phenotypic variation in adaptively significant traits. In the absence of available genomic data resources, transcriptomes were recently generated in Macrobrachium rosenbergii to identify candidate genes and markers potentially associated with growth. The characterisation of 47 candidate loci by ABI re-sequencing of four cultured and eight wild samples revealed 342 putative SNPs. Among these, 28 SNPs were selected in 23 growth-related candidate genes to genotype in 200 animals selected for improved growth performance in an experimental GFP culture line in Vietnam. The associations between SNP markers and individual growth performance were then examined. For additive and dominant effects, a total of three exonic SNPs in glycogen phosphorylase (additive), heat shock protein 90 (additive and dominant) and peroxidasin (additive), and a total of six intronic SNPs in ankyrin repeats-like protein (additive and dominant), rolling pebbles (dominant), transforming growth factor-β induced precursor (dominant), and UTP-glucose-1-phosphate uridylyltransferase 2 (dominant) genes showed significant associations with the estimated breeding values in the experimental animals (P =0.001−0.031). Individually, they explained 2.6−4.8 % of the genetic variance (R2=0.026−0.048). This is the first large set of SNP markers reported for M. rosenbergii and will be useful for confirmation of associations in other samples or culture lines as well as having applications in marker-assisted selection in future breeding programs.

Quality of information for quality of life: Healthcare big data analytics

Big data analysis in healthcare sector is still in its early stages when comparing with that of other business sectors due to numerous reasons. Accommodating the volume, velocity and variety of healthcare data Identifying platforms that examine data from multiple sources, such as clinical records, genomic data, financial systems, and administrative systems Electronic Health Record (EHR) is a key information resource for big data analysis and is also composed of varied co-created values. Successful integration and crossing of different subfields of healthcare data such as biomedical informatics and health informatics could lead to huge improvement for the end users of the health care system, i.e. the patients.

Adopting healthcare big data in Sri Lankan healthcare sector

The concept of big data has already outperformed traditional data management efforts in almost all industries. Other instances it has succeeded in obtaining promising results that provide value from large-scale integration and analysis of heterogeneous data sources for example Genomic and proteomic information. Big data analytics have become increasingly important in describing the data sets and analytical techniques in software applications that are so large and complex due to its significant advantages including better business decisions, cost reduction and delivery of new product and services [1]. In a similar context, the health community has experienced not only more complex and large data content, but also information systems that contain a large number of data sources with interrelated and interconnected data attributes. That have resulted in challenging, and highly dynamic environments leading to creation of big data with its enumerate complexities, for instant sharing of information with the expected security requirements of stakeholders. When comparing big data analysis with other sectors, the health sector is still in its early stages. Key challenges include accommodating the volume, velocity and variety of healthcare data with the current deluge of exponential growth. Given the complexity of big data, it is understood that while data storage and accessibility are technically manageable, the implementation of Information Accountability measures to healthcare big data might be a practical solution in support of information security, privacy and traceability measures. Transparency is one important measure that can demonstrate integrity which is a vital factor in the healthcare service. Clarity about performance expectations is considered to be another Information Accountability measure which is necessary to avoid data ambiguity and controversy about interpretation and finally, liability [2]. According to current studies [3] Electronic Health Records (EHR) are key information resources for big data analysis and is also composed of varied co-created values [3]. Common healthcare information originates from and is used by different actors and groups that facilitate understanding of the relationship for other data sources. Consequently, healthcare services often serve as an integrated service bundle. Although a critical requirement in healthcare services and analytics, it is difficult to find a comprehensive set of guidelines to adopt EHR to fulfil the big data analysis requirements. Therefore as a remedy, this research work focus on a systematic approach containing comprehensive guidelines with the accurate data that must be provided to apply and evaluate big data analysis until the necessary decision making requirements are fulfilled to improve quality of healthcare services. Hence, we believe that this approach would subsequently improve quality of life.

The role of emotion in product, service and business model design

Designers have become aware of the importance of creating strong emotional experiences intertwined with new tangible products for the past decade, however an increased interest from firms has emerged in developing new service and business models as complimentary forms of emotion-driven innovation. This interdisciplinary study draws from the psychological sciences – theory of emotion – and the management sciences – business model literature to introduce this new innovation agenda. The term visceral hedonic rhetoric (VHR) is defined as the properties of a product, (and in this paper service and business model extensions) that persuasively induce the pursuit of pleasure at an instinctual level of cognition. This research paper lays the foundation for VHR beyond a product setting, presenting the results from an empirical study where organizations explored the possibilities for VHR in the context of their business. The results found that firms currently believe VHR is perceived in either their product and/or services they provide. Implications suggest shifting perspective surrounding the use of VHR across a firm’s business model design in order to influence the outcomes of their product and/or service design, resulting in an overall stronger emotional connection with the customer.

Acquaintance or partner? Social economy organisations, institutional logics and regional development in Australia

The social economy as a regional development actor is gaining greater attention given its purported ability to address social and environmental problems. This growth in interest is occurring within a global environment that is calling for a more holistic understanding of development compared to traditionally economic-centric conceptions. While regional development policies and practices have long considered for-profit businesses as agents for regional growth, there is a relatively limited understanding of the role of the social economy as a development actor. The institutional environment is a large determinant of all kinds of entrepreneurial activity, and therefore understanding the relationships between the social economy and broader regional development processes is warranted. This paper moves beyond suggestions of an economic-centric focus of regional development by utilising institutional logics as a theoretical framework for understanding the role of social enterprise in regional development. A multiple case study of ten social enterprises in two regional locations in Australia suggests that social enterprise can represent competing logics to economic-centric institutional values and systems. The paper argues that dominant institutional logics can constrain or promote the inter-play between the social and the economic aspects of development, in the context of social enterprise.

Social capital or ethnic enclave location? A multilevel explanation of immigrant business growth

The rapid increase in migration into host countries and the growth of immigrant-owned business enterprises has revitalized research on ethnic business. Does micro (individual)-level social capital, or meso (group)-level location within the ethnic enclave lead to immigrant business growth? Or do you need both? We analyze quantitative data collected from 110 Chinese restaurants in Australia, a major host country. At the micro level we find that coethnic (same ethnic group) networks are critical to the growth of an immigrant entrepreneur's business, particularly in the early years. But non-coethnic (different ethnic group) social capital only has a positive impact on business growth for immigrant businesses outside the ethnic enclave. Our findings are relevant, not only to host-country policymakers, but also for future immigrant business owners and ethnic community leaders trying to better understand how to promote healthy communities and sustainable economic growth.

Use of observed within-person variation of cardiac troponin in emergency department patients for determination of biological variation and percentage and absolute reference change values

BACKGROUND Many patients presenting to the emergency department (ED) for assessment of possible acute coronary syndrome (ACS) have low cardiac troponin concentrations that change very little on repeat blood draw. It is unclear if a lack of change in cardiac troponin concentration can be used to identify acutely presenting patients at low risk of ACS. METHODS We used the hs-cTnI assay from Abbott Diagnostics, which can detect cTnI in the blood of nearly all people. We identified a population of ED patients being assessed for ACS with repeat cTnI measurement who ultimately were proven to have no acute cardiac disease at the time of presentation. We used data from the repeat sampling to calculate total within-person CV (CV(T)) and, knowing the assay analytical CV (CV(A)), we could calculate within-person biological variation (CV(i)), reference change values (RCVs), and absolute RCV delta cTnI concentrations. RESULTS We had data sets on 283 patients. Men and women had similar CV(i) values of approximately 14%, which was similar at all concentrations <40 ng/L. The biological variation was not dependent on the time interval between sample collections (t = 1.5-17 h). The absolute delta critical reference change value was similar no matter what the initial cTnI concentration was. More than 90% of subjects had a critical reference change value <5 ng/L, and 97% had values of <10 ng/L. CONCLUSIONS With this hs-cTnI assay, delta cTnI seems to be a useful tool for rapidly identifying ED patients at low risk for possible ACS.

Towards simple rules heuristics for IT business value

The business value of IT (BVIT) has been a prominent and central research topic in the IS discipline. Due to continuous and unpredictable technology and business changes, a more dynamic perspective on IT business value that includes organizational learning is required. We suggest that simple rules heuristics can address this challenge. The simple rules heuristics approach has been introduced by Eisenhardt and co-authors (Bingham & Eisenhardt, 2011; Bingham, Eisenhardt, & Furr, 2007; Eisenhardt & Sull, 2001) to better understand strategic decision making for capturing superabundant, heterogeneous, fastmoving opportunities. They argue that explicit organizational learning can translate accumulated experience into increasingly effective heuristics for strategic processes in highvelocity environments. We make three main contributions by exploring the suitability of a simple rules heuristics approach for the creation of IT business value: (1) we propose six types of simple rules heuristics for capturing IT-based opportunities in dynamic environments, including synergy heuristics as specifically relevant in an IT context, (2) we show how a simple rules heuristics approach can advance our understanding of dynamics and organizational learning for BVIT, and; (3) we introduce the strategic logic of opportunity to BVIT.

Business process management as the 'killer app' for Petri nets

Since their inception in 1962, Petri nets have been used in a wide variety of application domains. Although Petri nets are graphical and easy to understand, they have formal semantics and allow for analysis techniques ranging from model checking and structural analysis to process mining and performance analysis. Over time Petri nets emerged as a solid foundation for Business Process Management (BPM) research. The BPM discipline develops methods, techniques, and tools to support the design, enactment, management, and analysis of operational business processes. Mainstream business process modeling notations and workflow management systems are using token-based semantics borrowed from Petri nets. Moreover, state-of-the-art BPM analysis techniques are using Petri nets as an internal representation. Users of BPM methods and tools are often not aware of this. This paper aims to unveil the seminal role of Petri nets in BPM.