Conceptualising and Modelling E-Recruitment Process for Enterprises through a Problem Oriented Approach

Internet-led labour market has become so competitive it is forcing many organisations from different sectors to embrace e-recruitment. However, realising the value of the e-recruitment from a Requirements Engineering (RE) analysis perspective is challenging. This research was motivated by the results of a failed e-recruitment project conducted in military domain which was used as a case study. After reviewing the various challenges faced in that project through a number of related research domains, this research focused on two major problems: (1) the difficulty of scoping, representing, and systematically transforming recruitment problem knowledge towards e-recruitment solution specification; and (2) the difficulty of documenting e-recruitment best practices for reuse purposes in an enterprise recruitment environment. In this paper, a Problem-Oriented Conceptual Model (POCM) with a complementary Ontology for Recruitment Problem Definition (Onto-RPD) is proposed to contextualise the various recruitment problem viewpoints from an enterprise perspective, and to elaborate those problem viewpoints towards a comprehensive recruitment problem definition. POCM and Onto-RPD are developed incrementally using action-research conducted on three real case studies: (1) Secureland Army Enlistment; (2) British Army Regular Enlistment; and (3) UK Undergraduate Universities and Colleges Admissions Service (UCAS). They are later evaluated in a focus group study against a set of criteria. The study shows that POCM and Onto-RPD provide a strong foundation for representing and understanding the e-recruitment problems from different perspectives.

Conceptualising and Modelling e-Recruitment Process for Enterprises through a Problem Oriented Approach

Internet-led labour market has become so competitive forcing many organisations from different sectors to embrace e-recruitment. However, realising the value of the e-recruitment from a Requirements Engineering (RE) analysis perspective is challenging. This research is motivated by the results of a failed e-recruitment project conducted in military domain which is used as a case study in this research. After reviewing the various challenges faced in that project through a number of related research domains, this research focuses on two major problems which are the (1) difficulty of scoping, representing, and systematically transforming recruitment problem knowledge towards e-recruitment solution specification; and (2) the difficulty of documenting e-recruitment best practices for reuse purposes in an enterprise recruitment environment. In this paper, a Problem-Oriented Conceptual Model (POCM) with a complementary Ontology for Recruitment Problem Definition (Onto-RPD) is proposed to contextualise the various recruitment problem viewpoints from an enterprise perspective and to elaborate those problem viewpoints towards a comprehensive recruitment problem definition. The POCM and Onto-RPD are developed incrementally using action-research conducted on three real case studies: (1) Secureland Army Enlistment, (2) British Army Regular Enlistment, and (3) UK Undergraduate Universities and Colleges Admissions Service (UCAS). They are later evaluated in a focus group study against a set of criteria. The study showed that the POCM and Onto-RPD provide a strong foundation for representing and understanding the e-recruitment problems from different perspectives.

Creating multi-vendor solutions: the resources and capabilities required

Purpose – The purpose of this paper is to investigate the resources and capabilities required by manufacturers to develop and deliver multi-vendor solutions. Design/methodology/approach – A multi-case design comprising six UK-based manufacturers: two from each of the aerospace/defence, information technology and telecommunications sectors. Findings – Manufacturers can be characterized by their propensity to include products from other vendors in their solutions; single vendor solution providers (SVSPs) focus on solutions comprising their own products, while multi-vendor solution providers (MVSPs) fully embrace products from other manufacturers. Three capabilities were identified which distinguish MVSPs from SVSPs given the complexity of multi-vendor solutions (expertise specifying the solution, engineers trained in implementing/supporting the solution, partnerships with component suppliers of the solution). These capabilities are underpinned by both technical capability and impartiality in solution specification. Research limitations/implications – MVSPs need to be impartial when specifying customer solutions. They should be guided by the best interests of the customer rather than the interests of the product-based SBUs. Achieving impartiality can conflict with some manufacturers' product heritage. The research has focused on three sectors; further research is needed to test whether the findings are applicable beyond these sectors. Practical implications – Solutions are a valuable approach in creating market differentiation, although not all manufacturers will possess the resources/capabilities to be successful. Originality/value – A continuum of solution providers is proposed; SVSPs at one extreme and MVSPs at the other. The operant resource-based capability “impartiality” was identified as being particularly important to MVSPs in creating value for customers.

A Visual Language for Design Pattern Modeling and Instantiation

In this chapter we describe the Design pattern modeling language, a notation supporting the specification of Design pattern solutions and their instantiation into UML design models. DPML uses a simple set of visual abstractions and readily lends itself to tool support. DPML Design pattern solution specifications are used to construct visual, formal specifications of Design patterns. DPML instantiation diagrams are used to link a Design pattern solution specification to instances of a UML model, indicating the roles played by different UML elements in the generic Design pattern solution. A prototype tool is described, together with an evaluation of the language and tool.

ENHANCING THE SOFTWARE LIFE CYCLE OF KNOWLEDGE-BASED SYSTEMS USING A TASK-BASED SPECIFICATION METHODOLOGY

Several methodologies have been developed to enhance the software life cycle of knowledge-based systems by emphasizing on the use of both prototypes and specifications. However, these methodologies focus on the development phase of knowledge-based systems. The roles of prototypes and specifications in the maintenance phase has not been fully explored. Because a suitable problem specification for a knowledge-based system is often difficult to acquire, validating changes to non-executable solution specification during the maintenance phase can be a problem. To address this, we propose an alternative paradigm in which the prototype complements the specification throughout the life cycle. The traceability between them is facilitated by organizing both types of artifacts using a common functional decomposition structure. Based on our task-based specification methodology (TBSM), we have also developed a knowledge engineering tool (called TAME) to facilitate the acquisition and the organization of the specification and the prototype. The proposed methodology and the tool together can thus enhance the verification, validation, and the maintenance of knowledge-based systems through their life cycles.

The Effect of Problem Complexity, Computer Power, and Feedback Aids on the Accuracy and Completness of Computer Programs

An essential step in the development of a computer program is the specification of the solution logic to be implemented by the program. The importance of accurate problem solution specification is further stressed by researchers in programming language and artificial intelligence who suggest that in a relatively short time computer programs will be synthesized automatically and that the job of the user will be only to specify the problem solution. The research reported in this paper involved an investigation of the ability of computer users to specify problem solutions in the form of example solutions. This ability was evaluated as a function of the user's background and experience, the complexity of the available processor (i.e., degree of generalization of the inputs), and the available feedback aids. Further, the ability of experienced programmers to implement the problem solution in FORTRAN IV logic code was investigated. The research reported here is part of study in multi-level communications, both abstract and concrete (i.e., general statements and examples), that may provide a method for effective two-way communication between users and computers. Two participant groups (programmers and bookkeepers/accountants) working with three levels of problem complexity and three levels of processor complexity were used. The experimental task employed in this study required specification of a logic for solution of a Navy task force problem. This task involved choosing ships from a ship list which identified the ship type, the transiting time (the time required for the ship to get from its present position to the desired site), and stationing time (the number of days the ship can remain on station with available provisions). In addition to this specification of ship combinations, the participants had to specify by the example solution the range of transiting and stationing times required. It was found that both programmers' and bookkeepers/accountants' scores decreased with increasing levels of problem and processor complexity, but the scores for the bookkeepers/accountants were significantly less than those of the programmers. In a regression analysis it was found that the degree of computer generalization of the user input (processor complexity) explained more variance than did problem complexity. Further for those experiment conditions where little computer generalization of user input was provided, performance was significantly lower than for other experiment conditions. This result suggests that computer generalization of user inputs is an important factor to accurate specification of problem solutions. Finally, the results show that participant strategy in generating solutions was the most significant factor affecting performance. The strategy measures, which indicated the degree of systematization of the participants, explained 58 percent of the score variance.