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Job analysis is the common basis for designing a training course or programme, preparing performance tests, writing position (job) descriptions, identifying performance appraisal criteria, and job restructuring. Its other applications in human resource development include career counselling and wage and salary administration. Job analysis answers the questions of what tasks, performed in what manner, make up a job. Outputs of this analytical study include: (a) a list of the job tasks; (b) details of how each task is performed; (c) statements describing the responsibility, job knowledge, mental application, and dexterity, as well as accuracy required; and (d) a list of the equipment, materials, and supplies used to perform the job. Various techniques for conducting a job analysis have been used. Each has its advantages and disadvantages. As a result, different techniques or combinations of techniques are appropriate to different situations. The combined on‐site observation and individual interview techniques are recommended for industrial, trade, craft, clerical, and technical jobs because they generate the most thorough and probably the most valid information. A job analysis schedule is used to report the job information obtained through observations and individual interviews. The schedule provides a framework of 12 items in which to arrange and describe important job analysis information. These 12 items are organised into four sections. Section one consists of items one through four. These items identify the job within the establishment in which it occurs. The second section presents item five, the work performed. It provides a thorough and complete description of the tasks of the job. The Work Performed section describes what the job incumbent does, how it is done, and why it is done. Section three presents items six through nine. These are the requirements placed on the job incumbent for successful performance. It is a detailed interpretation of the basic minimum (a) responsibility, (b) job knowledge, (c) mental application, and (d) dexterity and accuracy required of the job incumbent. The fourth section includes three items which provide background information on the job. These items are: (a) equipment, materials and supplies; (b) definitions of terms; and (c) general comments. Appendix A is a glossary of terms associated with job analysis. It is provided to facilitate more exacting communication. A job analysis schedule for a complex and a relatively simple job are included in Appendices B and C. These examples illustrate how important job analysis information is arranged and described. Appendix D provides a list of action verbs which are helpful when describing the manipulative tasks of a job.

The supervisor job analysis process was initially developed in a large US fast food chain for establishing selection criteria. As supervisors started using this tool, their interest and enthusiasm led the firm to expand it beyond its original purpose to include the other components of performance. The process is discussed and its use by line management in developing job evaluation dimensions, selection criteria, performance appraisal and identifying training needs is described. Finally, the implementation process and effectiveness of the programme is assessed.

Through a survey of 200 employees working in five of the thirty establishments analysed in previous research about the microeconomic effects of reducing the working time (Cahier 25), the consequences on employees of such a reduction can be assessed; and relevant attitudes and aspirations better known.

Outlines the purpose of job analysis and the way in which it is carried out. Identifies the main steps within the process and discusses each in turn. Considers job evaluation, looking at ranking and point systems as well as factor comparison. Covers the management of compensation as an integral part of any effective management system. Provides a basic approach to the subject involving identifying needs, determining objectives, developing and implementing programmes and revising procedures.

With changes in world and national economies and the continuing trend over the last 20 years towards industrial automation, the nature of the workforce required has changed. Take the current industrial pattern of automation as an example. The demand for production operators drops while the demand for maintenance and repair workers increases. Unfortunately, however, most of the industrial workforce lacks the skills required to become effective members of this new workforce. This means that the changes in manufacturing processes and technologies must take place more slowly than would be the case if a ready supply of the right kind of labour existed. Not surprisingly, this means that we need to place emphasis on the training and development of workers to give them the appropriate knowledge and skills.

The purpose of this paper is to assess the use of two innovative job analysis techniques. First, a graphic‐based approach is used to collect job classification data. Second, the results are presented in a graphical representation to decision makers. In addition, the paper examines two concepts, similarity and relatedness, often confused by subject matter experts (SMEs) and decision makers in the context of job classification.

A case study approach was used. Focus groups of SMEs used a graphic‐based tool to group jobs into occupational clusters based on the concepts of similarity and relatedness. To effectively communicate the results to organizational decision makers a graphic presentation technique was used.

The paper found that SMEs were highly engaged in the graphical approach. Decision makers were also intrigued by the graphical presentation. In addition, the paper found confusion between the concepts of similarity and relatedness throughout the process. This confusion had important implications for the grouping of jobs into occupational clusters.

The graphic presentation of results highlighted issues around which the agency had been previously struggling. The approach allowed decision makers to examine and understand meaningful data and reach consensus on complex, multi‐faceted issues. The results also showed that people often confuse the similarity and relatedness of jobs, and that this confusion should be taken into consideration when communicating with non‐job analysts.

Job analysis and classification has changed little over the past several decades. This paper applies innovative ideas to job classification which are equally applicable to job analysis offering interesting avenues for future research and practice.

The purpose of this research is to apply principles from the field of industrial and organizational psychology on organizational analysis and job analysis to better identify successful candidates for employment in an Information Commons.

Review of professional research, research from both library literature and industrial and organizational psychology, review of professional association surveys.

Library administration must invest time to articulate mission, value, and goals for the Information Commons before undertaking the organizational analysis. From the organizational analysis will flow the job analysis, job descriptions and hiring protocols.

Core concepts of organizational analysis and job analysis from the field of industrial and organizational psychology are pertinent and useful to library leaders in planning, implementing and staffing new service models, such as Information Commons.

The purpose of this empirical study is to identify the job tasks where decisions regarding social responsibility are likely to occur and assess the potential connections between social responsibility and professionalism.

A job study conducted by the Universal Public Procurement Certification Council (UPPCC) of 2,593 practitioners is used for data collection. Factor analysis is applied to a set of 75 procurement job tasks to determine the relationship between practitioners’ performance and management of job tasks and social responsibility variables.

The results suggest that there are specific job tasks performed and managed in both public and private sector procurement that share a unique relationship with social responsibility variables.

The manuscript advances the research on professionalism in procurement and administration through empirically testing job tasks performed and managed by practitioners and identifying relationships between job tasks according to a professional orientation toward social responsibility.

The study shows that specific job tasks are performed and managed in procurement and administration with a social responsibility consideration.

The technical nature of job tasks found to be related to social responsibility suggests a paradoxical view of the politics-administration dichotomy, and the notion that neutral tasks of both the public and private sectors are not void of a social function.

One attribute of professionalism in the literature, social responsibility, is operationalized through actual performance and management of job tasks by practitioners.

This paper aims to investigate emerging trends in data analytics and knowledge management (KM) job market by using the knowledge, skills and abilities (KSA) framework. The findings from the study provide insights into curriculum development and academic program design.

This study traced and retrieved job ads on LinkedIn to understand how data analytics and KM interplay in terms of job functions, knowledge, skills and abilities required for jobs, as well as career progression. Conducting content analysis using text analytics and multiple correspondence analysis, this paper extends the framework of KSA proposed by Cegielski and Jones‐Farmer to the field of data analytics and KM.

Using content analysis, the study analyzes the requisite KSA that connect analytics to KM from the job demand perspective. While Kruskal–Wallis tests assist in examining the relationships between different types of KSA and company’s characteristics, multiple correspondence analysis (MCA) aids in reducing dimensions and representing the KSA data points in two-dimensional space to identify potential associations between levels of categorical variables. The results from the Kruskal–Wallis tests indicate a significant relationship between job experience levels and KSA. The MCA diagrams illustrate key distinctions between hard and soft skills in data across different experience levels.

The practical implications of the study are two-fold. First, the extended KSA framework can guide KM professionals with their career planning toward data analytics. Second, the findings can inform academic institutions with regard to broadening and refining their data analytics or KM curricula.

This paper is one of the first studies to investigate the connection between data analytics and KM from the job demand perspective. It contributes to the ongoing discussion and provides insights into curriculum development and academic program design.

Reports on a case study on the design and development of two training courses for employees of gas and energy distribution companies in The Netherlands who were faced with internal and external changes. Discusses the conceptual framework of job analysis, developing a specific set of data collection techniques, particularly structured questionnaires and interviews. Concludes that each individual company should research and develop its own training course. The course should be designed to meet the company′s own peculiar requirements, there should be more professional co‐operation between industry and academe in research and training; and finally that training courses should be more widely based on the concept of job profiles, extending even to entire sectors of the economy.