One of the most critical issues facing managers of high tech companies is how to retain the spirit of excitement and energy as the company grows. Interviews with managers of these companies reflected the view that the spirit of excitement and energy which is so palatable in smaller software companies, somehow dissipates in a seemingly inevitable way as the company increases in size. (The notion that "something happens" when a company gets "too big" is a common one.) The question arising from this for managers is how that spirit of excitement or high level of motivation is created in the first place, and how it can be retained. In this commentary, some preliminary findings concerning the excitement levels in different size high tech companies are presented.The findings are based on the questionnaire results of 339 people from 11 high tech software companies; 7 "small" companies with less than 50 employees, 3 "medium" sized companies with between 150-250 employees and 1 "large" company with over 2, 000 employees in total.The average response rate for all companies was 55%. For the small companies it was much higher, at 68%, but in the large company it was 51%. The average age of the sample was 28 years, average years in present company was 2.5 years, 80 were women with 259 men. Most (96%) have at least a Bachelors degree so they represent a highly educated sample.
{"title":"Maintaining the spirit of excitement in growing companies","authors":"Annamaria Garden","doi":"10.1145/54127.54130","DOIUrl":"https://doi.org/10.1145/54127.54130","url":null,"abstract":"One of the most critical issues facing managers of high tech companies is how to retain the spirit of excitement and energy as the company grows. Interviews with managers of these companies reflected the view that the spirit of excitement and energy which is so palatable in smaller software companies, somehow dissipates in a seemingly inevitable way as the company increases in size. (The notion that \"something happens\" when a company gets \"too big\" is a common one.) The question arising from this for managers is how that spirit of excitement or high level of motivation is created in the first place, and how it can be retained. In this commentary, some preliminary findings concerning the excitement levels in different size high tech companies are presented.The findings are based on the questionnaire results of 339 people from 11 high tech software companies; 7 \"small\" companies with less than 50 employees, 3 \"medium\" sized companies with between 150-250 employees and 1 \"large\" company with over 2, 000 employees in total.The average response rate for all companies was 55%. For the small companies it was much higher, at 68%, but in the large company it was 51%. The average age of the sample was 28 years, average years in present company was 2.5 years, 80 were women with 259 men. Most (96%) have at least a Bachelors degree so they represent a highly educated sample.","PeriodicalId":426630,"journal":{"name":"ACM Sigcpr Computer Personnel","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1988-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122548860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
One of the most critical issues facing managers of high tech companies is keeping their technical employees satisfied and trying to prevent a rapid staff turnover. The shortage of good quality technical people in high tech companies creates a high demand for their skills. This makes it easy for them to change employers and more important for companies to provide working conditions which are attractive enough to keep their technical staff. The managerial problem is to know what are the critical organisational and job features that affect the desire to stay in or leave a company. This commentary provides some insight into this issue. Two main areas are covered; the first is the reasons why technical employees would leave their present company; the second looks at factors influencing the length of time they expect to stay in their present company.The findings presented form part of a much larger study on the motivation of high tech software professionals. The present commentary is based on the preliminary questionnaire results of 302 people from 7 high tech software companies, 3 small companies with less than 50 employees, 3 medium size companies with between 150-200 employees and 1 large company with over 2, 000 employees in total.
{"title":"Behavioural and organisational factors involved in the turnover of high tech professionals","authors":"Annamaria Garden","doi":"10.1145/54127.54129","DOIUrl":"https://doi.org/10.1145/54127.54129","url":null,"abstract":"One of the most critical issues facing managers of high tech companies is keeping their technical employees satisfied and trying to prevent a rapid staff turnover. The shortage of good quality technical people in high tech companies creates a high demand for their skills. This makes it easy for them to change employers and more important for companies to provide working conditions which are attractive enough to keep their technical staff. The managerial problem is to know what are the critical organisational and job features that affect the desire to stay in or leave a company. This commentary provides some insight into this issue. Two main areas are covered; the first is the reasons why technical employees would leave their present company; the second looks at factors influencing the length of time they expect to stay in their present company.The findings presented form part of a much larger study on the motivation of high tech software professionals. The present commentary is based on the preliminary questionnaire results of 302 people from 7 high tech software companies, 3 small companies with less than 50 employees, 3 medium size companies with between 150-200 employees and 1 large company with over 2, 000 employees in total.","PeriodicalId":426630,"journal":{"name":"ACM Sigcpr Computer Personnel","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1988-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122389587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
How closely does the myth that programmers spend long hours working alone compare with reality? Not very much, according to the results of this study.
程序员花很长时间独自工作的神话与现实有多接近?根据这项研究的结果，并不是很多。
{"title":"How much time do software professionals spend communicating?","authors":"S. L. Sullivan","doi":"10.1145/54127.54128","DOIUrl":"https://doi.org/10.1145/54127.54128","url":null,"abstract":"How closely does the myth that programmers spend long hours working alone compare with reality? Not very much, according to the results of this study.","PeriodicalId":426630,"journal":{"name":"ACM Sigcpr Computer Personnel","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1988-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126549284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Jungian personality typology has been widely used in MIS research to investigate personal characteristics of MIS participants. The Personal Style Inventory (PSI) is a Jungian instrument in the public domain which has received little previous attention from the MIS community. Reliability, concurrent validity, and construct validity were investigated in an experiment where this instrument was directly compared with the Abbreviated Version of the Myers-Briggs Type Indicator (MBTI/AV) utilizing a sample of undergraduate business majors. Although the PSI was found to be a somewhat weaker instrument, the ability to directly incorporate it into other questionnaires makes it a useful tool for research utilizing a blind mail survey methodology.
{"title":"Validation of a Jungian instrument for MIS research","authors":"C. H. Mawhinney, A. Lederer","doi":"10.1145/43947.43948","DOIUrl":"https://doi.org/10.1145/43947.43948","url":null,"abstract":"The Jungian personality typology has been widely used in MIS research to investigate personal characteristics of MIS participants. The Personal Style Inventory (PSI) is a Jungian instrument in the public domain which has received little previous attention from the MIS community. Reliability, concurrent validity, and construct validity were investigated in an experiment where this instrument was directly compared with the Abbreviated Version of the Myers-Briggs Type Indicator (MBTI/AV) utilizing a sample of undergraduate business majors. Although the PSI was found to be a somewhat weaker instrument, the ability to directly incorporate it into other questionnaires makes it a useful tool for research utilizing a blind mail survey methodology.","PeriodicalId":426630,"journal":{"name":"ACM Sigcpr Computer Personnel","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1988-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114746159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The growing demand for software requires increasingly productive people and organizations, yet little is known about how best to select, train, organize, and manage people and organizations to produce software. Boehm's COCOMO software costing model has shown that people and organizations can have a dramatic effect on productivity and costs. Issues of project organization, education and training, professional development, career paths, personnel selection, evaluation, group dynamics, and motivation play a significant part in software productivity. As this review of 130 references to the recent research literature indicates, work is being done on these issues, however, some gaps need to be filled and methods developed to insure their effective use.
{"title":"People and organizations in software production: a review of the literature","authors":"S. Nash, S. Redwine","doi":"10.1145/43947.43949","DOIUrl":"https://doi.org/10.1145/43947.43949","url":null,"abstract":"The growing demand for software requires increasingly productive people and organizations, yet little is known about how best to select, train, organize, and manage people and organizations to produce software. Boehm's COCOMO software costing model has shown that people and organizations can have a dramatic effect on productivity and costs. Issues of project organization, education and training, professional development, career paths, personnel selection, evaluation, group dynamics, and motivation play a significant part in software productivity. As this review of 130 references to the recent research literature indicates, work is being done on these issues, however, some gaps need to be filled and methods developed to insure their effective use.","PeriodicalId":426630,"journal":{"name":"ACM Sigcpr Computer Personnel","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1988-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133884037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper discusses the architecture and design of an expert system PLEXACT, (PLEXsys in ACTion), which can be used to assist users in defining their information requirements and to analyze the consistency and integrity of the requirements. PLEXACT is different from other system analysis languages and tools because of its active participation in the requirements elicitation process and its dynamic system architecture which is adaptable to different system development settings.Current system analysis methodologies are first reviewed and evaluated. An intelligent system development tool that can remedy the disadvantages of the current approaches is described. The knowledge needed for information systems development is identified and representations of this knowledge are discussed. An object-oriented and communication-based architecture, called PLEXACT, is proposed. PLEXACT is a configurable system architecture which can support expert system building for information system development. The kernel of PLEXACT includes: (1) Development Coordinator: coordinates different expert system models to solve the system problems and guide the development process. Development Coordinator consists of four components: User Modeler, Project Controller, Problem Analyzer, and Development Knowledge Base and Data Base; (2) Information Systems Modeler: builds information system development theories, methods, and tools into expert system models to capture multiple perspectives of a system at different level of abstraction. Models can work together by specifying their coupling information to Model Creator. Information Systems Modeler consists of Model Creator, Model Instantiator, System Analyst and Designer, Model-based Learner, and Modeling Knowledge Base and Data Base. Current research in knowledge-based system development tools and some implementation issues of PLEXACT are also discussed.
本文讨论了一个专家系统PLEXACT (PLEXsys in ACTion)的体系结构和设计，该系统可以帮助用户定义他们的信息需求，并分析需求的一致性和完整性。PLEXACT与其他系统分析语言和工具的不同之处在于它积极参与需求提取过程，并且它的动态系统架构能够适应不同的系统开发环境。首先回顾和评价当前的系统分析方法。描述了一种可以弥补当前方法缺点的智能系统开发工具。识别信息系统开发所需的知识，并讨论这些知识的表示形式。提出了一种面向对象和基于通信的体系结构，称为PLEXACT。PLEXACT是一种可配置的系统架构，可以支持信息系统开发中的专家系统构建。PLEXACT的核心包括:(1)开发协调器(Development Coordinator):协调不同的专家系统模型，解决系统问题，指导开发过程。开发协调器由四个组件组成:用户建模器、项目控制器、问题分析器、开发知识库和数据库;(2)信息系统建模师(Information Systems Modeler):将信息系统开发的理论、方法和工具构建为专家系统模型，以在不同抽象层次捕捉系统的多个视角。通过向Model Creator指定它们的耦合信息，模型可以一起工作。信息系统建模器由模型创建者、模型实例化器、系统分析和设计器、基于模型的学习器、建模知识库和数据库组成。讨论了基于知识的系统开发工具的研究现状和PLEXACT的实现问题。
{"title":"PLEXACT: an architecture & design of a knowledge-based system for information systems development","authors":"Minder Chen, J. Nunamaker, B. Konsynski","doi":"10.1145/36338.36339","DOIUrl":"https://doi.org/10.1145/36338.36339","url":null,"abstract":"This paper discusses the architecture and design of an expert system PLEXACT, (PLEXsys in ACTion), which can be used to assist users in defining their information requirements and to analyze the consistency and integrity of the requirements. PLEXACT is different from other system analysis languages and tools because of its active participation in the requirements elicitation process and its dynamic system architecture which is adaptable to different system development settings.Current system analysis methodologies are first reviewed and evaluated. An intelligent system development tool that can remedy the disadvantages of the current approaches is described. The knowledge needed for information systems development is identified and representations of this knowledge are discussed. An object-oriented and communication-based architecture, called PLEXACT, is proposed. PLEXACT is a configurable system architecture which can support expert system building for information system development. The kernel of PLEXACT includes: (1) Development Coordinator: coordinates different expert system models to solve the system problems and guide the development process. Development Coordinator consists of four components: User Modeler, Project Controller, Problem Analyzer, and Development Knowledge Base and Data Base; (2) Information Systems Modeler: builds information system development theories, methods, and tools into expert system models to capture multiple perspectives of a system at different level of abstraction. Models can work together by specifying their coupling information to Model Creator. Information Systems Modeler consists of Model Creator, Model Instantiator, System Analyst and Designer, Model-based Learner, and Modeling Knowledge Base and Data Base. Current research in knowledge-based system development tools and some implementation issues of PLEXACT are also discussed.","PeriodicalId":426630,"journal":{"name":"ACM Sigcpr Computer Personnel","volume":"219 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1987-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113977895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research recognizes the lack of knowledge about career issues in data processing and proposes a way of examining the job histories of MIS managers to predict their career success. Job history is defined as a series of job experiences over the span of the person's work life. Results from research in other professional groups suggests that job histories are critical determinants of overall career success. When tested in the MIS environment, the results are expected to show that the series of jobs throughout a data processing manager's career affects the overall level of success achieved and that ability and job history predict career success in an additive rather than an interactive way. These results are discussed in terms of their implications for researchers and practitioners in the MIS area.
{"title":"Job histories as predictors of career success in management information systems","authors":"Marilyn A. Morgan","doi":"10.1145/36338.36340","DOIUrl":"https://doi.org/10.1145/36338.36340","url":null,"abstract":"This research recognizes the lack of knowledge about career issues in data processing and proposes a way of examining the job histories of MIS managers to predict their career success. Job history is defined as a series of job experiences over the span of the person's work life. Results from research in other professional groups suggests that job histories are critical determinants of overall career success. When tested in the MIS environment, the results are expected to show that the series of jobs throughout a data processing manager's career affects the overall level of success achieved and that ability and job history predict career success in an additive rather than an interactive way. These results are discussed in terms of their implications for researchers and practitioners in the MIS area.","PeriodicalId":426630,"journal":{"name":"ACM Sigcpr Computer Personnel","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1987-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131141560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper explores the concept of inexpert systems which accumulate ignorance and misunderstanding. Capitalizing on the work of N. Worthy and N. Nowen (1985), the idea of an intelligence-avoiding system is applied to a number of common hard problems, formost among which is the development of accounting systems by trained COBOL programmers.
{"title":"An inexpert system for system development","authors":"E.D. Ought","doi":"10.1145/25051.25054","DOIUrl":"https://doi.org/10.1145/25051.25054","url":null,"abstract":"This paper explores the concept of inexpert systems which accumulate ignorance and misunderstanding. Capitalizing on the work of N. Worthy and N. Nowen (1985), the idea of an intelligence-avoiding system is applied to a number of common hard problems, formost among which is the development of accounting systems by trained COBOL programmers.","PeriodicalId":426630,"journal":{"name":"ACM Sigcpr Computer Personnel","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1987-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125254590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years there has been a tremendous increase in the development of Expert Systems (ESs) in organizations. This increased development is straining the already limited supply of qualified ES developers. These ES developers have come to be know as Knowledge Engineers (KEs), and their job as Knowledge Engineering. The process of Knowledge Engineering is divided into two tasks: Knowledge Acquisition (KA) and ES construction. KA has been defined as "The process of extracting, structuring, and organizing Knowledge from several sources, usually human experts, so it can be used in a program." (Waterman, 1986; p. 392) This process of KA has been identified as the "bottleneck" that currently constrains the development of ESs.This paper summarizes what is known about the KA process in an effort to identify what the key factors are that influence the success of the KA phase of the Knowledge Engineering process. Due to the similarities that exist between ESs and traditional systems development, the literature that pertains to traditional Information Requirements Determination and to Systems Analysts will be utilized to guide this exploration. Case study reports of actual ES development projects and the practitioner literature from this highly applied field will also be referenced. A model of the knowledge Engineering process has been developed and will be used to help determine and discuss the key factors that influence the KA process.Five key factors have been identified and will be discussed in detail. These factors are: the attributes of the participants in the ES development process; elicitation techniques utilized in the process; the development of external representations; representation selection problems; and the verification of the ES by the Domain Expert who participates in its continued development via the user-system interface. These factors are presented, key issues identified, and research questions suggested for each area.It is hoped that the analysis of the key factors in KA will lead to the identification of the skills and techniques necessary to successfully perform the KA process. Once these skills have been identified, training programs can be developed to help reduce the shortage of qualified KEs and, ultimately, facilitate the increased development of ESs in organizations.
{"title":"Key factors in knowledge acquisition","authors":"J. Fellers","doi":"10.1145/25051.25053","DOIUrl":"https://doi.org/10.1145/25051.25053","url":null,"abstract":"In recent years there has been a tremendous increase in the development of Expert Systems (ESs) in organizations. This increased development is straining the already limited supply of qualified ES developers. These ES developers have come to be know as Knowledge Engineers (KEs), and their job as Knowledge Engineering. The process of Knowledge Engineering is divided into two tasks: Knowledge Acquisition (KA) and ES construction. KA has been defined as \"The process of extracting, structuring, and organizing Knowledge from several sources, usually human experts, so it can be used in a program.\" (Waterman, 1986; p. 392) This process of KA has been identified as the \"bottleneck\" that currently constrains the development of ESs.This paper summarizes what is known about the KA process in an effort to identify what the key factors are that influence the success of the KA phase of the Knowledge Engineering process. Due to the similarities that exist between ESs and traditional systems development, the literature that pertains to traditional Information Requirements Determination and to Systems Analysts will be utilized to guide this exploration. Case study reports of actual ES development projects and the practitioner literature from this highly applied field will also be referenced. A model of the knowledge Engineering process has been developed and will be used to help determine and discuss the key factors that influence the KA process.Five key factors have been identified and will be discussed in detail. These factors are: the attributes of the participants in the ES development process; elicitation techniques utilized in the process; the development of external representations; representation selection problems; and the verification of the ES by the Domain Expert who participates in its continued development via the user-system interface. These factors are presented, key issues identified, and research questions suggested for each area.It is hoped that the analysis of the key factors in KA will lead to the identification of the skills and techniques necessary to successfully perform the KA process. Once these skills have been identified, training programs can be developed to help reduce the shortage of qualified KEs and, ultimately, facilitate the increased development of ESs in organizations.","PeriodicalId":426630,"journal":{"name":"ACM Sigcpr Computer Personnel","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1987-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132475653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The impact of the rapidly growing use of personal computers on data processing (DP) departments is analyzed using data from a national survey of DP managers. The implications of the findings are discussed in the context of policy and managing personal computers and DP departments.
{"title":"Personal computers and data processing departments: interfaces, impact and implications","authors":"S. Chandra","doi":"10.1145/25051.25052","DOIUrl":"https://doi.org/10.1145/25051.25052","url":null,"abstract":"The impact of the rapidly growing use of personal computers on data processing (DP) departments is analyzed using data from a national survey of DP managers. The implications of the findings are discussed in the context of policy and managing personal computers and DP departments.","PeriodicalId":426630,"journal":{"name":"ACM Sigcpr Computer Personnel","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1987-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132091774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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