Purpose – The purpose of this paper is to formulate the manufacturing strategy in a developing country with particular reference to Nigeria in sub-Saharan African country. Design/methodology/approach – Using survey methodology and the partial least squares – structural equation modeling technique, The authors find that in addition to the four basic environmental factors – business cost, labor availability, competitive hostility and environmental dynamism, both government policies and the adopted manufacturing practices have significant effects on the manufacturing strategic priorities. Findings – Among other findings, the environmental factors of government policies and the type of manufacturing practices adopted have significant effects on manufacturing strategy. Research limitations/implications – Further studies should explicitly look into the effect of the adopted manufacturing strategy on company performance. Possibly, a case study research might be considered to establish this relationship. Practical implications – As manufacturing firms continue to experience greater competition, especially from China, and given the technological advancement in manufacturing, the business environment facing Nigerian manufacturing companies is likely to become more dynamic, complex, diverse and even hostile. Under such a turbulent climate, the configuration and effective deployment of manufacturing strategies is imperative to achieving superior business performance. Social implications – A more effective strategy will make companies more competitive in the market place, thus creating employment in a sector that has witnessed declining growth in employment. Originality/value – The linkage between environmental factors and manufacturing strategy has been studied widely. However, this study reveals the role of government policies and manufacturing practices in formulating manufacturing strategy in a developing country context.

No abstract

No abstract

Two special considerations are discussed which frequently arise when conducting statistical analyses for the assessment and evaluation of engineering education programs. The first concerns the multiple comparison problem and Type I errors, specifically when should the significance level be adjusted and which adjustment procedure is most appropriate? Three scenarios are presented to illustrate three different applications of the classical Bonferroni procedure, one of the most extensively used adjustment procedures. A scenario is also presented for when an adjustment is not necessary. The second consideration is: when evaluating a predictive model should a tree diagram be used as an alternative to a classification table? For example, how does one assess a model's predictions when certain of its "recommendations" are not followed? For this type of case, a classification table may yield incomplete information. The use of a tree diagram to present more information on model performance is discussed. I. INTRODUCTIONEngineering education has witnessed an explosion of noteworthy research during the past decade spurred on by two converging initiatives. The first is the current evolution of curricula in response to the changes in the engineering accreditation criteria by the Accreditation Board for Engineering and Technology (ABET). The second is the National Science Foundation's (NSF) bold move to fund the Engineering Education Coalitions in concert with a series of other NSF educational initiatives that have generated a number of the ideas currently being implemented in response to the new ABET criteria. In addition, the former has highlighted the critical need for assessment within the engineering education community, while the latter has triggered a call for wide-scale dissemination and implementation of many of these NSF sponsored educational innovations. Indeed, research in engineering education is becoming a "respected" endeavor as more educators and administrators are using such results to make educational and policy improvements.That said, as educators turned to this type of research they must also become more cognizant of the statistical ramifications of their results. This paper presents two special considerations that frequently arise when statistical analyses are employed as part of the assessment and evaluation of engineering education programs and courses. It has been our experience that researchers often overlook these considerations, and consequently may draw inaccurate conclusions at worst or faulty analyses at best. Although these two issues are separate in nature, they both deal with reaching potentially erroneous conclusions with regards to Type I and II errors.The first is associated with the multiple comparison problem, specifically adjusting the Type I error level (i.e., the probability of rejecting the null hypothesis, when, in fact, it is true) and determining which adjustment procedure is appropriate for a particular multiple comparison situation. That is, when should the significance leve...

No abstract