Management Science

Computer systems cannot improve organizational performance if they aren't used. Unfortunately, resistance to end-user systems by managers and professionals is a widespread problem. To better predict, explain, and increase user acceptance, we need to better understand why people accept or reject computers. This research addresses the ability to predict peoples' computer acceptance from a measure of their intentions, and the ability to explain their intentions in terms of their attitudes, subjective norms, perceived usefulness, perceived ease of use, and related variables. In a longitudinal study of 107 users, intentions to use a specific system, measured after a one-hour introduction to the system, were correlated 0.35 with system use 14 weeks later. The intention-usage correlation was 0.63 at the end of this time period. Perceived usefulness strongly influenced peoples' intentions, explaining more than half of the variance in intentions at the end of 14 weeks. Perceived ease of use had a small but significant effect on intentions as well, although this effect subsided over time. Attitudes only partially mediated the effects of these beliefs on intentions. Subjective norms had no effect on intentions. These results suggest the possibility of simple but powerful models of the determinants of user acceptance, with practical value for evaluating systems and guiding managerial interventions aimed at reducing the problem of underutilized computer technology.

The present research develops and tests a theoretical extension of the Technology Acceptance Model (TAM) that explains perceived usefulness and usage intentions in terms of social influence and cognitive instrumental processes. The extended model, referred to as TAM2, was tested using longitudinal data collected regarding four different systems at four organizations (N = 156), two involving voluntary usage and two involving mandatory usage. Model constructs were measured at three points in time at each organization: preimplementation, one month postimplementation, and three months postimplementation. The extended model was strongly supported for all four organizations at all three points of measurement, accounting for 40%–60% of the variance in usefulness perceptions and 34%–52% of the variance in usage intentions. Both social influence processes (subjective norm, voluntariness, and image) and cognitive instrumental processes (job relevance, output quality, result demonstrability, and perceived ease of use) significantly influenced user acceptance. These findings advance theory and contribute to the foundation for future research aimed at improving our understanding of user adoption behavior.

Trong bối cảnh quản lý, lập trình toán học thường được sử dụng để đánh giá một tập hợp các phương án hành động thay thế có thể, nhằm lựa chọn một phương án tốt nhất. Trong khả năng này, lập trình toán học phục vụ như một công cụ hỗ trợ lập kế hoạch quản lý. Phân tích Bao hàm Dữ liệu (DEA) đảo ngược vai trò này và sử dụng lập trình toán học để đánh giá ex post facto hiệu quả tương đối của các thành tựu quản lý, dù chúng được lập kế hoạch hoặc thực hiện như thế nào. Lập trình toán học do đó được mở rộng để sử dụng như một công cụ kiểm soát và đánh giá các thành tựu quá khứ cũng như công cụ hỗ trợ lập kế hoạch cho hoạt động tương lai. Hình thức tỷ lệ CCR được giới thiệu bởi Charnes, Cooper và Rhodes, như một phần của cách tiếp cận Phân tích Bao hàm Dữ liệu, bao hàm cả sự không hiệu quả về kỹ thuật và quy mô thông qua giá trị tối ưu của hình thức tỷ lệ, được thu được trực tiếp từ dữ liệu mà không cần yêu cầu định trước các trọng số và/hoặc phân định rõ ràng các dạng chức năng giả định của mối quan hệ giữa đầu vào và đầu ra. Một sự tách biệt giữa hiệu quả kỹ thuật và hiệu quả quy mô được thực hiện bởi các phương pháp phát triển trong bài báo này mà không làm thay đổi các điều kiện sử dụng DEA trực tiếp trên dữ liệu quan sát. Sự không hiệu quả về kỹ thuật được xác định bởi sự thất bại trong việc đạt được các mức đầu ra tốt nhất có thể và/hoặc việc sử dụng quá nhiều lượng đầu vào. Các phương pháp để xác định và điều chỉnh phạm vi của những sự không hiệu quả này, được cung cấp trong các công trình trước, được minh họa. Trong bài báo hiện tại, một biến mới được giới thiệu, cho phép xác định liệu các hoạt động được thực hiện trong các vùng có lợi suất tăng, không đổi hay giảm (trong các tình huống đa đầu vào và đa đầu ra). Các kết quả được thảo luận và liên hệ không chỉ với kinh tế học cổ điển (đầu ra đơn) mà còn với các phiên bản kinh tế học hiện đại hơn được xác định với “lý thuyết thị trường có thể tranh đấu.”

Bài báo này trả lời câu hỏi, “Tại sao các tổ chức lại xử lý thông tin?” Sự không chắc chắn và sự mơ hồ được định nghĩa là hai yếu tố ảnh hưởng đến việc xử lý thông tin trong các tổ chức. Cấu trúc tổ chức và các hệ thống nội bộ xác định cả lượng và sự phong phú của thông tin được cung cấp cho các nhà quản lý. Các mô hình được đề xuất cho thấy cách mà các tổ chức có thể được thiết kế để đáp ứng nhu cầu thông tin về công nghệ, quan hệ liên phòng ban và môi trường. Một hàm ý đối với các nhà quản lý là một vấn đề chính là thiếu sự rõ ràng, chứ không phải thiếu thông tin. Các mô hình chỉ ra cách mà các tổ chức có thể được thiết kế để cung cấp các cơ chế thông tin nhằm giảm thiểu sự không chắc chắn và giải quyết sự mơ hồ.

By decision-making in a fuzzy environment is meant a decision process in which the goals and/or the constraints, but not necessarily the system under control, are fuzzy in nature. This means that the goals and/or the constraints constitute classes of alternatives whose boundaries are not sharply defined.

An example of a fuzzy constraint is: “The cost of A should not be substantially higher than α,” where α is a specified constant. Similarly, an example of a fuzzy goal is: “x should be in the vicinity of x₀ ,” where x₀ is a constant. The italicized words are the sources of fuzziness in these examples.

Fuzzy goals and fuzzy constraints can be defined precisely as fuzzy sets in the space of alternatives. A fuzzy decision, then, may be viewed as an intersection of the given goals and constraints. A maximizing decision is defined as a point in the space of alternatives at which the membership function of a fuzzy decision attains its maximum value.

The use of these concepts is illustrated by examples involving multistage decision processes in which the system under control is either deterministic or stochastic. By using dynamic programming, the determination of a maximizing decision is reduced to the solution of a system of functional equations. A reverse-flow technique is described for the solution of a functional equation arising in connection with a decision process in which the termination time is defined implicitly by the condition that the process stops when the system under control enters a specified set of states in its state space.

Much of the current thinking about competitive strategy focuses on ways that firms can create imperfectly competitive product markets in order to obtain greater than normal economic performance. However, the economic performance of firms does not depend simply on whether or not its strategies create such markets, but also on the cost of implementing those strategies. Clearly, if the cost of strategy implementation is greater than returns obtained from creating an imperfectly competitive product market, then firms will not obtain above normal economic performance from their strategizing efforts. To help analyze the cost of implementing strategies, we introduce the concept of a strategic factor market, i.e., a market where the resources necessary to implement a strategy are acquired. If strategic factor markets are perfect, then the cost of acquiring strategic resources will approximately equal the economic value of those resources once they are used to implement product market strategies. Even if such strategies create imperfectly competitive product markets, they will not generate above normal economic performance for a firm, for their full value would have been anticipated when the resources necessary for implementation were acquired. However, strategic factor markets will be imperfectly competitive when different firms have different expectations about the future value of a strategic resource. In these settings, firms may obtain above normal economic performance from acquiring strategic resources and implementing strategies. We show that other apparent strategic factor market imperfections, including when a firm already controls all the resources needed to implement a strategy, when a firm controls unique resources, when only a small number of firms attempt to implement a strategy, and when some firms have access to lower cost capital than others, and so on, are all special cases of differences in expectations held by firms about the future value of a strategic resource. Firms can attempt to develop better expectations about the future value of strategic resources by analyzing their competitive environments or by analyzing skills and capabilities they already control. Environmental analysis cannot be expected to improve the expectations of some firms better than others, and thus cannot be a source of more accurate expectations about the future value of a strategic resource. However, analyzing a firm’s skills and capabilities can be a source of more accurate expectations. Thus, from the point of view of firms seeking greater than normal economic performance, our analysis suggests that strategic choices should flow mainly from the analysis of its unique skills and capabilities, rather than from the analysis of its competitive environment.

The objective of the research was to discover the chief determinants of entrepreneurship, the process by which organizations renew themselves and their markets by pioneering, innovation, and risk taking. Some authors have argued that personality factors of the leader are what determine entrepreneurship, others have highlighted the role played by the structure of the organization, while a final group have pointed to the importance of strategy making. We believed that the manner and extent to which entrepreneurship would be influenced by all of these factors would in large measure depend upon the nature of the organization. Based upon the work of a number of authors we derived a crude typology of firms: Simple firms are small and their power is centralized at the top. Planning firms are bigger, their goal being smooth and efficient operation through the use of formal controls and plans. Organic firms strive to be adaptive to their environments, emphasizing expertise-based power and open communications. The predictiveness of the typology was established upon a sample of 52 firms using hypothesis-testing and analysis of variance techniques. We conjectured that in Simple firms entrepreneurship would be determined by the characteristics of the leader; in Planning firms it would be facilitated by explicit and well integrated product-market strategies, and in Organic firms it would be a function of environment and structure. These hypotheses were largely borne out by correlational and multiple regression analyses. Any programs which aim to stimulate entrepreneurship would benefit greatly from tailoring recommendations to the nature of the target firms.

A growth model for the timing of initial purchase of new products is developed and tested empirically against data for eleven consumer durables. The basic assumption of the model is that the timing of a consumer's initial purchase is related to the number of previous buyers. A behavioral rationale for the model is offered in terms of innovative and imitative behavior. The model yields good predictions of the sales peak and the timing of the peak when applied to historical data. A long-range forecast is developed for the sales of color television sets.

This paper gives an account of an experiment in the use of the so-called DELPHI method, which was devised in order to obtain the most reliable opinion consensus of a group of experts by subjecting them to a series of questionnaires in depth interspersed with controlled opinion feedback.

Innovation is defined as the development and implementation of new ideas by people who over time engage in transactions with others within an institutional order. This definition focuses on four basic factors (new ideas, people, transactions, and institutional context). An understanding of how these factors are related leads to four basic problems confronting most general managers: (1) a human problem of managing attention, (2) a process problem in managing new ideas into good currency, (3) a structural problem of managing part-whole relationships, and (4) a strategic problem of institutional leadership. This paper discusses these four basic problems and concludes by suggesting how they fit together into an overall framework to guide longitudinal study of the management of innovation.