A Holistic Framework for Trust in Online Transactions

This paper systematically reviews previous studies of trust from social, economic and technological perspectives and develops a holistic framework for trust, which can be used to analyse the establishment and maintenance of trust in online transactions, and identify the mechanisms that can be used to increase trust. Trust plays a crucial role in the formation of dependent relationships represented by online transactions, and a holistic treatment of trust is necessary because of the gap that exists between the developments in information systems and our understanding of their social and economic implications, and the impact on the perceived trust of the transacting parties. This review enables us to depict an online transaction through its attributes and context, and systematically map these to identified trust antecedents. The key components and processes of the framework are outlined, and three strands of empirical work are discussed to develop it further. The framework highlights the critical role of institutions in the establishment and maintenance of trust in online transactions, which informs the development of e‐commerce and e‐business platforms and the underpinning information systems, and facilitates the establishment of mechanisms to induce additional institutions to increase trust in online transactions.     

Best exact nonparametric confidence intervals for quantiles

Well-known nonparametric confidence intervals for quantiles are of the form (X _{i : n} , X _{j : n} ) with suitably chosen order statistics X _{i : n} and X _{j : n} , but typically their coverage levels differ from those prescribed. It appears that the coverage level of the confidence interval of the form (X _{i : n} , X _{j : n} ) with random indices I and J can be rendered equal, exactly to any predetermined level γ?∈?(0, 1). Best in the sense of minimum E(J???I), i.e., ‘the shortest’, two-sided confidence intervals are constructed. If no two-sided confidence interval exists for a given γ, the most accurate one-sided confidence intervals are constructed.     

On the E-optimality of complete block designs under a mixed interference model

In experiments in which the response to a treatment can be affected by other treatments, the interference model with neighbor effects is usually used. It is known that circular neighbor balanced designs (CNBDs) are universally optimal under such a model if the neighbor effects are fixed (Druilhet, 1999) or random (4 and 7). However, such designs cannot exist for every combination of design parameters. In the class of block designs with the same number of treatments as experimental units per block, a CNBD cannot exist if the number of blocks, b  , is equal to p(t−1)±1$p(t-1)\pm 1$, where p is a positive integer and t is the number of treatments. Filipiak et al. (2008) gave the structure of the left-neighboring matrix of E-optimal complete block designs with p  =1 under the model with fixed neighbor effects. The purpose of this paper is to generalize E-optimality results for designs with p∈N$p\in \mathbb{N}$ assuming random neighbor effects.     

Multiple Criteria Hierarchy Process with ELECTRE and PROMETHEE

Robust Ordinal Regression (ROR) supports Multiple Criteria Decision Process by considering all sets of parameters of an assumed preference model, that are compatible with preference information elicited by a Decision Maker (DM). As a result of ROR, one gets necessary and possible preference relations in the set of alternatives, which hold for all compatible sets of parameters, or for at least one compatible set of parameters, respectively. In this paper, we propose an extension of ELECTRE and PROMETHEE methods to the case of the hierarchy of criteria, which was never considered before. Then, we adapt ROR to the hierarchical versions of ELECTRE and PROMETHEE methods.     

RNA multiple structural alignment with longest common subsequences

In this paper, we present a new model for RNA multiple sequence structural alignment based on the longest common subsequence. We consider both the off-line and on-line cases. For the off-line case, i.e., when the longest common subsequence is given as a linear graph with n vertices, we first present a polynomial O(n ²) time algorithm to compute its maximum nested loop. We then consider a slightly different problem—the Maximum Loop Chain problem and present an algorithm which runs in O(n ⁵) time. For the on-line case, i.e., given m RNA sequences of lengths n, compute the longest common subsequence of them such that this subsequence either induces a maximum nested loop or the maximum number of matches, we present efficient algorithms using dynamic programming when m is small. This research is partially supported by EPSCOR Visiting Scholar's Program and MSU Short-term Professional Development Program.